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Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD), with an incidence of more than 20%. IDH induces ischemic organ damage and even reduces the ultrafiltration and duration of HD sessions. Frequent attacks of IDH are a risk factor for death in HD patients. Malnutrition is common in HD patients and is also associated with mortality. Although the link between IDH episodes and malnutrition has been observed in practice, it has not been supported by the data. To study the relationship, we propose a promising hybrid model called BSCWJAYA_KELM, which is a wrapper feature selection method based on a variant of the JAYA optimization algorithm (SCWJAYA) and Kernel extreme learning machine (KELM). In this paper, we verify the optimization capability of the SCWJAYA algorithm in the model by comparing experiments with some state-of-the-art methods for IEEE CEC2014, IEEE CEC2017, and IEEE CEC2019 benchmark functions. The prediction accuracy of BSCWJAYA_KELM is validated by the public datasets and the HD dataset. In the experiments on the HD dataset, 1940 HD sessions of 178 HD patients are analyzed by the developed BSCWJAYA_KELM model. The key indicators selected from vast amounts of data are serum uric acid, dialysis vintage, age, diastolic pressure, and albumin. The BSCWJAYA_KELM method is a stable and excellent prediction model that can achieve a more accurate prediction of IDH., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Intradialytic hypotension (IDH) is the most common acute complication in hemodialysis (HD) sessions and is associated with increased morbidity and mortality in HD patients. To prevent the episode of IDH, it is critical to predict its occurrence. Chronic kidney disease-mineral and bone disorders (CKD-MBD) induce cardiac and vascular calcification, which impairs the compensatory mechanisms of blood pressure during HD. In this study, we proposed a feature selection framework called BSWEGWO_KELM to analyze 1940 records from 178 HD patients, which was based on an enhanced grey wolf optimization (GWO) algorithm and the kernel extreme learning machine (KELM). Then, global optimization experiments, together with feature selection experiments on public data sets and HD dataset, were performed to verify the effectiveness of the BSWEGWO_KELM method. The experimental results showed that the established BSWEGWO_KELM had the capability of screening out the key indicators such as dialysis vintage, mean arterial pressure (MAP), alkaline phosphatase (ALP), and intact parathyroid hormone (iPTH). Consequently, BSWEGWO_KELM can be applied as a practical and accurate method to predict IDH., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Inorganic arsenic (iAs) is a group 1 carcinogen, and consumption of rice can be a significant pathway of iAs exposure in the food chain. Although there are regulations in place to control iAs for marketed rice in some countries, additional measures are explored to remove arsenic from rice. Due to the surface-bound and soluble nature of iAs, previous studies have shown that it can be removed to a significant extent using different cooking methods. Towards this goal we modified and tested the absorption method in combination with four home-friendly cooking treatments (UA = unwashed and absorbed, WA = washed and absorbed, PSA = pre-soaked and absorbed, and PBA = parboiled and absorbed) using both brown and white rice (3 types each). The nutrient elements were measured using ICP-MS and arsenic speciation was carried out using LC-ICP-MS. Overall, our results show that PBA was the optimum approach assessed, removing 54% and 73% of inorganic arsenic (iAs) for brown and white rice respectively, raising the margin of exposure (MOE) by 3.7 for white rice and 2.2 times for brown rice, thus allowing the consumption of rice more safely for infants, children and adults. Other cooking treatments were effective in reducing the iAs concentration from white rice only. Here we also report changes in selected nutrient elements (P, K, Mg, Zn and Mn) which are relatively abundant in rice. In general, the treatments retained more nutrients in brown rice than white rice. No significant loss of Zn was observed from both rice types and the loss of other nutrients was similar or less than in comparison to reported losses from rice cooked in excess water in the literature. We conclude that PBA is a promising technique and further research is needed by including different regional rice types and water quality levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Trehalose is the major blood sugar in insects; it not only serves as an energy source but also plays important roles in physiological responses to adverse conditions. However, only a few studies have explored the effects of heavy metal exposure stress on trehalose metabolism in insects. Therefore, in this study, we examined the effects of cadmium stress on changes in trehalose metabolism in Aedes albopictus. Three concentrations of cadmium (0.005, 0.01, and 0.1 mg/L) were selected for evaluation of long-term stress in Ae. albopictus (from eggs to adults); Ae. albopictus in double-distilled water was used as the control group. The trehalose and glucose contents, trehalase activity, and trehalose metabolism-related gene expression were determined. The effects of long-term cadmium exposure on growth, development, and reproduction were also assessed. Trehalose contents were increased, whereas glucose contents and trehalase activity were decreased in Ae. albopictus following long-term exposure to low concentrations of cadmium compared with those in untreated individuals. Moreover, the expression of trehalose-6-phosphate synthase was upregulated, and that of trehalase was downregulated, indicating that Ae. albopictus may enhance trehalose synthesis to resist cadmium stress. Cadmium exposure also caused Ae. albopictus individuals to become smaller with a longer developmental duration, whereas both reproduction and hatching rates of the offspring were decreased compared with those in the control group. Our findings demonstrated that cadmium exposure affected the morphology, physiology, and biochemistry of Ae. albopictus. These findings also confirmed the role of trehalose in the response of Ae. albopictus to cadmium stress, providing insights into the effects of heavy metal stress on trehalose metabolism in an insect model., (Copyright © 2020 Elsevier Inc. All rights reserved.)

LncRNA H19 has been widely reported to be up-regulated upon hypoxia. We aimed to uncover the function and mechanism of lncRNA H19 in hypoxic PC-12 cells. Neural-like PC-12 cells were exposed to hypoxia to stimulating an in vitro model of hypoxic brain damage. The expression levels of lncRNA H19 in PC-12 cells were altered by transfection, then cell viability, migration and apoptosis were assessed respectively. Moreover, the cross-regulation between lncRNA H19, miR-28 and SP1 was studied to reveal one of the possible mechanisms of lncRNA H19's function. We found that hypoxia induced remarkable decreases in cell viability and migration, and induced a notable increase in cell apoptosis. Hypoxia-induced cell damage was aggravated by lncRNA H19 overexpression, while was alleviated by lncRNA H19 silence. miR-28 was negatively regulated by lncRNA H19, and SP1 was a target gene of miR-28. Furthermore, lncRNA H19 down-regulated miR-28 expression, which in turn preventing SP1 from degradation by miR-28, and ultimately deactivated PDK/AKT and JAK/STAT signaling pathways. In conclusion, our research demonstrated a protective role of lncRNA H19 silence in hypoxic PC-12 cells. A possible mechanism of which lncRNA H19 functioned to PC-12 cells was that lncRNA H19 down-regulated miR-28 expression, preventing SP1 exhausted by miR-28., (Copyright © 2018 Elsevier B.V. All rights reserved.)