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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.

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.

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.

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.

Nowadays, great attention has been paid to the design and development of novel macromolecular constructions in drug delivery. Hyperbranched polymers (HBPs) are amongst various types of polymeric structures with exceptional physicochemical properties and biomedical applications relevant to their dendritic structure. Unlike perfect dendrimers, HBPs can be produced via one-step polymerization reactions. Moreover, they can be applied as building blocks in preparing dendronized, dendrigraft or dendritic nanostructures. Hyperbranched polyglycerols (HPGs) and their derivatives are widely regarded as promising biomaterials in a variety of diagnostic and therapeutic applications through solubilization, microencapsulation or conjugation with diagnostic agents, drugs, genetic materials, proteins, peptides, etc. HPGs have good chemical stability, inertness under biological conditions, water-solubility with low viscosity and multi-functionalization, which prompt their application as ideal or interesting nanocarriers or modifying agents. Herein, it was aimed to review recent developments and targeted approaches in utilizing HPGs for delivery of drugs, proteins and nucleic acids, as well as tissue engineering, diagnostic and theranostic applications.