Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.

METHODS

We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.

FINDINGS

Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.

INTERPRETATION

As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed.

FUNDING

Bill & Melinda Gates Foundation.

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.

Experimental evaluation of transition rate of sapphire crystal for thermal and fast neutrons using MNSR vertical neutron beam line

Using a perfect single crystal as a neutron filter allows us to have a thermal neutron beam with almost no background of fast neutrons. Single crystals of Al2O3 (sapphire) have proven to be effective filters for fast neutrons and are incorporated into neutron instruments. The present work would experimentally investigate c-axis neutron transmission rate by using different crystal thicknesses. In fact, the optimal thickness for sapphire filter is the one that maximizes the transmission of low energy neutrons and minimizes the transmission of fast neutrons, if there is no significant decrease in thermal neutron flux. In addition, neutron-filtering power of a-axis and c-axis sapphire crystals were compared with each other using different tests on a 2.5 cm slab of the sapphire crystals. The experimental tests were carried out by means of the available neutron flux top of the vertical neutron beam line of the Isfahan Miniature Neutron Source Reactor (MNSR) in two methods of foil activation and flux monitoring. In addition, the thermal and fast neutron dose rate reduction was discussed by using different thicknesses of the c-axis crystal.

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.

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.

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.

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.

Parents' Perception of Fissure Sealant Therapy in 6-12 Year Old Children: Evaluating a theory-driven intervention

BACKGROUND

Dental caries is considered a major global health issue and among the most challenging diseases worldwide. An effective way of preventing dental caries is the fissure sealant (FS) therapy.

OBJECTIVE

To determine the effectiveness of an educational intervention developed based on the health belief model (HBM) for parents' perception of FS therapy for their children.

METHODS

Quasi-experiment among 300 parents of 6-12 year-old children, 150 in the intervention group (IG) and 150 in the control (CG), in the south of Iran recruited via both clustering and convenience sampling. Data were collected using a validated questionnaire collecting demographic information, knowledge and data on HBM constructs and FS behaviour. Eight intervention sessions, 40-60 minutes long, were held for over month. The primary outcome was child's receipt of fissure sealants 3 months after the intervention.

RESULTS

The two groups had similar knowledge and the HBM constructs at baseline. After the intervention, the receipt of FS therapy was 65% and 12% in the IG and CG, respectively (p ⟨ 0.001, Chi Sq.). ANCOVA supported post-test differences between the intervention and control groups when accounting for baseline scores (p⟨0.05).

CONCLUSIONS

The educational HBM-based intervention improved parents' perceptions and their children's receipt of FS therapy. The intervention affected the HBM constructs. Barriers to healthy oral/dental behaviours may be reduced by interventions at multiple layers (beyond the individual level).

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.

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.

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.

Ceramic vs polymeric membrane implementation for potable water treatment

The continued technological developments and decreased purchase costs of ceramic membranes have seen increased recent interest in the technology as an alternative to the more widely used polymeric membranes. This paper assesses the relative technical, practical and economic merits of the two membrane materials in the context of potable water production from surface water sources. The work focuses on phenomena of direct technoeconomic significance, namely cleaning efficacy (manifested as permeability recovery), membrane integrity and incurred labour effort. Topics reviewed thus comprise: (a) practical comparison of the two technologies challenged with the same feedwater, (b) comparative technoeconomic analyses, (c) membrane integrity studies of polymeric membranes - incorporating aged samples extracted from operating installations, (d) sludging incidents, and (e) pilot and full-scale data. Available relevant data reveal: (a) bench-scale comparative tests do not indicate a consistent significant difference in the net permeability between the two membranes; (b) polymeric membranes are subject to a decline in both mechanical strength and permeability from the loss of the hydrophilic agent over a period of years from the action of hypochlorite used for cleaning; (c) the decreased mechanical strength with age of polymeric membranes increases the manual repair requirement and shortens membrane life, respectively impacting on labour and membrane replacement costs where the latter is also determined by the permeability; (d) the chemical and mechanical robustness of ceramic membranes permits more aggressive chemical cleaning, which then affects the chemicals consumption cost; and (e) anecdotal evidence suggests that polymeric membranes challenged with pre-coagulated surface waters may be subject to sludging, the agglomeration of solids in the membrane channels, which may also be age-related. Notwithstanding the above, data from published comparative technoeconomic studies indicate a linear relationship between the overall cost benefit and the membrane module cost ratio mitigated by the relative membrane life and operating flux.

Novel self-assembled nanogels of PEG-grafted poly HPMA with bis(α-cyclodextrin) containing disulfide linkage: synthesis, bio-disintegration, and in vivo biocompatibility

Synthesis of self-assembled nanogels of PEG-grafted poly HPMA with bis(α-cyclodextrin) containing disulfide linkage.

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.

COVID-19 DIAGNOSIS BY POINT OF CARE LUNG ULTRASOUND: A NOVEL DEEP LEARNING ARTIFICIAL INTELLIGENCE METHOD

BACKGROUND

METHODS AND RESULTS

CONCLUSION

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.

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.

Graphic abstract

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 β-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.

Early and late morbidity of local excision after chemoradiotherapy for rectal cancer

Background

Local excision (LE) after chemoradiotherapy is a new option in low rectal cancer, but morbidity has never been compared prospectively with total mesorectal excision (TME). Early and late morbidity were compared in patients treated either by LE or TME after neoadjuvant chemoradiotherapy for rectal cancer.

Method

This was a post-hoc analysis from a randomized trial. Patients with clinical T2/T3 low rectal cancer with good response to the chemoradiotherapy and having either LE, LE with eventual completion TME, or TME were considered. Early (1 month) and late (2 years) morbidities were compared between the three groups.

Results

There were no deaths following surgery in any of the three groups. Early surgical morbidity (20 per cent LE versus 36 per cent TME versus 43 per cent completion TME, P = 0.025) and late surgical morbidity (4 per cent versus 33 per cent versus 57 per cent, P < 0.001) were significantly lower in the LE group than in the TME or the completion TME group. of LE, was associated with the lowest rate of early (10 versus 18 versus 21 per cent, P = 0.217) and late medical morbidities (0 versus 7 versus 7 per cent, P = 0.154), although this did not represent a significant difference between the groups. The severity of overall morbidity was significantly lower at 2 years after LE compared with TME or completion TME (4 versus 28 versus 43 per cent grade 3–5, P < 0.001).

Conclusion

The rate of surgical complications after neoadjuvant chemoradiotherapy in the LE group was half that of TME group at 1 month and 10 times lower at 2 years. LE is a safe approach for organ preservation and should be considered as an alternative to watch-and-wait in complete clinical responders and to TME in subcomplete responders.

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.

Sphincter-saving surgery after neoadjuvant therapy for ultra-low rectal cancer where abdominoperineal resection was indicated: 10-year results of the GRECCAR 1 trial

This phase III trial included patients with ultra-low rectal adenocarcinoma that initially required abdominoperineal resection. The surgical decision was based on clinical tumour status after preoperative treatment. The overall sphincter-saving resection rate was 85 per cent, with 72 per cent rate of intersphincteric resection. Long-term results showed that changing the initial abdominoperineal resection indication into a sphincter-saving resection according to tumoral response is oncologically safe.

Saving the sphincter

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.

Design of Adaptive-Robust Controller for Multi-State Synchronization of Chaotic Systems with Unknown and Time-Varying Delays and Its Application in Secure Communication

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.

International variation in managing locally advanced or recurrent rectal cancer: prospective benchmark analysis

BACKGROUND

Tumour extension beyond the mesorectal plane (ymrT4) occurs in 5-10 per cent of patients with rectal cancer and 10 per cent of patients develop locally recurrent rectal cancer (LRRC) after primary surgery. There is global variation in healthcare delivery for these conditions.

METHODS

An international benchmark trial of the management of ymrT4 tumours and LRRC was undertaken in France and Australia between 2015 and 2017. Heterogeneity in management and operative decision-making were analysed by comparison of surgical resection rates, blinded intercountry reading of pelvic MRI, quality-of-life assessment and qualitative evaluations.

RESULTS

Among 154 patients (97 in France and 57 in Australia), 31·8 per cent had ymrT4 disease and 68·2 per cent LRRC. The surgical resection rates were 88 and 79 per cent in France and Australia respectively (P = 0·112). The concordance in operative planning was low (κ = 0·314); the rate of pelvic exenteration was lower in France than Australia both in clinical practice (36 of 78 versus 34 of 40; P < 0·001) and in theoretical conditions (10 of 25 versus 50 of 57; P = 0·002). The R0 resection rate was lower in France than Australia for LRRC (25 of 49 versus 18 of 21; P = 0·007) but not for ymrT4 tumours (21 of 26 versus 15 of 15; P = 0·139). Morbidity rates were similar. Patients who underwent non-exenterative procedures had higher scores on the mental functioning subscale at 12 months (P = 0·047), and a lower level of distress at 6 months (P = 0·049). Qualitative analysis highlighted five categories of psychosocial factors influencing treatment decisions: patient, strategy, specialist, organization and culture.

CONCLUSION

This international benchmark trial has highlighted the differences in worldwide treatment of locally advanced and LRRC. Standardized care should improve outcomes for these patients.

Biological mesh used to repair perineal hernias following abdominoperineal resection for anorectal cancer

PURPOSE

This study aimed to determine the outcome for patients who had undergone perineal hernia repair, via a perineal approach, using a biological mesh post-abdominoperineal excision (APE) for anorectal cancer.

METHOD

All consecutive patients having undergone perineal hernia repair involving an extracellular matrix of porcine small intestinal submucosa at our hospital between 2015 and 2018 were included. Follow-up clinical examinations and computed tomography scans were performed.

RESULTS

Six patients were treated surgically for symptomatic perineal hernia after a median of 31 months from APE. The median follow-up after hernia repair was 11 months (interquartile range [IQR], 6-35 months). Three patients (50%) developed a recurrent perineal hernia after a median interval of 6 months.

CONCLUSION

Perineal hernia repair using a biological mesh resulted in a high recurrence rate in patients who had undergone APE for anorectal cancer.

Surgery in reference centers improves survival of sarcoma patients: a nationwide study

BACKGROUND

NETSARC (netsarc.org) is a network of 26 sarcoma reference centers with specialized multidisciplinary tumor boards (MDTB) aiming to improve the outcome of sarcoma patients. Since 2010, presentation to an MDTB and expert pathological review are mandatory for sarcoma patients nationwide. In the present work, the impact of surgery in a reference center on the survival of sarcoma patients investigated using this national NETSARC registry.

PATIENTS AND METHODS

Patients' characteristics and follow-up are prospectively collected and data monitored. Descriptive, uni- and multivariate analysis of prognostic factors were conducted in the entire series (N = 35 784) and in the subgroup of incident patient population (N = 29 497).

RESULTS

Among the 35 784 patients, 155 different histological subtypes were reported. 4310 (11.6%) patients were metastatic at diagnosis. Previous cancer, previous radiotherapy, neurofibromatosis type 1 (NF1), and Li-Fraumeni syndrome were reported in 12.5%, 3.6%, 0.7%, and 0.1% of patients respectively. Among the 29 497 incident patients, 25 851 (87.6%) patients had surgical removal of the sarcoma, including 9949 (33.7%) operated in a NETSARC center. Location, grade, age, size, depth, histotypes, gender, NF1, and surgery outside a NETSARC center all correlated to overall survival (OS), local relapse free survival (LRFS), and event-free survival (EFS) in the incident patient population. NF1 history was one of the strongest adverse prognostic factors for LRFS, EFS, and OS. Presentation to an MDTB was associated with an improved LRFS and EFS, but was an adverse prognostic factor for OS if surgery was not carried out in a reference center. In multivariate analysis, surgery in a NETSARC center was positively correlated with LRFS, EFS, and OS [P < 0.001 for all, with a hazard ratio of 0.681 (95% CI 0.618-0.749) for OS].

CONCLUSION

This nationwide registry of sarcoma patients shows that surgical treatment in a reference center reduces the risk of relapse and death.

The Incidence of Cytomegalovirus Glycoprotein B Genotypes in Kidney Transplant Recipients in Iran

BACKGROUND

Cytomegalovirus (CMV) is the most common opportunistic viral infection in kidney transplant recipients. CMV classification is usually based on its glycoprotein B (gB) genotypes, which divides the virus into 4 strains (gB1-4).

OBJECTIVE

To determine the incidence of CMV genotypes in Iran and their relation to various clinical factors.

METHODS

We studied 80 renal transplant recipients admitted to our transplant referral center between 2014 and 2015. All of the studied patients were monitored every 1-2 weeks for CMV infection by immunofluorescence method. There were 34 CMV-infected patients whose sera were studied with sequencing technique to identify the 4 CMV genotypes. All patients were followed up to 6 months after transplantation.

RESULTS

gB1 was the most common genotype (35.3%); it was followed by gB3 and gB4 (each with 17.6 %), gB2, and mixed gB1,3 and gB1,2 (each with 14.7%). Age (p=0.037), time of infection after transplantation (p=0.011), and biopsy-proven rejection (p=0.012) were associated with CMV genotype. After adjusting for covariates, significant associations were found between genotype gB1 and family relationship (p=0.047) as well as HLA mismatch (p=0.014); genotype gB3 and family relationship (p=0.011); and genotype gB4 and age (p=0.019).

CONCLUSION

The most common CMV gB genotype in CMV-infected kidney transplant recipients in Iran was gB1. We recommend considering related therapeutic applications in the management of such patients.