Probiotics as a boon in Food diligence: Emphasizing the therapeutic roles of Probiotic beverages on consumers' health

Probiotics are living microorganisms that improve health when eaten or introduced to the body. They have become an essential aspect of everyone’s life now due to these products growing health benefits. They contain live bacteria that help our gut maintain good gut microflora and keep us healthy. Lactobacillus and Bifidobacterium are the most used probiotic strains. Everyone knows the importance of these products nowadays and has highlighted the parts that a reader must know about these products and how they affect the human body. This review discusses probiotics, dairy-based probiotics, non-dairy based probiotics and their role in therapeutics. This review has also been elaborated on an essential by-product of the dairy industry, i.e., whey, which has many health benefits and highly recommended for people who are in sports and body-building. While analysing the health benefits of probiotic supplements, there is a lot to discuss,i.e. how and where probiotic products work in our body and provide us with different health benefits. The most common way a probiotic performs is by boosting our immune system and fighting diseases. While looking at the market part of probiotic products globally, it is a multibillion-dollar market mostly based in Europe, where people are more health-conscious. The probiotic market has now frowned worldwide and is one of the most profitable markets in the current world

The Effectiveness of fMRI Data when Combined with Polygraph Data

The Integrated Zone Comparison Technique (IZCT) was utilized with computerized polygraph instrumentation and the Academy for Scientific Investigative Training’s Horizontal Scoring System ASIT PolySuite algorithm, as part of a blind study in the detection of deception. This paper represents a synergy analysis of combining fMRI only deception data with each of the three individual physiological parameters that are used in polygraph. They include the electro-dermal response (EDR), pneumo, and cardio measurements. In addition, we compared the detection accuracy analysis using each single parameter by itself. Th e fMRI score and each individual polygraph parameter score on individual subjects were averaged to establish an overall score

Age related diffusion and tractography changes in typically developing pediatric cervical and thoracic spinal cord

Background and objective: Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) are two techniques that can measure white matter integrity of the spinal cord. Recently, DTI indices have been shown to change with age. The purpose of this study is (a) to evaluate the maturational states of the entire pediatric spinal cord using DTI and DTT indices including fractional anisotropy (FA), mean diffusivity (MD), mean length of white matter fiber tracts and tract density and (b) to analyze the DTI and DTT parameters along the entire spinal cord as a function of spinal cord levels and age. Method: A total of 23 typically developing (TD) pediatric subjects ranging in age from 6 to 16 years old (11.94 ± 3.26 (mean ± standard deviation), 13 females and 10 males) were recruited, and scanned using 3.0 T MR scanner. Reduced FOV diffusion tensor images were acquired axially in the same anatomical location prescribed for the T2-weighted images to cover the entire spinal cord (C1-mid L1 levels). To mitigate motion induced artifacts, diffusion directional images were aligned with the reference image (b0) using a rigid body registration algorithm performed by in-house software developed in Matlab (MathWorks, Natick, Massachusetts). Diffusion tensor maps (FA and MD) and streamline deterministic tractography were then generated from the motion corrected DTI dataset. DTI and DTT parameters were calculated by using ROIs drawn to encapsulate the whole cord along the entire spinal cord by an independent board certified neuroradiologist. These indices then were compared between two age groups (age group A = 6–11 years (n = 11) and age group B = 12–16 years (n = 12)) based on similar standards and age definitions used for reporting spinal cord injury in the pediatric population. Standard least squared linear regression based on a restricted maximum likelihood (REML) method was used to evaluate the relationship between age and DTI and DTT parameters. Results: An increase in FA (group A = 0.42 ± 0.097, group B = 0.49 ± 0.116), white matter tract density (group A = 368.01 ± 236.88, group B = 440.13 ± 245.24) and mean length of fiber tracts (group A = 48.16 ± 20.48 mm, group B = 60.28 ± 23.87 mm) and a decrease in MD (group A = 1.06 ± 0.23 × 10−3 mm2/s, group B = 0.82 ± 0.24 × 10−3 mm2/s) were observed with age along the entire spinal cord. Statistically significant increases have been shown in FA (p = 0.004, R2 = 0.57), tract density (p = 0.0004, R2 = 0.58), mean length of fiber tracts (p \u3c 0.001, R2 = 0.5) and a significant decrease has been shown in MD (p = 0.002, R2 = 0.59) between group A and group B. Also, it has been shown DTI and DTT parameters vary along the spinal cord as a function of intervertebral disk and mid-vertebral body level. Conclusion: This study provides an initial understanding of age related changes of DTI values as well as DTT metrics of the spinal cord. The results show significant differences in DTI and DTT parameters which may result from decreasing water content, myelination of fiber tracts, and the thickening diameter of fiber tracts during the maturation process. Consequently, when quantitative DTI and DTT of the spinal cord is undertaken in the pediatric population an age and level matched normative dataset should be used to accurately interpret the quantitative results. © 201

Network Influence of the Cerebellum for Predicting DBS Response in Patients with Advanced Parkinson’s Disease

Introduction: Deep brain stimulation (DBS) is a treatment option for reducing motor symptoms in patients with Parkinson’s Disease (PD) when first-line medication becomes ineffective. Existing literature has hypothesized that the clinical outcome of DBS may depend on brain connectivity profiles of the stimulation site to distant brain regions. However, the potential of brain connectivity profiles to predict response to DBS in PD remains unclear. Objective: This study aimed to investigate how changes in structural and functional connectivity may relate to patient response to DBS, through the examination of brain network changes using graph theory. Methods: Ten patients with advanced PD were included in this study. Diffusion tensor imaging (DTI) and resting-state fMRI scans were acquired prior to DBS implantation. Pre-DBS and post-DBS UPDRS-III scores were obtained. Network analysis of the DTI and fMRI scans was performed using GRETNA to compute structural and functional graph theory metrics, respectively. Metrics were correlated with UPDRS-III improvement to identify significant correlations to UPDRS improvement due to DBS. Results: Combined structural and functional graph theory metrics highlighted 32 structures to be significantly correlated with UPDRS-III improvement. Mainly, connections to the cerebellum were found to be significantly correlated with UPDRS-III improvement across several metrics for both structural and functional connectivity. Discussion: This work combined DTI, fMRI, and graph theory analysis to evaluate improvement with DBS. Several imaging biomarkers were identified that are robust predictors for UPDRS-III improvement. This work warrants investigation into the compensatory effect of the cerebellum and other potential biomarkers for identifying DBS candidates

Characteristic Dynamic Functional Connectivity During Sevoflurane-Induced General Anesthesia

General anesthesia (GA) during surgery is commonly maintained by inhalational sevoflurane. Previous resting state functional MRI (rs-fMRI) studies have demonstrated suppressed functional connectivity (FC) of the entire brain networks, especially the default mode networks, transitioning from the awake to GA condition. However, accuracy and reliability were limited by previous administration methods (e.g. face mask) and short rs-fMRI scans. Therefore, in this study, a clinical scenario of epilepsy patients undergoing laser interstitial thermal therapy was leveraged to acquire 15 min of rs-fMRI while under general endotracheal anesthesia to maximize the accuracy of sevoflurane level. Nine recruited patients had fMRI acquired during awake and under GA, of which seven were included in both static and dynamic FC analyses. Group independent component analysis and a sliding-window method followed by k-means clustering were applied to identify four dynamic brain states, which characterized subtypes of FC patterns. Our results showed that a low-FC brain state was characteristic of the GA condition as a single featuring state during the entire rs-fMRI session; In contrast, the awake condition exhibited frequent fluctuations between three distinct brain states, one of which was a highly synchronized brain state not seen in GA. In conclusion, our study revealed remarkable dynamic connectivity changes from awake to GA condition and demonstrated the advantages of dynamic FC analysis for future studies in the assessments of the effects of GA on brain functional activities

Alterations in Cerebral Glucose Metabolism Measured by FDG PET in Subjects Performing a Meditation Practice Based on Clitoral Stimulation

Background: The relationship between sexuality, or the libido, and spirituality or religion has long been debated in psychiatry. Recent studies have explored the neurophysiology of both sexual experiences and spiritual practices such as meditation or prayer. In the present study, we report changes in cerebral glucose metabolism in a unique meditation practice augmented by clitoral stimulation called, Orgasmic Meditation, in which a spiritual state is described to be attained by both male and female participants engaged in the practice as a pair. Methods: Male (N=20) and female (N=20) subjects had an intravenous catheter connected to a bag of normal saline inserted prior to the practice. During the practice, men stimulated their partner’s clitoris for exactly 15 minutes (he received no sexual stimulation). Midway through the practice, researchers injected 18F-fluorodeoxyglucose so the scan would reflect cerebral metabolism during the practice. Positron emission tomography (PET) imaging was performed approximately 30 minutes later. Results: In the female participants, the meditation state showed significant decreases in the left inferior frontal, inferior parietal, insula, middle temporal, and orbitofrontal regions as well as in the right angular gyrus, anterior cingulate and parahippocampus compared to a neutral state (p\u3c0.01). Male subjects had significant decreases in the left middle frontal, paracentral, precentral, and postcentral regions as well as the right middle frontal and paracentral regions during meditation (p\u3c0.01). Men also had significantly increased metabolism in the cerebellum and right postcentral and superior temporal regions (p\u3c0.01). Conclusions: These findings represent a distinct pattern of brain activity, for both men and women, that is a hybrid between that of other meditation practices and sexual stimulation. Such findings have potential psychotherapeutic implications and may deepen our understanding of the relationship between spiritual and sexual experience

Identification of Chronic Mild Traumatic Brain Injury Using Resting State Functional MRI and Machine Learning Techniques

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration: [clinicaltrials.gov], identifier [NCT03241732]

Cerebral Blood Flow and Brain Functional Connectivity Changes in Older Adults Participating in a Mindfulness-Based Stress Reduction Program

There is a growing interest in the potential beneficial effects of mindfulness meditation training in protecting against age-related physical, emotional, and cognitive decline. The current prospective, single-center, single-arm study investigated if functional magnetic resonance imaging-based changes in cerebral blood flow and brain functional connectivity could be observed in 11 elderly adults (mean age 79) after participation in a Mindfulness-Based Stress Reduction (MBSR) program. The results showed significantly (p \u3c 0.05) altered cerebral blood flow and functional connectivity in the cingulate gyrus, limbic structures, and subregions of the temporal and frontal lobes, similar to findings of other meditation-related studies in younger populations. Furthermore, these changes were also associated with significant improvements in depression symptoms. This study suggests that the MBSR program can potentially modify cerebral blood flow and connectivity in this population

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study

Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79–91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings