Proton pump inhibitors are associated with increased risk of development of chronic kidney disease

Background Acute interstitial nephritis secondary to proton pump inhibitors (PPIs) frequently goes undiagnosed due to its subacute clinical presentation, which may later present as chronic kidney disease (CKD). We investigated the association of PPI use with the development of CKD and death. Methods Two separate retrospective case–control study designs were employed with a prospective logistic regression analysis of data to evaluate the association of development of CKD and death with PPI use. The population included 99,269 patients who were seen in primary care VISN2 clinics from 4/2001 until 4/2008. For evaluation of the CKD outcome, 22,807 with preexisting CKD at the first observation in Veterans Affairs Health Care Upstate New York (VISN2) network data system were excluded. Data obtained included use of PPI (Yes/No), demographics, laboratory data, pre-PPI comorbidity variables. Results A total of 19,311/76,462 patients developed CKD. Of those who developed CKD 24.4 % were on PPI. Patients receiving PPI were less likely to have vascular disease, COPD, cancer and diabetes. Of the total of 99,269 patients analyzed for mortality outcome, 11,758 died. A prospective logistic analysis of case–control data showed higher odds for development of CKD (OR 1.10 95 % CI 1.05–1.16) and mortality (OR 1.76, 95 % CI 1.67–1.84) among patients taking PPIs versus those not on PPIs. Conclusions Use of proton pump inhibitors is associated with increased risk of development of CKD and death. With the large number of patients being treated with proton pump inhibitors, healthcare providers need to be better educated about the potential side effects of these medications

Perceived changes in the mental well‐being among Nigerians due to Ramadan Intermittent Fasting during the COVID‐19 pandemic

INTRODUCTION: Muslims fast every year during the month of Ramadan from dawn until dusk. This study examined mental well-being and correlating factors among Nigerian adults who observed Ramadan intermittent fasting (RIF). METHODS: A validated generalized anxiety disorder-2 and Patient Health Questionnaire-2, the four-item spiritual well-being index, and the Islamic intrinsic religiosity questionnaire were used to collect data about mental well-being (depression, anxiety), spirituality, and intrinsic religiosity through a web-based survey between the May 9, 2021 (27th of Ramadan, 1442) and the June 4, 2021 (29th of Shawwal, 1442). We studied the mental well-being of respondents over a period of 4 weeks before Ramadan (BR) and during the 4 weeks of Ramadan between the April 12, 2021 and the May 12, 2021(DR). Multinomial regression analysis was used to determine the factors associated with depression and anxiety. This research did not receive any grant or funding. RESULTS: A total of 770 adult Nigerians who observed RIF study were included in this cross-sectional study. When compared to mental well-being BR, observing RIF by Nigerian adult respondents was associated with a significant improvement in their mental well-being. A higher proportion of respondents felt less depressed DR (61.3% vs. 56.5%. \u3c .001). Interest and pleasure in doing things improved DR than BR (p= 0.007) and respondents felt less nervous and anxious (60.7% vs. 57.1%, respectively; p \u3c.001). Mental well-being was independently associated with sociodemographic characteristics, physical activity, and perceived relationships. CONCLUSIONS: This study found significant improvement in mental well-being DR compared to BR despite the ongoing COVID-19 pandemic. The effect of RIF on mental well-being needs further research with multicentric studies among different sets of ethnic populations

A combination of methods needed to assess the actual use of provisioning ecosystem services

Failure to recognize that potential provisioning ecosystem services are not necessarily collected and used by people may have important consequences for management of land and resources. Accounting for people's actual use of ecosystem services in decision making processes requires a robust methodological approach that goes beyond mapping the presence of ecosystem services. But no such universally accepted method exists, and there are several shortcomings of existing methods such as the application of land use/cover as a proxy for provisioning ecosystem service availability and surveys based on respondents' recall to assess people's collection of e.g. wild food. By combining four complementary methods and applying these to the shifting cultivation systems of Laos, we show how people’s actual use of ecosystem services from agricultural fields differs from ecosystem service availability. Our study is the first in Southeast Asia to combine plot monitoring, collection diaries, repeat interviews, and participant observation. By applying these multiple methods borrowed from anthropology and botany among other research domains, the study illustrates that no single method is sufficient on its own. It is of key importance for scientists to adopt methods that can account for both availability of various services and actual use of those services

Kinesin expands and stabilizes the GDP-microtubule lattice

Kinesin-1 is a nanoscale molecular motor that walks towards the fast-growing (plus) ends of microtubules, hauling molecular cargo to specific reaction sites in cells. Kinesin-driven transport is central to the self-organization of eukaryotic cells and shows great promise as a tool for nano-engineering1. Recent work hints that kinesin may also play a role in modulating the stability of its microtubule track, both in vitro2,3 and in vivo4, but the results are conflicting5,6,7 and the mechanisms are unclear. Here, we report a new dimension to the kinesin–microtubule interaction, whereby strong-binding state (adenosine triphosphate (ATP)-bound and apo) kinesin-1 motor domains inhibit the shrinkage of guanosine diphosphate (GDP) microtubules by up to two orders of magnitude and expand their lattice spacing by ~1.6%. Our data reveal an unexpected mechanism by which the mechanochemical cycles of kinesin and tubulin interlock, and so allow motile kinesins to influence the structure, stability and mechanics of their microtubule track

Examination of sleep in relation to dietary and lifestyle behaviors during Ramadan: A multi-national study using structural equation modeling among 24,500 adults amid COVID-19

Background Of around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors. Methods This cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters. Results In total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7–9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters. Conclusion Increasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF

LayNii: a software suite for layer-fMRI

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis