Mitochondrial Dysfunction in Multiple Sclerosis: A Systematic Review

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disorder of the central nervous system (CNS) and is characterized by a high degree of heterogeneity in progression and treatment response. Mitochondrial dysfunction is increasingly recognized as an important feature of MS pathology and may be relevant for clinical disease progression. This paper systematically reviews published evidence concerning the role of mitochondrial abnormalities with MS. Literature searched using the Web of Science, PMC/Medline via PubMed and Scopus databases up to May 2017 with no time and language limitation. After quality assessment, 9 articles were included in the study. All data extraction was conducted by two reviewers independently. Based on the results of the studies, it seems that mitochondrial DNA abnormality and mitochondrial dysfunction may be due to primary inflammation in MS or may be occurred itself before any inflammation, but definitely contributes to axonal degeneration and disease progression. Mitochondrial abnormality contributes to axonal degeneration in MS and disease progression

COVID-19 Infection and Seropositivity in Multiple Sclerosis Patients in Guilan in 2021

Background: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. COVID-19 has presented a significant challenge to the care providers of patients with MS. Objectives: The present study aimed to investigate the frequency of COVID-19 infection and its seropositivity in MS patients in Guilan, Iran, in 2021. Materials & Methods: In this analytical-cross-sectional study, all patients with relapsing-remitting MS registered in the Guilan MS Association with an expanded disability status scale of less than 5 who were referred for evaluation participated in the study. Information related to the clinical and serological symptoms of COVID-19 infection, changes in drug use, and the occurrence of new attacks were collected. Serological results of COVID-19 (IgG) among them were registered. Results: In total, 260 patients with MS (78.8% women, and 21.2% men) with a Mean±SD age of 38.7±9.9 years, and a Mean±SD duration of MS of 8.9±4.9 years were investigated. The most commonly used drugs were Dimethyl fumarate, Interferon, and Rituximab, respectively. Thirty-three patients (12.6%) had a clinical COVID-19 infection, of which 32 people had a mild and only one had a critical infection. Eight patients (1.3%) had positive COVID-19 IgG tests. No significant relationship was found between the COVID-19 infection with the type of medication, medication change, clinical attack of MS, and co-morbidities (P>0.05). Conclusion: A few patients had positive COVID-19 IgG tests and clinical COVID-19 infection. The vast majority had mild disease, and the clinical attack was not related to COVID-19 infection

Stroke Associated with SARS-CoV-2 Infection and its Pathogenesis: A Systematic Review

Introduction: In this systematic review, we will discuss the change of stroke incidence during the COVID-19 pandemic period and the proposed mechanisms of the relationship between SARS-CoV-2 and stroke. Methods: Web of Science, PMC/Medline, and Scopus databases were searched until July 2020 without time and language limitations. After quality assessment, 22 articles were included in this study. Results: Based on the results, it is impossible to conclude any definite relationship between the rising or decreasing stroke frequency or the shift in the ischemic and hemorrhagic ratio and SARS-CoV-2 infection. However, it appears that SARS-CoV-2 infection has some correlation with stroke. The supposed mechanisms for the SARS-CoV-2-related hemorrhagic stroke include 1) SARS-CoV-2-related vasculopathy with the endothelial damage of small vessels, 2) viral infection-induced platelet dysfunction or thrombocytopenia, and 3) activation of the proinflammatory cascade leading to coagulopathy. The helpful strategies are receiving therapeutic anticoagulation for high D-dimer or a known thrombus due to SARS-CoV-2 infection, as well as using extracorporeal membrane oxygenation (ECMO) in some patients. Furthermore, the possible mechanisms for the SARS-CoV-2-related ischemic stroke include 1) dysregulation of angiotensin-converting enzyme 2 (a key host cellular receptor for SARS-CoV-2)-related physiologic functions, 2) endothelial cell damages, 3) thrombo-inflammation, and 4) coagulopathy and coagulation abnormalities related to SARS-CoV-2 infection.  Conclusion: A better understanding of the SARS-CoV-2 pathogenesis and its relation to neurologic abnormalities such as stroke can help to design new therapeutic approaches

Collapsed cone superposition algorithm validation for chest wall tangential fields using virtual wedge filters

Background: Virtual wedge (VW) is used in radiotherapy to compensate for missing tissues and create a uniform dose distribution in tissues. According to TECDOC-1583 and technical reports series no. 430, evaluating the dose calculation accuracy is essential for the quality assurance of treatment planning systems (TPSs). In this study, the dose calculation accuracy of the collapsed cone superposition (CCS) algorithm in the postmastectomy radiotherapy of the chest wall for breast cancer was evaluated by comparing the calculated and measured dose in VW fields. Methods: Two tangential fields with the typical VW angles were planned using ISOgray TPS in a thorax phantom. The CCS algorithm was used for dose calculation at 6 and 15 MV photon beams. The obtained dose distributions from EBT3 film spaces and TPS were evaluated using the gamma index. Results: The measured and calculated dose values using VW in a heterogeneous medium with different beam energies were in a good agreement with each other (acceptance rate: 88.0%–93.4%). The calculated and measured data did not differ significantly with an increase/decrease in wedge angle. In addition, the results demonstrated that ISOgray overestimated and underestimated the dose of the soft tissue and lung in the planned volume, respectively. Conclusions: According to the results of gamma index analysis, the calculated dose distribution using VW model with the CCS algorithm in a heterogeneous environment was within acceptable limits

Infectious Agents and Stroke: A Systematic Review

Introduction: A major cause of injury and the second cause of death worldwide is stroke. Among several infectious agents considered as the risk factor of stroke, some pathogens demonstrated stronger robust associations with stroke. Proposing an accurate correlation between infectious microorganisms and stroke provides valuable information for early intervention and control of the infections.  Methods: In this study, we searched the literature using the Web of Science, PMC/Medline via PubMed, and Scopus databases up to July 2018 without time and language restrictions. After quality assessment, 16 articles were included in the study. The whole data extraction process was independently conducted by two reviewers.  Results: Based on the results of the studies, viruses, such as Hepatitis C virus (HCV), Hepatitis B virus (HBV), Human Immunodeficiency Virus (HIV), Herpes Simplex Virus Type-1, 2 (HSV-1, 2), Varicella-Zoster Virus (VZV or Chickenpox), and West Nile virus (WNV) seem to be common causes of ischemic stroke. Moreover, the association of other microbial categories, such as Streptococcus mutans (in bacteria), Toxocara spp. and Toxoplasma gondii (in parasites), and Rhizopus sp. (in fungi) with stroke was reported.  Conclusion: Considering the adverse role of the above-mentioned microorganisms, it is necessary to implement some preventive measures for stroke treatment

Analysis of Cerebrospinal Fluid in Diagnosis of Bacterial Meningitis; Using Nuclear Magnetic Resonance Spectroscopy: A Systematic Review

Background: Analysis of biofluids provides a unique window into the biochemical status of a living organism since the composition of a given biofluid will be modulated according to the level of function of the cells that are intimately concerned with its manufacture and secretion. One of the most successful approaches to biofluid analysis has been the application of NMR spectroscopy. Objectives: The aim of this study was the survey of the role of Nuclear Magnetic Resonance (NMR) Spectroscopy in differential diagnosis of septic bacterial meningitis. Methods: Using the search strategy from three databases (MEDLINE/PMC, Web of Science, Scopus), list of references of selected articles and gray literature, without time and language limitation, articles up to March 2017 were entered into this review. In this review, 219 articles were acquired at the primary search. Study selection and quality assessment processes were done based on Cochrane library guidelines. After assessing the quality and inclusion and exclusion criteria, 4 articles were selected and entered into the data synthesis. Results: The results of 4 studies demonstrated relative elevation of lactate value and extracellular acidosis in bacterial meningitis not in aseptic meningitis. Moreover in most of them, decreasing its level by treatment was evident. Conclusion: Metabolomic analysis with NMR spectroscopy of cerebrospinal fluid can become a powerful helping in differentiation of septic meningitis from aseptic meningitis

Effect of baking in different ovens on the quality and structural characteristics of saltine crackers

The aim of the study was to evaluate the physical and microstructural characteristics of crackers baked in four different industrial baking ovens (indirect radiation-cyclotherm, indirect convection, hybrid and industrial tunnel-ITO). Indirect convection and cyclotherm ovens provide the highest (5685.43 ± 51 W m−2) and the lowest (4860 ± 38.87 W m−2) amount of heat flux, respectively. Despite the amount of heat flux, indirect convection led to crackers with the highest moisture (7.86% vs. 4.82% in clyclotherm) and specific volume, but the lowest hardness. Cyclotherm resulted in crackers with lower specific volume, surface area, porosity, smooth and regular surface. Conversely, the hybrid and ITO ovens showed closer heat flux, leading to crackers with similar moisture content, texture parameters, specific volume, browning and inner porosity. Overall results show the potential of baking using different ovens for modifying the quality parameters of the crackers.The authors acknowledge the funding support of Spanish Ministry of Science and Innovation (Project RTI2018-095919-B-C21), and Generalitat Valenciana (Project Prometeo 2017/189).Peer reviewe

Diagnosis of Meningitis Caused by Pathogenic Microorganisms Using Magnetic Resonance Imaging: A Systematic Review

Introduction: Bacterial meningitis is an acute infectious inflammation of the protective membranes covering the brain. Its early diagnosis is vital because of its high morbidity and mortality. It is mostly diagnosed by a gold standard diagnostic tool i.e. Cerebrospinal Fluid (CSF) analysis. However, it is sometimes difficult and or impossible to do this procedure and an alternative diagnostic tool is needed. Contrast enhanced magnetic resonance imaging can detect the pus or other changes in subarachnoid space. But our optimal aim is to use an imaging method without using contrast to be useable and available in more specific condition. Methods: This study aimed to survey the role of non-contrast Magnetic Resonance Imaging (MRI) in the diagnosis of the bacterial meningitis. MEDLINE/PubMed Central, Web of Science and Scopus were searched without time period and language limitation until March 2017. We found 6410 papers in our initial search. After assessing the content of the papers based on Cochrane library guidelines and inclusion/exclusion criteria, 6 relevant studies were included in the systematic review. All of included studies were observational studies. Results: MRI studies demonstrated that Fluid Attenuation Inversion Recovery (FLAIR) and Diffusion-Weighted Image (DWI) MR imaging among all MRI modalities can detect some abnormalities compatible with bacterial meningitis. FLAIR and DWI-MR imaging are potentially useful to diagnose bacterial meningitis and can be used in emergent condition in which bacterial meningitis is highly suspicious and the other diagnostic tools are not available or feasible

A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients

Abstract Background The Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure expansion. In this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the classifier. Methods Both Expectation-Maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold cross-validation. Results The presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, respectively. Conclusions When considering the modification of the NB classifier, incorporating a latent component into the model offers numerous advantages, particularly within medical and health-related contexts. By doing so, the researchers can bypass the extensive search algorithm and structure learning required in the local learning and structure extension approach. The inclusion of latent class variables allows for the integration of all attributes during model construction. Consequently, the NB-BLCA model serves as a suitable alternative to conventional NB classifiers when the assumption of independence is violated, especially in domains pertaining to health and medicine