Mango peel extracts and mangiferin chromatographic Fourier-transform infrared correlation with antioxidant, antidiabetic, and advanced glycation end product inhibitory potentials using in silico modeling and in vitro assays.

Mangifera indica peels are a rich source of diverse flavonoids and xanthonoids; however, generally these are discarded. Computational studies revealed that mangiferin significantly interacts with amino acid residues of transcriptional regulators 1IK3, 3TOP, and 4f5S. The methanolic extract of Langra variety of mangoes contained the least phenol concentrations (22.6 ± 0.32 mg/gGAE [gallic acid equivalent]) compared to the chloroform (214.8 ± 0.12 mg/gGAE) and ethyl acetate fractions (195.6 ± 0.14 mg/gGAE). Similarly, the methanolic extract of Sindhri variety contained lower phenol concentrations (42.3 ± 0.13 mg/gRUE [relative utilization efficiency]) compared with the chloroform (85.6 ± 0.15 mg/gGAE) and ethyl acetate (76.1 ± 0.32 mg/gGAE) fractions. Langra extract exhibited significant α-glucosidase inhibition (IC50 0.06 mg/mL), whereas the ethyl acetate fraction was highly active (IC50 0.12 mg/mL) in Sindhri variety. Mangiferin exhibited significant inhibition (IC50 0.026 mg/mL). A moderate inhibition of 15-LOX was observed in all samples, whereas mangiferin was least active. In advanced glycation end product inhibition assay, the chloroform fraction of Langra variety exhibited significant inhibition in nonoxidative (IC50 64.4 µg/mL) and oxidative modes (IC50 54.7 µg/mL). It was concluded that both Langra and Sindhri peel extracts and fractions possess significant antidiabetic activities. The results suggest the potential use of peel waste in the management and complications of diabetes.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN.

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods.

Changes in the Quality of Myofibrillar Protein Gel Damaged by High Doses of Epigallocatechin-3-Gallate as Affected by the Addition of Amylopectin.

This work investigated the improvement of amylopectin addition on the quality of myofibrillar proteins (MP) gel damaged by high doses of epigallocatechin-3-gallate (EGCG, 80 µM/g protein). The results found that the addition of amylopectin partially alleviated the unfolding of MP induced by oxidation and EGCG, and enhanced the structural stability of MP. Amylopectin blocked the loss of the free amine group and thiol group, and increased the solubility of MP from 7.0% to 9.5%. The carbonyl analysis demonstrated that amylopectin addition did not weaken the antioxidative capacity of EGCG. It was worth noting that amylopectin significantly improved the gel properties of MP treated with a high dose of EGCG. The cooking loss was reduced from 51.2% to 35.5%, and the gel strength was reduced from 0.41 N to 0.29 N after adding high concentrations of amylopectin (A:E(8:1)). This was due to that amylopectin filled the network of MP gel after absorbing water and changed into a swelling state, and partially reduced interactions between EGCG and oxidized MP. This study indicated that amylopectin could be used to increase the polyphenol loads to provide a more lasting antioxidant effect for meat products and improve the deterioration of gel quality caused by oxidation and high doses of EGCG.

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms.

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.

Development and Evaluation of Essential Oil-Based Nanoemulgel Formulation for the Treatment of Oral Bacterial Infections.

Prevalence of oral infections in diabetic patients is a health challenge due to persistent hyperglycemia. However, despite great concerns, limited treatment options are available. We therefore aimed to develop nanoemulsion gel (NEG) for oral bacterial infections based on essential oils. Clove and cinnamon essential oils based nanoemulgel were prepared and characterized. Various physicochemical parameters of optimized formulation including viscosity (65311 mPa·S), spreadability (36 g·cm/s), and mucoadhesive strength 42.87 N/cm2) were within prescribed limits. The drug contents of the NEG were 94.38 ± 1.12% (cinnamaldehyde) and 92.96 ± 2.08% (clove oil). A significant concentration of clove (73.9%) and cinnamon essential oil (71.2 %) was released from a polymer matrix of the NEG till 24 h. The ex vivo goat buccal mucosa permeation profile revealed a significant (52.7-54.2%) permeation of major constituents which occurred after 24 h. When subjected to antimicrobial testing, significant inhibition was observed for several clinical strains, namely Staphylococcus aureus (19 mm), Staphylococcus epidermidis (19 mm), and Pseudomonas aeruginosa (4 mm), as well as against Bacillus chungangensis (2 mm), whereas no inhibition was detected for Bacillus paramycoides and Paenibacillus dendritiformis when NEG was utilized. Likewise promising antifungal (Candida albicans) and antiquorum sensing activities were observed. It was therefore concluded that cinnamon and clove oil-based NEG formulation presented significant antibacterial-, antifungal, and antiquorum sensing activities.

Effects of heating rates on the self-assembly behavior and gelling properties of beef myosin.

BACKGROUND: Myosin is the most important component of myofibrillar protein, with excellent gelling properties. To date, heating treatment remains the mainstream method for forming gel in meat products, and it has the most extensive application in the field of meat industry. However, at present, there are few reports on the effects of heating rates on myosin self-assembly and aggregation behavior during heating treatment. RESULTS: The present study aimed to investigate the effects of different heating rates (1, 2, 3 and 5 °C min-1 ) on the self-assembly behavior, physicochemical, structural and gelling properties of myosin. At the lowest heating rate of 1 °C min-1 , the myosin gel had a dense microstructure, the highest elastic modulus (G') and water holding capacity compared to higher heating rates (2, 3 and 5 °C min-1 ). At higher temperatures (40, 45 °C), the surface hydrophobicity, turbidity, particle size distribution and self-assembly behavior of myosin in pre-gelling solutions showed that myosin had sufficient time to denature, underwent full structure unfolding before aggregation at the heating rate of 1°C min-1 , and formed regular and homogeneous spherical aggregates. Therefore, the myosin gel also had a better three-dimensional network. CONCLUSION: The heating rates had an important effect on the quality of myosin gels, and had theoretical implications for improving the quality of meat gel products. © 2023 Society of Chemical Industry.

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization.

The extractive summarization approach involves selecting the source document's salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.

Fabrication and Optimization of Essential-Oil-Loaded Nanoemulsion Using Box-Behnken Design against Staphylococos aureus and Staphylococos epidermidis Isolated from Oral Cavity.

Oral bacterial infections are fairly common in patients with diabetes mellitus; however, due to limited treatment options, herbal medicines are considered an alternate solution. This study aimed to formulate a stable essential-oil-loaded nanoemulsion for the treatment of oral bacterial infections. Essential oils from edible sources including coriander, clove, cinnamon and cardamom were extracted by hydrodistillation. The response surface methodology was used to optimize the nanoemulsion formulation by applying the Box-Behnken design. The oil concentration, surfactant concentration and stirring speed were three independent factors, and particle size and polydispersity index were two responses. The particle size, polydispersity index and zeta potential of the optimized formulation were 130 mm, 0.222 and -22.9, respectively. The ATR-FTIR analysis revealed that there was no incompatibility between the active ingredients and the excipients. A significant release profile in active ingredients of nanoemulsion, i.e., 88.75% of the cinnamaldehyde and 89.33% of eugenol, was recorded after 24 h. In the ex vivo goat mucosal permeation study, 71.67% of the cinnamaldehyde permeated and that of the eugenol 70.75% from the nanoemulsion. The optimized formulation of the essential-oil-loaded nanoemulsion showed a 9 mm zone of inhibition against Staphylococcus aureus and Staphylococcus epidermidis, whereas in anti-quorum sensing analysis, the optimized nanoemulsion formulation showed an 18 mm zone of inhibition. It was concluded that formulated essential-oil-loaded nanoemulsion can be used against S. epidermidis and S. aureus infections in oral cavity.

Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review.

Medical imaging is the process of visual representation of different tissues and organs of the human body to monitor the normal and abnormal anatomy and physiology of the body. There are many medical imaging techniques used for this purpose such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and diagnostic sonography. These advanced medical imaging techniques have many applications in the diagnosis of myocardial diseases, cancer of different tissues, neurological disorders, congenital heart disease, abdominal illnesses, complex bone fractures, and other serious medical conditions. There are benefits as well as some risks to every imaging technique. There are some steps for minimizing the radiation exposure risks from imaging techniques. Advance medical imaging modalities such as PET/CT hybrid, three-dimensional ultrasound computed tomography (3D USCT), and simultaneous PET/MRI give high resolution, better reliability, and safety to diagnose, treat, and manage complex patient abnormalities. These techniques ensure the production of new accurate imaging tools with improving resolution, sensitivity, and specificity. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field will become a field of regular measurement of various complex diseases and will provide healthcare solutions.

An injectable antibacterial chitosan-based cryogel with high absorbency and rapid shape recovery for noncompressible hemorrhage and wound healing.

Great challenges remain in the effective control of irregular and incompressible deep wound bleeding and the promotion of wound healing after bacterial infection. In this study, cryogels were prepared using an ice template based on chitosan (CS), oxidized gallic acid (OGA) and hemin (HE), which are all green edible materials. The cryogels exhibit rapid blood-triggered shape recovery and a high swelling ratio. The cryogels with 5 mg mL-1 HE (Gel-CS/OGA@HE5) exert excellent photothermal effects. Additionally, the cryogels have excellent cytocompatibility and blood clotting abilities. In the mouse liver injury model and mouse tail amputation model, Gel-CS/OGA@HE5 presents better hemostasis properties than gauze and a gelatin sponge. Moreover, Gel-CS/OGA@HE5 displays excellent healing performance as a wound dressing. Overall, we provide a simple and effective strategy to prepare cryogels for controlling wound bleeding and promoting wound healing.

Antibacterial aerogels with nano­silver reduced in situ by carboxymethyl cellulose for fresh meat preservation.

Bacterial cellulose (BC) was used as a reinforcing agent, citric acid (CA) as a cross-linking agent, and CMC@AgNPs as antibacterial nanomaterials, in which CMC@AgNPs were reduced from AgNO3 in situ by carboxymethyl cellulose (CMC) as a reducing agent and stabilizer to fight microbial corruption. Its potential application in packaging fresh meat has been investigated. Results showed that the antibacterial CMC@AgNPs/BC/CA aerogels with excellent structural integrity and outstanding water absorption were developed by adding 0.3% BC and 0.25% CA. The CMC@AgNPs/BC/CA aerogel significantly reduced the color change and the total viable bacterial counts (TVC) in fresh meat after 7 days of refrigerated storage. The results indicated that CMC@AgNPs/BC/CA aerogels can effectively extend the shelf life of fresh meat, and can be used for meat packaging as a biologically active absorption pad.

Chitosan­sodium alginate-collagen/gelatin three-dimensional edible scaffolds for building a structured model for cell cultured meat.

Cell cultured meat (CCM) production is an innovative technology that does not depend on livestock farming practices to produce meat. The construction of structured CCM requires a three-dimensional (3D) scaffold to mimic the extracellular matrix to provide mechanical support for the cells. Furthermore, the 3D scaffolds should be edible and have good biocompatibility and tissue-like texture. Here, we demonstrated a 3D edible chitosan­sodium alginate-collagen/gelatin (CS-SA-Col/Gel) scaffold that can support the adhesion and proliferation of porcine skeletal muscle satellite cells, culminating in the construction of a structured CCM model. The 3D edible scaffolds were prepared by freeze-drying using electrostatic interactions between chitosan and sodium alginate. Initially, the physicochemical properties and structural characteristics of different scaffolds were explored, and the biocompatibility of the scaffolds was evaluated using the C2C12 cell model. The results showed that the 2-CS-SA-Col1-Gel scaffold provided stable mechanical support and abundant adhesion sites for the cells. Subsequently, we inoculated porcine skeletal muscle satellite cells on the 2-CS-SA-Col1-Gel scaffold and induced differentiation for a total of 14 days. Immunofluorescence staining results showed cytoskeleton formation, and Western blotting (WB) and qPCR results showed upregulation of skeletal proteins and myogenic genes. Ultimately, the structured CCM model has similar textural properties (chewiness, springiness and resilience) and appearance to those of fresh pork. In conclusion, the method of constructing 3D edible scaffolds to prepare structured CCM models exhibits the potential to produce cell cultured meat.

Dual role (promotion and inhibition) of transglutaminase in mediating myofibrillar protein gelation under malondialdehyde-induced oxidative stress.

This study investigated the effects of transglutaminase (TGase) on the properties of myofibrillar protein (MP) and its heat-induced gels under malondialdehyde (MDA)-induced oxidation. The physicochemical characteristics, protein aggregation and rheological properties of MP were assessed. The gelling behaviours of MP were analysed with measurements of gel strength, cooking loss, microstructure and secondary structure. Under varying degrees of MDA oxidation, the addition of TGase always led to changes in the tertiary structure, loss of free amine and thiol groups, crosslinking of the myosin heavy chain, and decreasing solubility. However, the effect of TGase on MP gel quality differed. At 6 mmol/L MDA, the addition of TGase reduced the quality of MP gels by increasing cooking loss. However, at 12 mmol/L MDA, TGase reduced both the cooking loss and gel strength.

Effect of MTGase on silver carp myofibrillar protein gelation behavior after peroxidation induced by peroxyl radicals.

The objective of this study was to explore the oxidative modification induced by AAPH (2,2'-azobis (2-amidinopropane) dihydrochloride) on the microbial transglutaminase (MTGase) cross-linking reaction and gelling properties of silver carp myofibril protein (SCMP). Compared to AAPH treatment, MTGase addition resulted further changes of protein properties as evidenced by the decreasing free amino and thiol group content, the decreased secondary and tertiary structure, and the increasing storage modulus (G') and gel strength (P < 0.05). The proper oxidation induced by AAPH (5 mM) promoted the glutamine-lysine and disulfide cross-linking due to MTGase (10 U/g). Therefore, the quality of the SCMP gel was improved with a good water-holding capacity (WHC), gel strength and G'. This results could lay a theoretical foundation for the development of silver carp surimi products with good quality. Chemical compounds: (2,2'-azobis(2-amidinopropane)dihydrochloride (PubChem CID:76344); O-Phthalaldehyde (PubChem CID:4807); 5,5'-Dithiobis(2-Nitrobenzoic Acid) (PubChem CID:6254); 8-Anilino-1-naphthalenesulfonic acid (PubChem CID:1369); Acrylamide (PubChem CID: 6579); ß-Mercaptoethanol (PubChem CID: 1567); Sodium dodecyl sulfate (PubChem CID:3423265).

The gelation properties of myofibrillar proteins prepared with malondialdehyde and (-)-epigallocatechin-3-gallate.

Impact of malondialdehyde (MDA) and (-)-Epigallocatechin-3-gallate (EGCG) on gelling properties of myofibrillar proteins (MPs) was investigated. Addition of 6 mM MDA enhanced molecular interactions of proteins, thus the strength and elastic modulus (G') of gel were improved. EGCG addition aggravated gel quality deterioration due to further modification of MPs induced by EGCG. Addition of 12 mM MDA jeopardized gel quality according to the increasing of strength and G', but the decreasing of water-holding capacity (WHC), and the collapse of microstructure. Nevertheless, EGCG reacted with MDA forming EGCG-MDA adducts, hence improved gel quality, which was supported by the decreasing of strength, but the increasing of WHC, and the repaired microstructure of gel at 12 mM MDA. Addition of 24 mM MDA severely jeopardized gel quality, which became even worse due to EGCG addition. This work is helpful to understand the impact of MDA and polyphenols on the gel-forming capacity of MPs.

Active biodegradable films based on the whole potato peel incorporated with bacterial cellulose and curcumin.

Development of biodegradable food packaging using biomass based materials derived from agricultural wastes has been a trend in recent years. The biopolymer films were prepared using 3% and 5% (w/w) potato peel (PP) powder. Bacterial cellulose (BC) (0, 5, 10 and 15% based on PP powder) was added as a reinforcement agent. The scanning electron microscopy (SEM) revealed that 10% BC had a promising compatibility with the PP matrix. X-ray diffraction (XRD) and thermogravimetric analysis (TGA) showed that the crystallinity and the thermal stability of films did not change with BC addition. Fourier transform infrared spectroscopy (FTIR) indicated the hydrogen bonding interactions between the PP matrix and BC in the films. BC addition significantly improved the tensile strength (TS), but reduced their water vapor permeability (WVP), oxygen permeability (OP) and moisture content (MC) of the PP films. Addition of curcumin further increased the antioxidant properties of the PP films. The PP films with 1-5% curcumin significantly reduced lipid oxidation in the fresh pork during storage with lower malondialdehyde (MDA) content.

The Aquilegia pubiflora (Himalayan columbine) mediated synthesis of nanoceria for diverse biomedical applications.

Herein, we report an eco-friendly, facile, one-pot, green synthesis of nanoceria for multiple biomedical applications. In the study, cerium oxide nanoparticles (CeO2-NPs) were synthesized using a simple aqueous extract of Aquilegia pubiflora as an effective reducing and capping agent. The biosynthesized nanoparticles were characterized via UV-vis spectroscopy, X-ray powder diffraction (XRD), high-performance liquid chromatography (HPLC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy. The NPs were highly stable, exhibited high purity, and had a spherical morphology and mean size of 28 nm. FTIR and HPLC studies confirmed the successful capping of bioactive compounds on the nanoparticles. The well-characterized NPs were evaluated for a number of biomedical applications, and their antimicrobial (antifungal, antibacterial, and antileishmanial), protein kinase inhibition, anticancer, antioxidant, anti-diabetic and biocompatibility properties were studied. Our results showed that the Aquilegia pubiflora mediated CeO2-NPs were highly active against fungal strains, compared to the tested bacterial strains, with Aspergillus niger resulting in the largest zone of inhibition (15.1 ± 0.27 mm). The particles also exhibited dose dependent leishmanicidal activity with significant LC50 values toward both the amastigote (114 µg mL-1) and promastigote (97 µg mL-1) forms of the parasite Leishmania tropica (KWH23). The NPs were found to be moderately active against the HepG2 cell line, showing 26.78% ± 1.16% inhibition at 200 µg mL-1. Last but not least, their highly biocompatible nature was observed with respect to freshly isolated human red blood cells (hRBCs), making the greenly synthesized CeO2-NPs a novel candidates for multidimensional medical applications.