Harnessing the Potential of Self-Assembled Peptide Hydrogels for Neural Regeneration and Tissue Engineering.

Spinal cord injury, traumatic brain injury, and neurosurgery procedures usually lead to neural tissue damage. Self-assembled peptide (SAP) hydrogels, a type of innovative hierarchical nanofiber-forming peptide sequences serving as hydrogelators, have emerged as a promising solution for repairing tissue defects and promoting neural tissue regeneration. SAPs possess numerous features, such as adaptable morphologies, biocompatibility, injectability, tunable mechanical stability, and mimicking of the native extracellular matrix. This review explores the capacity of neural cell regeneration and examines the critical aspects of SAPs in neuroregeneration, including their biochemical composition, topology, mechanical behavior, conductivity, and degradability. Additionally, it delves into the latest strategies involving SAPs for central or peripheral neural tissue engineering. Finally, the prospects of SAP hydrogel design and development in the realm of neuroregeneration are discussed.

Identification of Novel Mutations in the MMAA and MUT Genes among Methylmalonic Aciduria Families.

Background: Methylmalonic aciduria is a rare inherited metabolic disorder with autosomal recessive inheritance pattern. There are still MMA patients without known mutations in the responsible genes. This study aimed to identify mutations in Iranian MMA families using autozygosity mapping and NGS. Methods: Multiplex PCR was performed on DNAs isolated from 12 unrelated MMA patients and their family members using 19 STR markers flanking MUT, MMAA, and MMAB genes, followed by Sanger sequencing. WES was carried out in the patients with no mutation. Results: Haplotype analysis and Sanger sequencing revealed two novel, mutations, A252Vf*5 and G87R, within the MMAA and MUT genes, respectively. Three patients showed no mutations in either autozygosity mapping or NGS analysis. Conclusion: High-frequency mutations within exons 2 and 3 of MUT gene and exon 7 of MMAB gene are consistent with the global expected frequency of genetic variations among MMA patients.

Emotion recognition in EEG signals using deep learning methods: A review.

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identified through various sources, such as facial expressions, speech, behavior (gesture/position), or physiological signals. The use of physiological signals can enhance the objectivity and reliability of emotion detection. Compared with peripheral physiological signals, electroencephalogram (EEG) recordings are directly generated by the central nervous system and are closely related to human emotions. EEG signals have the great spatial resolution that facilitates the evaluation of brain functions, making them a popular modality in emotion recognition studies. Emotion recognition using EEG signals presents several challenges, including signal variability due to electrode positioning, individual differences in signal morphology, and lack of a universal standard for EEG signal processing. Moreover, identifying the appropriate features for emotion recognition from EEG data requires further research. Finally, there is a need to develop more robust artificial intelligence (AI) including conventional machine learning (ML) and deep learning (DL) methods to handle the complex and diverse EEG signals associated with emotional states. This paper examines the application of DL techniques in emotion recognition from EEG signals and provides a detailed discussion of relevant articles. The paper explores the significant challenges in emotion recognition using EEG signals, highlights the potential of DL techniques in addressing these challenges, and suggests the scope for future research in emotion recognition using DL techniques. The paper concludes with a summary of its findings.

Biocompatibility of graphene oxide nanosheets functionalized with various amino acids towards mesenchymal stem cells.

Graphene and its derivatives have gained popularity due to their numerous applications in various fields, such as biomedicine. Recent reports have revealed the severe toxic effects of these nanomaterials on cells and organs. In general, the chemical composition and surface chemistry of nanomaterials affect their biocompatibility. Therefore, the purpose of the present study was to evaluate the cytotoxicity and genotoxicity of graphene oxide (GO) synthesized by Hummer's method and functionalized by different amino acids such as lysine, methionine, aspartate, and tyrosine. The obtained nanosheets were identified by FT-IR, EDX, RAMAN, FE-SEM, and DLS techniques. In addition, trypan blue and Alamar blue methods were used to assess the cytotoxicity of mesenchymal stem cells extracted from human embryonic umbilical cord Wharton jelly (WJ-MSCs). The annexin V staining procedure was used to determine apoptotic and necrotic death. In addition, COMET and karyotyping techniques were used to assess the extent of DNA and chromosome damage. The results of the cytotoxicity assay showed that amino acid modifications significantly reduced the concentration-dependent cytotoxicity of GO to varying degrees. The GO modified with aspartic acid had the lowest cytotoxicity. There was no evidence of chromosomal damage in the karyotyping method, but in the comet assay, the samples modified with tyrosine and lysine showed the greatest DNA damage and rate of apoptosis. Overall, the aspartic acid-modified GO caused the least cellular and genetic damage to WJ-MSCs, implying its superior biomedical applications such as cell therapy and tissue engineering over GO.

Dendritic hybrid materials comprising polyhedral oligomeric silsesquioxane (POSS) and hyperbranched polyglycerol for effective antifungal drug delivery and therapy in systemic candidiasis.

Systemic Candida infections are routinely treated with amphotericin B (AMB), a highly effective antimycotic drug. However, due to severe toxicities linked to the parenteral administration of conventional micellar formulations (Fungizone®), its clinical utility is limited. Hyperbranched polyglycerols (HPGs) are multi-branched three-dimensional hydrophilic macromolecules that can be used to lessen the toxicity of AMB while also increasing its aqueous solubility. In the current research, to improve the safety and therapeutic efficacy of AMB, we developed new polyhedral oligomeric silsesquioxane - hyperbranched polyglycerol dendrimers with cholesterol termini (POSS-HPG@Chol) using azide-alkyne click reaction. Compared with Fungizone®, the as-synthesized POSS-HPG@Chol/AMB had lower minimum inhibitory and fungicidal concentrations against almost all studied Candida spp., as well as much less hemolysis and cytotoxicity. POSS-HPG@Chol/AMB revealed total protection of Balb/C mice from severe Candida infections in an experimental model of systemic candidiasis and can effectively reduce or eliminate AMB liver and kidney tissue injuries. Thanks to their safety, biocompatibility, and unique therapeutic properties, the developed POSS-polyglycerol dendrimers could be viable nanostructures for the delivery of poorly soluble drugs like AMB.

Chitosan-based emulgel and xerogel film containing Thymus pubescens essential oil as a potential wound dressing.

Controlling the wound exudates accompanied by microbial wound infections has still remained as one the most challenging clinical issues. Herein, a chitosan/gelatin/polyvinyl alcohol xerogel film containing Thymus pubescens essential oil is fabricated for antimicrobial wound dressing application. The chemical and physical characteristics of the devised formulation is characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, atomic force microscope, and tensile tests. Moreover, swelling capability, water vapour transmission rate, water contact angle, solubility, moisture content, and release properties are also studied. The antimicrobial and antibiofilm tests are performed using the broth microdilution and XTT assay, respectively. The produced formulation shows excellent antimicrobial efficacy against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and Candida species. It is also demonstrated that the obtained film can reduce (∼80 %) Candida albicans biofilm formation, and its biocompatibility is confirmed with MTT (∼100 %) and hemolysis tests. The antimicrobial activity can be correlated to the microbial membrane attraction for Candida albicans cells, illustrated by flow cytometry. This proposed film with appropriate mechanical strength, high swelling capacity in different pH values (∼200-700 %), controlled release property, and antimicrobial and antioxidant activities as well as biocompatibility can be used as a promising candidate for antimicrobial wound dressing applications.

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.

Nanotechnology-based cell-mediated delivery systems for cancer therapy and diagnosis.

The global prevalence of cancer is increasing, necessitating new additions to traditional treatments and diagnoses to address shortcomings such as ineffectiveness, complications, and high cost. In this context, nano and microparticulate carriers stand out due to their unique properties such as controlled release, higher bioavailability, and lower toxicity. Despite their popularity, they face several challenges including rapid liver uptake, low chemical stability in blood circulation, immunogenicity concerns, and acute adverse effects. Cell-mediated delivery systems are important topics to research because of their biocompatibility, biodegradability, prolonged delivery, high loading capacity, and targeted drug delivery capabilities. To date, a variety of cells including blood, immune, cancer, and stem cells, sperm, and bacteria have been combined with nanoparticles to develop efficient targeted cancer delivery or diagnosis systems. The review paper aimed to provide an overview of the potential applications of cell-based delivery systems in cancer therapy and diagnosis.

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Magnetic chitosan nanoparticles loaded with Amphotericin B: Synthesis, properties and potentiation of antifungal activity against common human pathogenic fungal strains.

Amphotericin B has long been regarded as the gold standard for treating invasive fungal infections despite its toxic potential. The main objective of this research was to develop a novel IONPs@CS-AmB formulation in a cost-effective manner in order to enhance AmB delivery performance, with lowering the drug's dose and adverse effects. The chitosan-coated iron oxide nanoparticles (IONPs@CS) were synthesized afterward, AmB-loaded IONPs@CS (IONPs@CS-AmB) prepared and characterized by AFM, FT-IR, SEM, EDX, and XRD. Biological activity of the synthesized NPs determined and the cytotoxicity of IONPs@CS-AmB evaluated using the MTT and in vitro hemolysis tests. The IONPs@CS-AmB was synthesized using the coprecipitation method with core-shell structure in size range of 27.70 to ∼70 nm. The FT-IR, XRD and EDX pattern confirmed the successful synthesis of IONPs @CS-AmB. The IONPs@CS-AmB exhibited significant antifungal activity and inhibited the metabolic activity of Candida albicans biofilms. The hemolysis and MTT assays showed that IONPs@CS-AmB is biocompatible with high cell viability when compared to plain AmB and fungizone. The IONPs@CS-AmB is more effective, less toxic and may be a suitable alternative to conventional drug delivery. IONPs@CS-AmB may be a viable candidate for use as a microbial-resistant coating on the surfaces of biomedical devices.

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.

Amphiphilic hyperbranched polyglycerol nanoarchitectures for Amphotericin B delivery in Candida infections.

Although Amphotericin B (AMB) is considered the most effective anti-mycotic agent for treating Candida infections, its clinical use is limited due to its high toxicity. To address this issue, we developed cholesterol-based dendritic micelles of hyperbranched polyglycerol (HPG), including cholesterol-cored HPG (Chol-HPG) and cholesterol end-capped HPG (HPG@Chol), for AMB delivery. The findings suggested that the presence of cholesterol moieties could control AMB loading and release properties. Dendritic micelles inhibited AMB hemolysis and cytotoxicity in HEK 293 and RAW 264.7 cell lines while increasing antifungal activity against C. albicans biofilm formation. Furthermore, significantly lower levels of renal and liver toxicity biomarkers compared to Fungizone® ensured AMB-incorporated dendritic micelle biosafety, which was confirmed by histopathological evaluations. Overall, the Chol-HPG and HPG@Chol dendritic micelles may be a viable alternative to commercially available AMB formulations as well as an effective delivery system for other poorly soluble antifungal agents.

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.

Nitric oxide releasing nanofibrous Fmoc-dipeptide hydrogels for amelioration of renal ischemia/reperfusion injury.

Renal ischemia/reperfusion (I/R) injury is responsible for significant mortality and morbidity during renal procedures. Nitric oxide (NO) deficiency is known to play a crucial role in renal I/R injury; however, low stability and severe toxicity of high concentrations of NO have limited its applications. Herein, we developed an in-situ forming Fmoc-dipheylalanine hydrogel releasing s-nitroso-n-acetylpenicillamine (FmocFF-SNAP) for renal I/R injury. Fmoc-FF hydrogel comprising of ß-sheet nanofibers was prepared through the pH-titration method. It was then characterized by electron microscopy, pyrene assay, and circular dichroism techniques. Mechanical properties of Fmoc-FF hydrogel (thixotropy and syringeability) were investigated by oscillatory rheology and texture analysis. To assess the therapeutic efficiency in the renal I/R injury model, expression of inducible nitric oxide synthase (iNOS) and endothelial nitric oxide synthase (eNOS) was measured in various samples (different concentrations of free SNAP and FmocFF-SNAP, unloaded Fmoc-FF, and sham control) by real-time RT-PCR, ROS production, serum biomarkers, and histopathological evaluations. According to the results, Fmoc-FF self-assembly in physiologic conditions led to the formation of an entangled nanofibrous and shear-thinning hydrogel. FmocFF-SNAP exhibited a sustained NO release over 7 days in a concentration-dependent manner. Importantly, intralesional injection of FmocFF-SNAP caused superior recovery of renal I/R injury when compared to free SNAP in terms of histopathological scores and renal function indices (e.g. serum creatinine, and blood urea nitrogen). Compared to the I/R control group, biomarkers of oxidative stress and iNOS expression were significantly reduced possibly due to the sustained release of NO. Interestingly, the eNOS expression showed a significant enhancement reflecting the regeneration of the injured endothelial tissue. Thus, the novel FmocFF-SNAP can be recommended for the alleviation of renal I/R injury.

Recent advances in design and applications of biomimetic self-assembled peptide hydrogels for hard tissue regeneration.

ABSTRACT: The development of natural biomaterials applied for hard tissue repair and regeneration is of great importance, especially in societies with a large elderly population. Self-assembled peptide hydrogels are a new generation of biomaterials that provide excellent biocompatibility, tunable mechanical stability, injectability, trigger capability, lack of immunogenic reactions, and the ability to load cells and active pharmaceutical agents for tissue regeneration. Peptide-based hydrogels are ideal templates for the deposition of hydroxyapatite crystals, which can mimic the extracellular matrix. Thus, peptide-based hydrogels enhance hard tissue repair and regeneration compared to conventional methods. This review presents three major self-assembled peptide hydrogels with potential application for bone and dental tissue regeneration, including ionic self-complementary peptides, amphiphilic (surfactant-like) peptides, and triple-helix (collagen-like) peptides. Special attention is given to the main bioactive peptides, the role and importance of self-assembled peptide hydrogels, and a brief overview on molecular simulation of self-assembled peptide hydrogels applied for bone and dental tissue engineering and regeneration.

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems.

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

Nanotechnology approaches for delivery and targeting of Amphotericin B in fungal and parasitic diseases.

Amphotericin B (AMB), with widespread antifungal and anti-parasitic activities and low cross-resistance with other drugs, has long been identified as a potent antimicrobial drug. However, its clinical toxicities, especially nephrotoxicity, have limited its use in clinical practice. Lately, nano-based systems have been the subject of serious research and becoming an effective strategy to improve toxicity and antimicrobial potency. Commercial AMB lipid formulations have been developed in order to improve the therapeutic index and nephrotoxicity, while limited use is mainly due to their high cost. The review aimed to highlight the updated information on nanotechnology-based approaches to the development of AMB delivery and targeting systems for treatment of fungal diseases and leishmaniasis, regarding therapeutic challenges and achievements of various delivery systems.