A systematic review of the beneficial effects of prebiotics, probiotics, and synbiotics on ADHD.

BACKGROUND: Children with attention deficit hyperactivity disorder (ADHD) may benefit from probiotics and prebiotics, but the effects are unclear. To determine whether probiotics and prebiotics affect children with ADHD, a systematic review was conducted. METHODS: The present systematic review analyzed cohort studies and randomized controlled trials that examined whether prebiotics and probiotics are associated with ADHD. Seven randomized controlled trials and two cohort studies met our inclusion criteria. RESULTS: Research on Lactobacillus rhamnosus GG (LGG) probiotic supplementation showed that children with ADHD had better emotional, physical, social, and school functioning, and a higher health-related quality of life compared to the placebo group. The studies also showed that Synbiotic 2000 reduces markers of intestinal and vascular inflammation in children with ADHD, in part through increasing SCFA levels. CONCLUSION: The use of probiotics and prebiotics as adjuvants therapy in patients with ADHD is beneficial. Further studies with longer duration, including more participants and a variety of age groups, and using various evaluation techniques such as in vivo observation are required to examine the effects of prebiotics and probiotics on ADHD.

Wearable Potentiometric Sensor Based on Na0.44MnO2 for Non-invasive Monitoring of Sodium Ions in Sweat.

Here, we present a wearable potentiometric ion sensor for real-time monitoring of sodium ions (Na+) in human sweat samples using Na0.44MnO2 as the sensing material. Na0.44MnO2 is an attractive material for developing wearable electrochemical sensors due to its good Na+ incorporation ability, electrical conductivity, stability, and low fabrication cost. In the first step, the analytical performance of the electrode prepared using Na0.44MnO2 is presented. Then, a miniaturized potentiometric cell integrated into a wearable substrate is developed, which reveals a Nernstian response (58 mV dec-1). We achieved the detection of Na+ in the linear ranges of 0.21-24.54 mmol L-1, which is well within the physiological range of Na+. Finally, for on-body sweat analysis, the potentiometric sensor is fully integrated into a headband textile. This platform can be employed for non-invasive analysis of Na+ in human sweat for healthcare and disease diagnosis.

Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms.

Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose ("e-nose"), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18-61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set's variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.

Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder.

BACKGROUND: There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. METHODS: The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. RESULTS: First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. CONCLUSION: Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.

Assessment of Single-and Double-Strand Breaks in DNA Induced by Auger Electrons of Radioisotopes Used in Diagnostic and Therapeutic Applications.

INTRODUCTION: Most of the radionuclides that are used for diagnostic purposes emit Auger electrons and can thus cause damage to the DNA molecule on a nanometer scale. Therefore, the nanodosimetric calculation of these radioisotopes is necessary to achieve better understanding on their effects. AIM: The aim of this study was to calculate the mean number of DNA strand breaks (single-strand breaks and double-strand breaks) caused by direct and indirect effects for six widely used Auger electron-emitting diagnostic radioisotopes, including 123I, 125I, 99mTc, 67Ga, 201Tl, 111In and two therapeutic radioisotopes of 131I(beta + Auger + CK emitter) and 211At(alpha + Auger + CK emitter). MATERIALS AND METHODS: Geant4-DNA simulation tool was used to evaluate the effects of Auger electrons, beta and alpha particles of these radioisotopes on DNA molecules. Two different DNA molecule geometric models were simulated and the results of these two models were compared with each other as well as with the results of previous studies. RESULTS AND CONCLUSION: The results showed that the geometric shape of the sugar-phosphate groups may have a significant effect on the number of single-strand breaks (SSBs) and double-strand breaks (DSBs) of the DNA molecule. Among the most widely used diagnostic radioisotopes, 201Tl and 125I, had the greatest impact on the number of SSBs and DSBs, respectively, while therapeutic radioisotope of 131I almost had no effect, therapeutic radioisotope of 211At had the moderate effect on the number of breaks in the DNA chain.

Micro-dosimetry calculation of Auger-electron-emitting radionuclides mostly used in nuclear medicine using GEANT4-DNA.

A large number of nuclear medicine radionuclides are Auger-electron-emitters and internal conversion electrons which can transmit significant doses to the patient during diagnosis. Therefore, the dosimetry of these radioisotopes is necessary for the evaluation of their biological effects and their use for treatment and targeted-radiotherapy. In this study, dosimetry calculation of a number of widely used radioisotopes in nuclear medicine was performed on a cellular scale using Geant4-DNA simulation. S-values of some of the diagnostic radioisotopes, including 123I, 125I, 99mTc, 67Ga, 201Tl, and 111In, were evaluated in a homogeneous spherical geometry model with unit density in which the cell and nucleus were concentric. The results revealed that S-values of these diagnostic radioisotopes were mainly greater than S-values of the radioisotope 131I, which emits ß-particles; they were lower but can be compared with 211At (emitter of alpha particles) in the cellular scale. It shows better the importance of dosimetry calculation of diagnostic Auger-electron-emitting radioisotope in a cellular scale and their applicability in treatment. It should be noted that the S-values obtained out of the Geant4-DNA simulation are in line with the values of the other codes and the MIRD technique.