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.

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.

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.

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.

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.

Incorporated glucosamine adversely affects the emulsifying properties of whey protein isolate polymerized by transglutaminase.

Glucosamine (GlcN) and microbial transglutaminase (Tgase) are used separately or together to improve the emulsifying properties of whey protein isolate (WPI). However, little is known about how the emulsifying properties change when GlcN residues are incorporated into WPI cross-linked by Tgase. We used Tgase as a biocatalyst to cross-link WPI in the presence of GlcN in a liquid system for 12 h at a moderate temperature (25°C). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analyses indicated that protein polymerization and GlcN conjugation occurred simultaneously, phenomena also supported by the loss of free amines (9.4-20.5%). Addition of 5 U Tgase/g protein improved the emulsifying properties of moderately cross-linked WPI polymers. The Tgase-treated WPI polymers had a larger particle size (∼2.6-fold) than native WPI, which may have reduced coalescence and flocculation in an oil-in-water emulsion system. However, the incorporation of GlcN residues into WPI, predominantly via enzymatic glycation, partly inhibited the cross-links between the WPI molecules catalyzed by Tgase, reducing the size of the WPI polymers 0.81- to 0.86-fold). Consequently, WPI+GlcN conjugates provided less stability to the emulsion. Moreover, high NaCl concentration (0.2 M) decreased the emulsifying properties of the WPI+GlcN conjugates by neutralizing negative electric charges in the glycoconjugates. However, the improved emulsifying properties of WPI cross-linked by Tgase may be useful in food processing at higher NaCl concentrations due to the formation of the thicker steric barrier at the oil-water interface.

Performance Analysis of Different Backoff Algorithms for WBAN-Based Emerging Sensor Networks.

The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision in wireless networks. The Binary Exponential Backoff (BEB) algorithm used in different standards does not obtain the optimum performance due to enormous Contention Window (CW) gaps induced from packet collisions. Therefore, The IEEE 802.15.6 CSMA/CA has developed the ABEB procedure to avoid the large CW gaps upon each collision. However, the ABEB algorithm may lead to a high collision rate (as the CW size is incremented on every alternative collision) and poor utilization of the channel due to the gap between the subsequent CW. To minimize the gap between subsequent CW sizes, we adopted the Prioritized Fibonacci Backoff (PFB) procedure. This procedure leads to a smooth and gradual increase in the CW size, after each collision, which eventually decreases the waiting time, and the contending node can access the channel promptly with little delay; while ABEB leads to irregular and fluctuated CW values, which eventually increase collision and waiting time before a re-transmission attempt. We analytically approach this problem by employing a Markov chain to design the PFB scheme for the CSMA/CA procedure of the IEEE 80.15.6 standard. The performance of the PFB algorithm is compared against the ABEB function of WBAN CSMA/CA. The results show that the PFB procedure adopted for IEEE 802.15.6 CSMA/CA outperforms the ABEB procedure.

Influence of biofilm surface layer protein A (BslA) on the gel structure of myofibril protein from chicken breast.

BACKGROUND: Different techniques have been applied to alter myofibril protein (MP) structure, which further promotes protein-protein interactions and influencing the MP gelling characteristics. Influence of BslA from natto food (protein concentration, 30 mg mL-1 ; at 0.001, 0.005, 0.01, 0.05 and 0.1 g kg-1 ) on the characteristics of MP gel of chicken breast was investigated. RESULTS: Results show that cooking loss significantly (P < 0.05) decreased with increased percentage of BslA. Hardness of MP gel did not significantly change at 0.01 g kg-1 BslA. Differential scanning calorimetry disclosed that MP was modified by the addition of BslA. Moreover, BslA produced a high value of storage modulus (G') and low value of phase angle (tan δ) during heating, especially at 0.01 g kg-1 . Analysis by sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis proved the formation of higher-molecular-weight polymers by developing non-disulfide covalent bonds between MP at 0.01 g kg-1 BslA. Surface hydrophobicity of the MP gel was decreased with increased percentage of BslA. Scanning electron microscopy confirmed the increasing number of uniform cavities of MP gel with the increased percentage of BslA. CONCLUSION: Addition of 0.01 g kg-1 BslA significantly improved the water holding capacity and rheological properties of MP by developing non-disulfide covalent bonds. © 2017 Society of Chemical Industry.

Susceptibility of whey protein isolate to oxidation and changes in physicochemical, structural, and digestibility characteristics.

Oxidation is an important factor for denaturing of whey protein isolate (WPI) during food processing. We studied the effects of chemical oxidation on physicochemical and structural changes along with in vitro digestibility of WPI in this work. Evaluation of physicochemical changes showed that carbonyl level and dityrosine content increased, whereas total and free thiol group levels decreased for oxidized WPI samples. For the structural changes, protein aggregation was measured by surface hydrophobicity, turbidity, and particle diameter, which was increased for oxidized WPI samples. The increase of the secondary structure ß-sheets and antiparallel ß-sheet also supported the aggregation of oxidized WPI. A direct quantitative relationship between physicochemical and structural changes and protein digestibility indicated that oxidation-related damage restricts the susceptibility of WPI to proteases. In conclusion, WPI had high susceptibility to oxidative stress, and both physicochemical and structural changes caused by severe oxidative stress could decrease the rate of in vitro digestibility of WPI.

Exploring essential oil-based bio-composites: molecular docking and in vitro analysis for oral bacterial biofilm inhibition.

Oral bacterial biofilms are the main reason for the progression of resistance to antimicrobial agents that may lead to severe conditions, including periodontitis and gingivitis. Essential oil-based nanocomposites can be a promising treatment option. We investigated cardamom, cinnamon, and clove essential oils for their potential in the treatment of oral bacterial infections using in vitro and computational tools. A detailed analysis of the drug-likeness and physicochemical properties of all constituents was performed. Molecular docking studies revealed that the binding free energy of a Carbopol 940 and eugenol complex was -2.0 kcal/mol, of a Carbopol 940-anisaldehyde complex was -1.9 kcal/mol, and a Carbapol 940-eugenol-anisaldehyde complex was -3.4 kcal/mol. Molecular docking was performed against transcriptional regulator genes 2XCT, 1JIJ, 2Q0P, 4M81, and 3QPI. Eugenol cinnamaldehyde and cineol presented strong interaction with targets. The essential oils were analyzed against Staphylococcus aureus and Staphylococcus epidermidis isolated from the oral cavity of diabetic patients. The cinnamon and clove essential oil combination presented significant minimum inhibitory concentrations (MICs) (0.0625/0.0312 mg/mL) against S. epidermidis and S. aureus (0.0156/0.0078 mg/mL). In the anti-quorum sensing activity, the cinnamon and clove oil combination presented moderate inhibition (8 mm) against Chromobacterium voilaceum with substantial violacein inhibition (58% ± 1.2%). Likewise, a significant biofilm inhibition was recorded in the case of S. aureus (82.1% ± 0.21%) and S. epidermidis (84.2% ± 1.3%) in combination. It was concluded that a clove and cinnamon essential oil-based formulation could be employed to prepare a stable nanocomposite, and Carbapol 940 could be used as a compatible biopolymer.

Bioactive plant waste components targeting oral bacterial pathogens as a promising strategy for biofilm eradication.

The significance of this study lies in its exploration of bioactive plant extracts as a promising avenue for combating oral bacterial pathogens, offering a novel strategy for biofilm eradication that could potentially revolutionize oral health treatments. Oral bacterial infections are common in diabetic patients; however, due to the development of resistance, treatment options are limited. Considering the excellent antimicrobial properties of phenolic compounds, we investigated them against isolated oral pathogens using in silico and in vitro models. We performed antibiogram studies and minimum inhibitory concentration (MIC), antibiofilm, and antiquorum sensing activities covering phenolic compounds. Bacterial strains were isolated from female diabetic patients and identified by using 16S rRNA sequencing as Pseudomonas aeruginosa, Bacillus chungangensis, Bacillus paramycoides, and Paenibacillus dendritiformis. Antibiogram studies confirmed that all strains were resistant to most tested antibiotics except imipenem and ciprofloxacin. Molecular docking analysis revealed the significant interaction of rutin, quercetin, gallic acid, and catechin with transcription regulator genes 1RO5, 4B2O, and 5OE3. All tested molecules followed drug-likeness rules except rutin. The MIC values of the tested compounds varied from 0.0625 to 0.5 mg/mL against clinical isolates. Significant antibiofilm activity was recorded in the case of catechin (73.5% ± 1.6% inhibition against B. paramycoides), cinnamic acid (80.9% ± 1.1% inhibition against P. aeruginosa), and vanillic acid and quercetin (65.5% ± 1.7% and 87.4% ± 1.4% inhibition, respectively, against B. chungangensis) at 0.25-0.125 mg/mL. None of the phenolic compounds presented antiquorum sensing activity. It was, therefore, concluded that polyphenolic compounds may have the potential to be used against oral bacterial biofilms, and further detailed mechanistic investigations should be performed.

The effects and underlying mechanism of interferon therapy on body weight and body composition.

Body weight changes in HCV patients on interferon therapy are well documented. However, the underlying mechanism involved in these changes is poorly understood and rarely reported. The main objectives of this review are to 1) discuss changes in body weight and other compartments of body composition, particularly, body fat, and 2) to discuss the underlying mechanism for these changes. The literature review suggests weight loss (12-29%) as a function of interferon therapy is common, affecting up to 90% of HCV patients. Whilst, loss in weight means proportionate loss in other body compartments (lean body mass and body fat, in particular) data on changes in segmented body composition are fragmentary. The possible mechanisms underlying weight loss or changes in other body composition have been reported and these include suppressed appetite due to induction of TNF by IFN, a decrease in serum leptin level, and importantly mitochondrial damage induced by the therapy. It is, therefore, suggested that close monitoring of chronic HCV patients receiving PEG-IFN and/or ribavirin for side effects of these drugs, particularly those related to weight loss, is vitally important from clinical point of view.

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.

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.

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.

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.

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.

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.