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Background: Irisin as an exercise-induced myokine was proposed to improve bone health. This study investigated the role of serum irisin (s-irisin) in patients with osteoporosis (OP) through correlating to most biological bone markers and oxidative stress. Methods: A cross-sectional study recruited an eligible 175 postmenopausal women at Al-Hussien Teaching Hospital, Iraq. They were scanned by DEXA and stratified into two groups based on T-score; the first 95 patients as control group (GI) with −1 ≤ T-score and the second 80 patients as cases group (GII) with T-score ≤ −2.5. Demographic criteria were age, bone mineral density (BMD, g/cm 2 ) and T-score. Serum irisin, total serum calcium (s-calcium), serum inorganic phosphate (s-phosphate), serum alkaline phosphatase (s-ALP), serum 25 [OH] vitamin D, the serum parathyroid hormone (s-PTH), serum Carboxy terminal collagen crosslinks (CTx), serum procollagen type I C-termidnal peptide (s-PICP), serum malondialdehyde (s-MDA) and serum superoxide dismutase (s-SOD) were collected from blood samples. Results: Serum irisin were 31.84 ± 2.65 vs. 20.88 ± 2.71 ng/mL for control and trial groups, respectively. Lower levels of BMD, T-score, 25 [OH] vitamin D, and s-irisin along with a higher serum levels of PTH, CTx, PICP, MDA and SOD were observed in patients with osteoporosis. All parameters were statistically meaningful upon correlation (p< 0.0001), except age and s-calcium (p= 0.0088 and p= 0.187, respectively). Conclusions:The results showed that, a significantly lower serum irisin levels among osteoporosis women, was intimately correlated to most bone turnover markers and it can be considered as encouraging results for clinical application in prediction and treatment of osteoporosis.

An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R 2 , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

BACKGROUND: Neurovascular coupling (NVC) is a key process in cerebral blood flow regulation. NVC ensures adequate brain perfusion to changes in local metabolic demands. Neuronal nitric oxide synthase (nNOS) is suspected to be involved in NVC; however, this has not been tested in humans. Our objective was to investigate the effects of nNOS inhibition on NVC in humans. METHODS: We performed a 3-visit partially randomized, double-blinded, placebo-controlled, crossover study in 12 healthy subjects. On each visit, subjects received an intravenous infusion of either S-methyl-L-thiocitrulline (a selective nNOS-inhibitor), 0.9% saline (placebo control), or phenylephrine (pressor control). The NVC assessment involved eliciting posterior circulation hyperemia through visual stimulation while measuring posterior and middle cerebral arteries blood velocity. RESULTS: nNOS inhibition blunted the rapidity of the NVC response versus pressor control, evidenced by a reduced initial rise in mean posterior cerebral artery velocity (−3.3% [−6.5, −0.01], P =0.049), and a reduced rate of increase (ie, acceleration) in posterior cerebral artery velocity (slope reduced −4.3% [−8.5, −0.1], P =0.045). The overall magnitude of posterior cerebral artery response relative to placebo control or pressor control was not affected. Changes in BP parameters were well-matched between the S-methyl-L-thiocitrulline and pressor control arms. CONCLUSIONS: Neuronal NOS plays a role in dynamic cerebral blood flow control in healthy adults, particularly the rapidity of the NVC response to visual stimulation. This work opens the way to further investigation of the role of nNOS in conditions of impaired NVC, potentially revealing a therapeutic target.