Differentiating schizophrenic patients from healthy control; application of machine learning to resting state fmri

In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data. Most of these studies focus on fMRI low frequency oscillations. This study focuses on the amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF). A Voxel-wise analysis is performed on the whole brain for two groups of subjects. A machine learning algorithm is applied to two independent groups of subjects (a total of 160 healthy control and schizophrenic subjects) to classify Schizophrenia subjects from healthy control. Kendall tau rank correlation coefficient is also used to dominate most important voxels (features). This study is done on three datasets: a) fALFF b) mALFF dataset and c) combination of mALFF and fALFF. The results show that using the combination dataset improves the classification and demonstrates that machine learning algorithms can extract new information from a resting state image of schizophrenia which can help in diagnosing and treating schizophrenic patients in the future. Future studies can focus on testing these algorithms on different modalities and moreover on different physiological disorders

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Oligopin® Supplementation Mitigates Oxidative Stress in Postmenopausal Women with Osteopenia: A Randomized, Double-blind, Placebo-Controlled Trial

Background: Evidence indicates a close association between oxidative stress and the etiopathogenesis of osteopenia. In vitro and animal studies report that Oligopin®, an extract of French maritime pine bark extract, has beneficial effects on oxidative stress. Purpose: Here, we aimed to determine whether supplementation with Oligopin® affects bone turnover markers, antioxidant enzymes, and oxidative stress markers in these patients. Methods: Forty-three postmenopausal women with osteopenia were randomized in a placebo-controlled, double-blind clinical trial to receive either 150 mg/day Oligopin® (n = 22) or placebo (n = 21) for 12 weeks. Plasma levels of bone turnover markers; osteocalcin (OC), type I collagen cross-linked C-telopeptide (CTX-1), OC/CTX1 ratio along with total antioxidant capacity(TAC), malondialdehyde (MDA) concentration, protein carbonyl, and total thiol contents in plasma, activities of manganese superoxide dismutase (MnSOD) and catalase in both peripheral blood mononuclear cells (PBMCs) and plasma as well as mRNA expression of MnSOD, catalase, and Nrf2 in PBMCs were measured at the baseline and the end of the intervention. Results: Oligopin® supplementation significantly increased OC levels and the ratio of OC to CTX1 in women with osteopenia compared to placebo intervention after 12 weeks. Oligopin® significantly decreased plasma protein carbonyl content in postmenopausal women compared with the after placebo treatment. Moreover, Oligopin® intervention significantly increased plasma total thiol content, TAC, plasma activity of both MnSOD and catalase, and the transcript level of Nrf2, MnSOD, and catalase in comparison with the placebo group. Conclusion: Supplementation with 150 mg/day Oligopin® for 12 weeks exerts beneficial effects in postmenopausal osteopenia through improving the antioxidant defense system in the plasma and PBMCs that was accompanied by an increase in indicators of bone turnover. © 202

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

: Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance