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Obesity and the brain are linked since the brain can control the weight of the body through its neurotransmitters. The aim of the present study was to investigate the effect of high-fat diet (HFD)-induced obesity on brain functioning through the measurement of brain glutamate, dopamine, and serotonin metabolic pools. In the present study, two groups of rats served as subjects. Group 1 was fed a normal diet and named as the lean group. Group 2 was fed an HFD for 4 weeks and named as the obese group. Markers of oxidative stress (malondialdehyde, glutathione, glutathione-s-transferase, and vitamin C), inflammatory cytokines (interleukin [IL]-6 and IL-12), and leptin along with a lipid profile (cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein levels) were measured in the serum. Neurotransmitters dopamine, serotonin, and glutamate were measured in brain tissue. Fecal samples were collected for observing changes in gut flora. In brain tissue, significantly high levels of dopamine and glutamate as well as significantly low levels of serotonin were found in the obese group compared to those in the lean group (P > 0.001) and were discussed in relation to the biochemical profile in the serum. It was also noted that the HFD affected bacterial gut composition in comparison to the control group with gram-positive cocci dominance in the control group compared to obese. The results of the present study confirm that obesity is linked to inflammation, oxidative stress, dyslipidemic processes, and altered brain neurotransmitter levels that can cause obesity-related neuropsychiatric complications.

Microorganisms and associated conditions make up some of the world’s leading causes of death and have the potential to cause a significant societal effect during pandemic states. Despite such significance in the healthcare field, attention and funding directed towards microbiological research lags its cohorts in cancer and cardiovascular disease. In the current SAR-CoV-2 pandemic, the importance of practicing clinicians and providers to remain knowledgeable on antimicrobial therapy for their patients is higher than ever before. The scope of this review is to provide clinicians a semi-comprehensive, up-to-date understanding of the mechanisms of action among antimicrobial agents as well as key distinctions in clinical manifestations of pathogens.

Brevibacillus laterosporus (B. laterosporus) is an aerobic gram-positive bacillus that is rarely associated with human infection. A review of multiple online databases revealed no other cases of bacteremia in an adult involving this organism. Historically, this "canoe-shaped" microbe has been characterized as a pathogen in invertebrates, and information regarding human infection is scarce. We present a clinical vignette of what we believe to be the first reported case of B. laterosporus bacteremia in an adult human subject.

BackgroundEnterovirus (EV) and parechovirus type A3 (PeV-A3) cause infections ranging from asymptomatic to life-threatening. Host immune responses in children, particularly innate responses to PeV-A3, remain largely unknown. The aim of this study was to determine aspects of the cytokine/chemokine responses to EV and PeV-A3 in cerebrospinal fluid (CSF) and plasma obtained from children with systemic/central nervous system infection.MethodsA total of 74 salvaged CSF samples (27 with EV, 23 with PeV-A3, and 24 with neither EV nor PeV-A3) and 35 paired blood samples (10 with EV, 14 with PeV-A3, and 11 with neither) were studied. Concentrations of cytokines and chemokines were measured using a customized 21-plex MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel. Additionally, clinical characteristics data for all the patients were collected from electronic medical records to evaluate the potential association between the immune response and presentations.ResultsWe demonstrate that EV and PeV-A3 infections induce different cytokine/chemokine immune responses in children. EV induces more robust responses in CSF with significantly elevated levels of fractalkine, interferon (IFN)-α2, IFN-γ, interleukin (IL)-1Rα, IL-4, IL-8, and tumor necrosis factor α; PeV-A3 induces less robust or absent responses in CSF but robust responses in plasma, with significantly higher concentrations of IFN-α2, IL-15, IL-1Rα, interferon-γ-inducible protein–10, and monocyte chemoattractant protein–1.ConclusionsHigh cytokine/chemokine concentrations in the plasma of PeV-A3 patients compared with EV patients could explain higher/more prolonged fever in PeV-A3 patients, whereas relatively low cytokine/chemokine concentrations in PeV-A3 CSF might explain the absence of CSF pleocytosis.

Background Autism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body’s metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless medical illness is suspected. Promising biomarkers include those of mitochondrial dysfunction, oxidative stress, energy metabolism, and apoptosis. Methods and participants Sodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated in 13 participants with ASD and 24 age-matched healthy controls. Additionally, five ratios, which include Na+/K+, GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7:MLTN, were tested to measure their predictive values in discriminating between autistic individuals and controls. These markers, either in absolute values, as five ratios, or combined (9 markers + 5 ratios) were subjected to a principal component analysis and multidimensional scaling (MDS), and hierarchical clustering, which are helpful statistical tools in the field of biomarkers. Results Our data demonstrated that both PCA and MDS analysis were effective in separating autistic from control subjects completely. This was also confirmed through the use of hierarchical clustering, which showed complete separation of the autistic and control groups based on nine biomarkers, five biomarker ratios, or a combined profile. Excellent predictive value of the measured profile was obtained using the receiver operating characteristics analysis, which showed an area under the curve of 1. Conclusion The availability of an improved predictive profile, represented by nine biomarkers plus the five ratios, inter-related different etiological mechanisms in ASD and would be valuable in providing greater recognition of the altered biological pathways in ASD. Our predictive profile could be used for the diagnosis and intervention of ASD.

Background Autism is a neurodevelopmental disorder that displays significant heterogeneity. Comparison of subgroups within autism, and analyses of selected biomarkers as measure of the variation of the severity of autistic features such as cognitive dysfunction, social interaction impairment, and sensory abnormalities might help in understanding the pathophysiology of autism. Methods and Participants In this study, two sets of biomarkers were selected. The first included 7, while the second included 6 biomarkers. For set 1, data were collected from 35 autistic and 38 healthy control participants, while for set 2, data were collected from 29 out of the same 35 autistic and 16 additional healthy subjects. These markers were subjected to a principal components analysis using either covariance or correlation matrices. Moreover, libraries composed of participants categorized into units were constructed. The biomarkers used include, PE (phosphatidyl ethanolamine), PS (phosphatidyl serine), PC (phosphatidyl choline), MAP2K1 (Dual specificity mitogen-activated protein kinase kinase 1), IL-10 (interleukin-10), IL-12, NFκB (nuclear factor-κappa B); PGE2 (prostaglandin E2), PGE2-EP2, mPGES-1 (microsomal prostaglandin synthase E-1), cPLA2 (cytosolic phospholipase A2), 8-isoprostane, and COX-2 (cyclo-oxygenase-2). Results While none of the studied markers correlated with CARS and SRS as measure of cognitive and social impairments, six markers significantly correlated with sensory profiles of autistic patients. Multiple regression analysis identifies a combination of PGES, mPGES-1, and PE as best predictors of the degree of sensory profile impairment. Library identification resulted in 100% correct assignments of both autistic and control participants based on either set 1 or 2 biomarkers together with a satisfactory rate of assignments in case of sensory profile impairment using different sets of biomarkers. Conclusion The two selected sets of biomarkers were effective to separate autistic from healthy control subjects, demonstarting the possibility to accurately predict the severity of autism using the selected biomarkers. The effectiveness of the identified libraries lied in the fact that they were helpful in correctly assigning the study population as control or autistic patients and in classifying autistic patients with different degree of sensory profile impairment.