Electrospun Polymeric Nanofibers: Current Trends in Synthesis, Surface Modification, and Biomedical Applications.

Electrospun polymeric nanofibers are essential in various fields for various applications because of their unique properties. Their features are similar to extracellular matrices, which suggests them for applications in healthcare fields, such as antimicrobials, tissue engineering, drug delivery, wound healing, bone regeneration, and biosensors. This review focuses on the synthesis of electrospun polymeric nanofibers, their surface modification, and their biomedical applications. Nanofibers can be fabricated from both natural and synthetic polymers and their composites. Even though they mimic extracellular matrices, their surface features (physicochemical characteristics) are not always capable of fulfilling the purpose of the target application. Therefore, they need to be improved via surface modification techniques. Both needle-based and needleless electrospinning are thoroughly discussed. Various techniques and setups employed in each method are also reviewed. Furthermore, pre- and postspinning modification approaches for electrospun nanofibers, including instrument design and the modification features for targeted biomedical applications, are also extensively discussed. In this way, the remarkable potential of electrospun polymeric nanofibers can be highlighted to reveal future research directions in this dynamic field.

Fifteen Years' Experience of Thoracoscopic Sympathetic Chain Interruption for Palmar Hyperhidrosis in Children and Adolescents: Evaluation of Different Techniques.

Background: Thoracoscopic sympathetic chain interruption is a definitive and effective therapy for severe primary palmar hyperhidrosis (PPH). Well-known methods include sympathectomy, sympathotomy, and clipping, but the occurrence of compensatory sweating offsets these methods. This study aims to report our experience with thoracoscopic sympathetic chain interruption in a large group of patients of age <18 years with PPH, focusing on surgical outcomes, complication rates, and patient satisfaction. Patients and Methods: This retrospective study included patients who underwent thoracoscopic sympathectomy, sympathotomy, or clipping for severe PPH between April 2008 and March 2023 at the Pediatric Surgery Department, Al-Azhar University Hospitals. Demographic and clinical data, operative steps, postoperative outcomes, complications, and patient satisfaction were reviewed from the patients' medical records. Results: During the 15-year study period, 420 children with PPH underwent bilateral thoracoscopic sympathetic chain interruption by either sympathectomy, sympathotomy, or clipping, with a sex ratio of 60% being females. The mean ages were 12 ± 3.48, 13 ± 2.45, and 13 ± 2.45 years, respectively. Sympathectomy was performed in 190 patients (45.2%), sympathotomy in 170 patients (40.5%), and clipping in 60 patients (14.3%). All patients had completed follow-up, with mean periods of ∼43 ± 5 months, 45 ± 3 months, and 42 ± 6 months, respectively. Complete palmar dryness was achieved in 405 patients (overall 96.4%) (97.8% after sympathectomy, 97.05% after sympathotomy, and 90% after clipping), whereas 2.1%, 2.9%, and 10% of patients experienced symptom recurrence, respectively, denoting significant statistical differences. Overall, 94 patients (22.4%) experienced compensatory sweating. Eventually, 409 patients (97.4%) were satisfied with the outcome, whereas 11 patients (2.6%) reported dissatisfaction, yet no significant differences found. Conclusion: The presented three modalities of thoracoscopic sympathetic chain interruption for PPH in children and adolescents are safe and effective, with overall very high postoperative satisfaction, despite a relatively high rate of compensatory sweating in sympathectomy group. Other major complications in this age population were scanty.

Biomineralization of Polyelectrolyte-Functionalized Electrospun Fibers: Optimization and In Vitro Validation for Bone Applications.

In recent years, polyelectrolytes have been successfully used as an alternative to non-collagenous proteins to promote interfibrillar biomineralization, to reproduce the spatial intercalation of mineral phases among collagen fibrils, and to design bioinspired scaffolds for hard tissue regeneration. Herein, hybrid nanofibers were fabricated via electrospinning, by using a mixture of Poly ɛ-caprolactone (PCL) and cationic cellulose derivatives, i.e., cellulose-bearing imidazolium tosylate (CIMD). The obtained fibers were self-assembled with Sodium Alginate (SA) by polyelectrolyte interactions with CIMD onto the fiber surface and, then, treated with simulated body fluid (SBF) to promote the precipitation of calcium phosphate (CaP) deposits. FTIR analysis confirmed the presence of SA and CaP, while SEM equipped with EDX analysis mapped the calcium phosphate constituent elements, estimating an average Ca/P ratio of about 1.33-falling in the range of biological apatites. Moreover, in vitro studies have confirmed the good response of mesenchymal cells (hMSCs) on biomineralized samples, since day 3, with a significant improvement in the presence of SA, due to the interaction of SA with CaP deposits. More interestingly, after a decay of metabolic activity on day 7, a relevant increase in cell proliferation can be recognized, in agreement with the beginning of the differentiation phase, confirmed by ALP results. Antibacterial tests performed by using different bacteria populations confirmed that nanofibers with an SA-CIMD complex show an optimal inhibitory response against S. mutans, S. aureus, and E. coli, with no significant decay due to the effect of CaP, in comparison with non-biomineralized controls. All these data suggest a promising use of these biomineralized fibers as bioinspired membranes with efficient antimicrobial and osteoconductive cues suitable to support bone healing/regeneration.

The role of neuromodulation in the management of drug-resistant epilepsy.

Drug-resistant epilepsy (DRE) poses significant challenges in terms of effective management and seizure control. Neuromodulation techniques have emerged as promising solutions for individuals who are unresponsive to pharmacological treatments, especially for those who are not good surgical candidates for surgical resection or laser interstitial therapy (LiTT). Currently, there are three neuromodulation techniques that are FDA-approved for the management of DRE. These include vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). Device selection, optimal time, and DBS and RNS target selection can also be challenging. In general, the number and localizability of the epileptic foci, alongside the comorbidities manifested by the patients, substantially influence the selection process. In the past, the general axiom was that DBS and VNS can be used for generalized and localized focal seizures, while RNS is typically reserved for patients with one or two highly localized epileptic foci, especially if they are in eloquent areas of the brain. Nowadays, with the advance in our understanding of thalamic involvement in DRE, RNS is also very effective for general non-focal epilepsy. In this review, we will discuss the underlying mechanisms of action, patient selection criteria, and the evidence supporting the use of each technique. Additionally, we explore emerging technologies and novel approaches in neuromodulation, such as closed-loop systems. Moreover, we examine the challenges and limitations associated with neuromodulation therapies, including adverse effects, complications, and the need for further long-term studies. This comprehensive review aims to provide valuable insights on present and future use of neuromodulation.

Health-related quality of life and cognitive function in children with Crigler-Najjar syndrome type 1.

BACKGROUND: The aim of the study was to assess the health-related quality of life (HRQOL) and cognitive function in patients with Crigler-Najjar syndrome (CNS) type I and its impact on their lives. METHODS: Twenty-one patients diagnosed with CNS type I aged 1 month to 18 years in the Paediatric Hepatology Unit of Cairo University Children's Hospital were enrolled in this cross-sectional observational study. The patients' health-related quality of life (HRQOL) was assessed using the World Health Organization Quality Of Life BREF questionnaire (WHOQOL-BREF) and the Short Form 36 Health Survey Questionnaire (SF-36). Cognitive function was assessed using the Stanford-Binet Intelligence Scale: Fifth Edition (SB5). RESULTS: All patients had a history of admission to a neonatal intensive care unit, 17 were managed by phototherapy only and 5 also underwent exchange transfusion. According to the WHOQOL questionnaire, 11 cases (52.4%) had a low QOL score, and 7 of 13 patients had an average score for their total IQ test. Cases with poor compliance to phototherapy had statistically significantly lower QOL scores (p=0.001), while, according to the SF36 survey, cases who received exchange transfusion had statistically significantly higher cognitive function (p=0.03). There was a positive correlation between the neurological effect as a complication of the disease and poor physical QOL. CONCLUSION: Paediatric patients with CNS have significantly lower HRQOL, especially physically, psychologically and environmentally. It is recommended that assessment of HRQOL should be a routine part of follow-up in CNS patients. Patients whose HRQOL is affected receive regular psychiatric counselling, social support and rehabilitation.Abbreviations: CNS: Crigler-Najjar syndrome; HRQOL: health-related quality of life; IQ: intelligence quotient; NICU: neonatal intensive care unit; QOL: quality of life; SB5: Stanford-Binet intelligence scale: 5th edition; SF-36: Short Form 36 Health Survey Questionnaire; UDGT: uridine diphosphate glucuronosyl transferase; UGT1A1: uridine 5'-diphosphate glucuronosyltransferase; WHOQOL-BREF: World Health Organization Quality of Life Brief Version.

Fabrication of cellulose nanocrystals/carboxymethyl cellulose/zeolite membranes for methylene blue dye removal: understanding factors, adsorption kinetics, and thermodynamic isotherms.

This study introduces environmentally-friendly nanocellulose-based membranes for AZO dye (methylene blue, MB) removal from wastewater. These membranes, made of cellulose nanocrystals (CNCs), carboxymethyl cellulose (CMC), zeolite, and citric acid, aim to offer eco-friendly water treatment solutions. CNCs, obtained from sugarcane bagasse, act as the foundational material for the membranes. The study aims to investigate both the composition of the membranes (CMC/CNC/zeolite/citric acid) and the critical adsorption factors (initial MB concentration, contact time, temperature, and pH) that impact the removal of the dye. After systematic experimentation, the optimal membrane composition is identified as 60% CNC, 15% CMC, 20% zeolites, and 5% citric acid. This composition achieved a 79.9% dye removal efficiency and a 38.3 mg/g adsorption capacity at pH 7. The optimized membrane exhibited enhanced MB dye removal under specific conditions, including a 50 mg adsorbent mass, 50 ppm dye concentration, 50 mL solution volume, 120-min contact time, and a temperature of 25°C. Increasing pH from neutral to alkaline enhances MB dye removal efficiency from 79.9% to 94.5%, with the adsorption capacity rising from 38.3 mg/g to 76.5 mg/g. The study extended to study the MB adsorption mechanisms, revealing the chemisorption of MB dye with pseudo-second-order kinetics. Chemical thermodynamic experiments determine the Freundlich isotherm as the apt model for MB dye adsorption on the membrane surface. In conclusion, this study successfully develops nanocellulose-based membranes for efficient AZO dye removal, contributing to sustainable water treatment technologies and environmental preservation efforts.

Development of collagen-chitosan dressing gel functionalized with propolis-zinc oxide nanoarchitectonics to accelerate wound healing.

Using an in situ sol-gel technique, new nanoarchitectonics of propolis loaded zinc oxide nanoarchitectonics (PP/ZnO-NPs) were developed in order to improve the in vivo outcomes of collagen-chitosan gel in wounded rats. The obtained nanoarchitectonics were fully characterized. The XRD results indicate the presence of a Zincite phase for ZnO-NPs and Zincite accompanied by a minor amount of zinc hydroxide for PP/ZnO-NPs samples. While the TEM findings illustrate the transfer of the ZnO-NPs from agglomerated spheres with an average particle size of 230 ± 29 nm to needle-like NPs of 323 ± 173 nm length (PP1/ZnO-NPs) and to a sheet-like NPs of 500 ± 173 nm diameter (PP2/ZnO-NPs). In addition, the incorporation of PP results in an increase in the surface negativity of ZnO-NPs to -31.4 ± 6.4 mV for PP2/ZnO-NPs. The antimicrobial activities of the nanocomposite gel loaded with 10%PP1/ZnO-NPs (G6) revealed the highest inhibition zone against E. coli (26 ± 2.31 mm). Remarkably, the in vivo outcomes showed that the nanocomposite gel (G6) has exceptional collagen deposition, quick wound closure rates, and re-epithelization. The outcomes demonstrate the nanocomposite gel encouraging biological properties for the treatment of damaged and infected wounds.

Surgical and Functional Outcome of Laparoscopic Vaginal Suspension Combined with Suture Rectopexy for Management of Pelvic Organ Prolapse

Abstract Purpose Laparoscopic techniques to treat pelvic organ prolapse are gaining popularity around the globe due to their low recurrence rates and better functional results compared to perineal techniques. However, the optimum surgical procedures are not yet determined. In the current research, we suggest a novel surgical approach, laparoscopic vaginal suspension with suture rectopexy, to treat multiorgan pelvic prolapse. Methods This prospective cohort trial was conducted from March 2018 to March 2022 and comprised 35 females with multiorgan pelvic organ prolapse with obstructed defecation symptoms. A residual rectal prolapse was still present despite the manual reduction of uterine prolapse. Patients' conditions before and after the operation were monitored regarding the obstructed defecation score, sexual function, need for laxatives, anorectal manometry pressures, anorectal sensations, and recurrence. The mean follow-up duration was one year. Results Modified Longo score for obstructed defecation significantly decreased at six and twelve months after surgery. Additionally, a significant reduction was reported in the number of patients who needed laxatives at six and twelve months after surgery. Anorectal manometry pre- and post-surgery showed a significant elevation in the mean squeeze pressure and a decline in all rectal sensations. All parameters of the female sexual function scoring system increased postoperatively. No recurrence was reported during follow-up. Conclusion For multiorgan pelvic prolapse, laparoscopic vaginal suspension combined with suture rectopexy has excellent functional outcomes, minimal morbidity, and low cost.

A novel automated Parkinson's disease identification approach using deep learning and EEG.

The neurological ailment known as Parkinson's disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson's disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study's suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.

Applications of propolis-based materials in wound healing.

Due to its excellent antiseptic efficacy and antimicrobial properties, propolis has shown attractive advantages in wound dressings. However, an inclusive review of the propolis-based materials as a wound dressing is still lacking. The current short review summarizes the skin wound healing process, relates evaluation parameters, and then reviews the refined propolis-based materials dressings such as antimicrobial property, adhesion and hemostasis, anti-inflammatory and substance delivery. The approaches implemented to achieve these functions are classified and discussed. Furthermore, applications of propolis wound dressing for treating different types of wounds such as heal wounds, burns, and ulcers are presented. The future directions of propolis-based wound dressings for wound healing are further proposed. This review showed that propolis-based materials might be a promising new dressing for wound occlusion and tissue repairing.

Sodium Alginate- and Cationic Cellulose-Functionalized Polycaprolactone Nanofibers for In Vitro and Antibacterial Applications.

The use of polyelectrolytes is emerging as a fascinating strategy for the functionalization of biomedical membranes, due to their ability to enhance biological responses using the interaction effect of charged groups on multiple interface properties. Herein, two different polyelectrolytes were used to improve the antibacterial properties of polycaprolactone (PCL) nanofibers fabricated via electrospinning. First, a new cationic cellulose derivative, cellulose-bearing imidazolium tosylate (CIMD), was prepared via the nucleophilic substitution of the tosyl group using 1-methylimidazole, as confirmed by NMR analyses, and loaded into the PCL nanofibers. Secondly, sodium alginate (SA) was used to uniformly coat the fibers' surface via self-assembly, as remarked through SEM-EDX analyses. Polyelectrolyte interactions between the CIMD and the SA, initially detected using a FTIR analysis, were confirmed via Z potential measurements: the formation of a CMID/SA complex promoted a substantial charge neutralization of the fibers' surfaces with effects on the physical properties of the membrane in terms of water adsorption and in vitro degradation. Moreover, the presence of SA contributed to the in vitro response of human mesenchymal stem cells (hMSCs), as confirmed by a significant increase in the cells' viability after 7 days in the case of the PCL/CMID/SA complex with respect to the PCL and PCL/CMID membranes. Contrariwise, SA did not nullify the antibacterial effect of CMID, as confirmed by the comparable resistance exhibited by S. mutans, S. aureus, and E. coli to the PCL/CIMD and PCL/CIMD/SA membranes. All the reported results corroborate the idea that the CIMD/SA functionalization of PCL nanofibers has a great potential for the fabrication of efficient antimicrobial membranes for wound healing.

Azygos vein preservation revisited: impact on early outcomes after repair of esophageal atresia/tracheoesophageal fistula in newborns.

Since the first successful repair of esophageal atresia/tracheoesophageal fistula (EA-TEF) was performed approximately 8 decades ago, surgeons have made considerable technical advances in solving intraoperative surgical challenges and reducing postoperative complications. According to some surgeons, preserving the Azygos vein makes this modification attractive. This study aimed to evaluate the benefits of preserving the Azygos vein during surgery for esophageal atresia with tracheoesophageal fistula and to highlight its advantages in reducing anastomotic leak, stricture, and other postoperative outcomes. This prospective comparative series was conducted between April 2020 and April 2023. The study included all newborns with EA-TEF eligible for primary repair. Patients were randomized to either Group A or B. Group A underwent Azygos vein preservation, whereas the remaining patients (Group B) underwent Azygos vein disconnection. Sixty-four patients were included in this study. Thirty-two patients (Group A) underwent Azygos vein preservation during EA-TEF repair, and the remaining thirty-two patients (Group B) underwent Azygos vein ligation and disconnection. Both groups were comparable in terms of demographics, clinical data, and operative findings (P > 0.05). Pneumonitis occurred in 4 patients in Group A and 16 patients in Group B. Anastomotic leaks occurred in two (6.2%) patients in Group A and six (18.7%) patients in Group B. There were two deaths in Group A and six deaths in Group B, with a significant difference between the two groups (P = 0.0485). Preserving the Azygos vein during esophageal atresia repair reduces the occurrence of postoperative pneumonia, leakage, and stenosis, and decreases postoperative mortality. Therefore, we declare that this modification is a significant and valuable addition to the current surgical procedures.

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment.

In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.

Novel cellulose derivative containing aminophenylacetic acid as sustainable adsorbent for removal of cationic and anionic dyes.

The synthesis and characterization of a novel cellulose derivative as a potential sustainable adsorbent for cationic and anionic dyes are described through processing in ionic liquids. Cellulose was solubilized in ionic liquid with tosyl chloride to form tosyl cellulose which reacted with 4-aminophenylacetic acid through nucleophilic substitution mechanism. The new cellulose derivative was characterized and explored as an effective adsorbent for methylene blue (MB) and methyl orange (MO) removal, and the adsorption behaviors were investigated with various models. The adsorption behavior of the cellulose derivative followed Langmuir and pseudo-second-order models, and the maximum adsorption efficiency recorded 135 and 106 mg/g for MB and MO, respectively. There is possibility that the enhanced adsorption capacity of the cellulose derivative is due to the carboxylic and amino functional groups that provide sufficient active sites to enhance dye molecule affinity. The adsorption results demonstrate that the cellulose derivative containing aminophenylacetic acid was efficient adsorbent for removals of MB and MO.

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.

Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.

Egyptian propolis extract for functionalization of cellulose nanofiber/poly(vinyl alcohol) porous hydrogel along with characterization and biological applications.

Bee propolis is one of the most common natural extracts and has gained significant interest in biomedicine due to its high content of phenolic acids and flavonoids, which are responsible for the antioxidant activity of natural products. The present study report that the propolis extract (PE) was produced by ethanol in the surrounding environment. The obtained PE was added at different concentrations to cellulose nanofiber (CNF)/poly(vinyl alcohol) (PVA), and subjected to freezing thawing and freeze drying methods to develop porous bioactive matrices. Scanning electron microscope (SEM) observations displayed that the prepared samples had an interconnected porous structure with pore sizes in the range of 10-100 µm. The high performance liquid chromatography (HPLC) results of PE showed around 18 polyphenol compounds, with the highest amounts of hesperetin (183.7 µg/mL), chlorogenic acid (96.9 µg/mL) and caffeic acid (90.2 µg/mL). The antibacterial activity results indicated that both PE and PE-functionalized hydrogels exhibited a potential antimicrobial effects against Escherichia coli, Salmonella typhimurium, Streptococcus mutans, and Candida albicans. The in vitro test cell culture experiments indicated that the cells on the PE-functionalized hydrogels had the greatest viability, adhesion, and spreading of cells. Altogether, these data highlight the interesting effect of propolis bio-functionalization to enhance the biological features of CNF/PVA hydrogel as a functional matrix for biomedical applications.

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer.

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

Synthesis and adsorption performance of functionalized chitosan and carboxyethylsilanetriol hybrids.

A novel adsorbent from cationic chitosan derivative and anionic silica precursor was fabricated to remove methylene blue (MB). The hybrid material was prepared from N-guanidinium chitosan acetate (GChi) and carboxyethylsilanetriol sodium salt by a simple ionic interaction followed by sol-gel approach. Multiple characterization methods were used to analyze the morphology and the structure of the well-prepared functionalized material. Batch experiments were conducted to optimize the various operational parameters. The Langmuir isotherm was used to fit the data, and it predicted monolayer adsorption with a maximum capacity of 334 mg g-1. A pseudo-second-order equation fit the adsorption process well. Chitosan/silica hybrids containing carboxylic groups are efficient and cost-effective adsorbents for cationic dyes adsorption from aqueous solutions.

Bacterial Cellulose/Cellulose Imidazolium Bio-Hybrid Membranes for In Vitro and Antimicrobial Applications.

In biomedical applications, bacterial cellulose (BC) is widely used because of its cytocompatibility, high mechanical properties, and ultrafine nanofibrillar structure. However, biomedical use of neat BC is often limited due to its lack of antimicrobial properties. In the current article, we proposed a novel technique for preparing cationic BC hydrogel through in situ incorporation of cationic water-soluble cellulose derivative, cellulose bearing imidazolium tosylate function group (Cell-IMD), in the media used for BC preparation. Different concentrations of cationic cellulose derivative (2, 4, and 6%) were embedded into a highly inter-twined BC nanofibrillar network through the in situ biosynthesis until forming cationic cellulose gels. Cationic functionalization was deeply examined by the Fourier transform infrared (FT-IR), NMR spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) methods. In vitro studies with L929 cells confirmed a good cytocompatibility of BC/cationic cellulose derivatives, and a significant increase in cell proliferation after 7 days, in the case of BC/Cell-IMD3 groups. Finally, antimicrobial assessment against Staphylococcus aureus, Streptococcus mutans, and Candida albicans was assessed, recording a good sensitivity in the case of the higher concentration of the cationic cellulose derivative. All the results suggest a promising use of cationic hybrid materials for biomedical and bio-sustainable applications (i.e., food packaging).