bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Inhibition of High Load Task on Individual Cognition：A Functional Near-Infrared Spectroscope Study
Authors: Zhou Chengmao , Song Xiaohui , Ye Changqing 
Published: May 2021

Abstract
Background: Cognitive load plays a vital role in human cognitive activities, and effective measurement and intervention of cognitive load can improve learners' learning effect. As a new method of measurement, physiological measurement can show the neural mechanism of cognitive overload. MRI and EEG, as the first physiological measurement methods, have made great contributions to the related research of cognition，but they have unavoidable flaws to a certain extent. As a new physiological measurement method in recent years, fNIRS can effectively avoid the influence of the above equipment on the experiment. This study uses fNIRS to measure cognitive load with the N-back classic paradigm, aiming to explore whether the sensitivity of the prefrontal area to the task and the difficulty of the task will affect the cognitive load of the individual. Results: Through the GLM model to concretely analyze the brain activation area and use repeated measurement analysis of variance to compare behavioral data, it can be concluded that the prefrontal cortex can be effectively activated under the stimulation of work tasks. In a certain range, the prefrontal lobe activation becomes more obvious with the increase of cognitive load, but when the cognitive load exceeds the maximum limit that the prefrontal lobe can handle, the prefrontal lobe will be in a negative activation state.Conclusion: These results suggest when the task load is appropriate, the PFC can be most effectively activated for working memory processing. Therefore, learners should be analyzed in teaching activities so as to choose appropriate teaching content to ensure the maximization of teaching effect.
The Alzheimer’s Disease THErapy with NEuroaid II (ATHENE) Study: A Double-Blind Randomized Delayed-Start Trial to Assess the Safety and Efficacy of MLC901 (NeuroAiD™II) as an add-on Treatment to Cholinesterase Inhibitors or Memantine in Patients With Mild to Moderate Alzheimer's Disease
Authors: Chen Christopher , Liu Qingshu , Moorakonda Rejesh Babu , Kandiah Nagaendran , Tan Boon Yeow , Villaraza Steven Gayoles , Cano Jemelle , Venketasubramanian Narayanaswamy 
Published: May 2021

Abstract
BackgroundPreclinical and clinical studies indicate a role for MLC901 (NeuroAiDTMII) in Alzheimer’s Disease (AD). We investigated its safety and efficacy as add-on therapy to standard treatment and evaluated a disease modifying effect in mild to moderate AD.MethodsMild-moderate probable AD patients by NINCDS-ADRDA criteria, stable on acetylcholinesterase inhibitors or memantine (n=125) were randomized to receive MLC901 (early starters) or placebo (delayed starters) for 6 months, followed by a further 6 months during which all patients received MLC901, in a delayed-start design. The primary outcome measure was serious adverse events at 6 months, secondary outcomes included the Alzheimer's Disease Assessment Scale - Cognitive subscale (ADAS-Cog) and other cognitive assessment scales.ResultsThere was no significant difference in the risk of serious adverse events between early and delayed starters at month (M) 6 (22.6% vs. 27.0%, risk difference = -4.4%, 90% CI -16.9 to 8.3%). Furthermore, there was no significant difference in the risk of adverse events, including the occurrence of stroke or vascular events, between early and delayed starters throughout the 12-month study period. The early-starters differed significantly on ADAS-Cog from the delayed-starters at M9 (mean difference -3.36, 95% CI -5.64 to -1.09) and M12 (mean difference -2.35, 95% CI -5.45 to 0.74). Other cognitive assessment scales showed trends in favor of MLC901.Conclusions﻿MLC901 is a safe adjunct to standard treatment for mild-moderate AD. There is no indication that the risk of any adverse events, including vascular, is increased with MLC901 in the study population. The cognitive outcomes provide support for a disease-modifying effect of MLC901 which requires confirmation in further studies. Clinical trial registration: ClinicalTrials.gov, NCT03038035. https://clinicaltrials.gov/ct2/show/NCT03038035
PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep Learning
Authors: Zhou Changjiang , Feng Xiaobing , Jin Yi , Gu Harvest F. , Zhao Youcai , Teng Xiaodong , Guo Lingchuan , Ji Jiatong , Jia Shuopeng , Xing Yan , Fan Xiangshan , Liao Jun 
Published: May 2021

Abstract
BackgroundThe possibility of digitizing whole-slide images (WSI) of tissue has led to the advent of artificial intelligence (AI) in digital pathology. Advances in precision oncology have resulted in an increasing demand for predictive assays that enable mining of subvisual morphometric phenotypes and might improve patient care ultimately. Hence, a pathologist-annotated and artificial intelligence-empowered platform for integration and analysis of WSI data and molecular detection data in tumors was established, called PAI-WSIT (http://www.paiwsit.com).MethodsThe standardized data collection process was used for data collection in PAI-WSIT, while a multifunctional annotation tool was developed and a user-friendly search engine and web interface were integrated for the database access. Furthermore, deep learning frameworks were applied in two tasks to detect malignant regions and classify phenotypic subtypes in colorectal cancers (CRCs), respectively.ResultsPAI-WSIT recorded 8633 WSIs of 1772 tumor cases, of which CRC from four regional hospitals in China and The Cancer Genome Atlas (TCGA) were the main ones, as well as cancers in breast, lung, prostate, bladder, and kidneys from two Chinese hospitals. A total of 1298 WSIs with high-quality annotations were evaluated by a panel of 8 pathologists. Gene detection reports of 582 tumor cases were collected. Clinical information of all tumor cases was documented. Besides, we reached overall accuracy of 0.933 in WSI classification for malignant region detection of CRC, and aera under the curves (AUC) of 0.719 on colorectal subtype dataset.ConclusionsCollectively, the annotation function, data integration and AI function analysis of PAI-WSIT provide support for AI-assisted tumor diagnosis, all of which have provided a comprehensive curation of carcinomas pathology.
Environmental Factors Contribute to the Transmissibility of COVID-19: Evidence from an Improved SEIR Model
Authors: Li Jie , Jia Kun , Zhao Wenwu , Yuan Bo , Liu Yanxu 
Published: May 2021

Abstract
COVID-19 is ravaging Brazil, and its spread shows spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors, and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model, and the results were considered to be an improvement over the SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
Identification of abnormal lipid profiles promote cognitive decline in Alzheimer’s disease spectrum via large-scale brain connectivity
Authors: Wang Qing , Zang Feifei , He Cancan , Zhang Zhijun , Xie Chunming 
Published: May 2021

Abstract
Background: Lipid metabolite dysfunction makes a substantial contribution to the clinical signs and pathophysiology of Alzheimer’s disease (AD). It is unclear that the role of dyslipidemia in the promotion of neuropathological processes and brain functional impairment that subsequently facilitates the progression of AD.Methods: Lipid pathway-based polygenic scores and large-scale resting-state networks (RSNs), was constructed. Together with canonical correlation analysis (CCA) and support vector machine (SVM) model, to explore the effects of lipid-related polygenic scores and blood lipoproteins on the molecular biomarkers, cognitive function, as well as large-scale RSNs. Associations between lipid-related genetic scores, serum lipoproteins, cognitive function, CSF biomarkers and RSNs were examined.Results: Dynamic trajectory of large-scale RSNs was exhibited significantly differential connectivity within-network, one-versus-all-others-network, and pairwise between networks across the AD spectrum (ADS). Importantly, the summative effects of lipid-pathway genetic variants and lipoproteins significantly promoted the process of β-amyloid and Tau levels and cognitive decline, and preferentially targeted functional couplings within- and between RSNs in the ADS, supporting the hypothesis that abnormal lipid profiles in the AD pathogenesis via disrupting large-scale RSNs and accelerating molecular pathological processes, consequently exacerbating cognitive decline.Conclusions: Our findings reveal the importance of lipids in the pathogenesis of AD via disruption of RSNs and acceleration of molecular pathological processes, consequently exacerbating cognitive decline. These findings provide the potential lipid-associated neuroimaging biomarkers for ADS.
Comparison of immunotherapy and prognosis of seizures caused by viral encephalitis and autoimmune encephalitis
Authors: Zhang Xianjun , Han Yanbing , Liu Xiaojuan , Yang Wenqiu , Wang Ting , Zhou Liang , Yang Wufeng , Ya Jinrong , Wang Shitao , Wu Qian 
Published: May 2021

Abstract
Background: The immunotherapy that is more effective for seizures caused by viral encephalitis and autoimmune encephalitis and the long-term use of anti-epileptic drugs is not clear. We aimed to compare the immunotherapy and prognosis of seizures caused by viral encephalitis and autoimmune encephalitis.Methods: Clinical data of 121 patients with seizures caused by viral encephalitis and autoimmune encephalitis diagnosed and treated in the two largest tertiary general hospitals in the Yunnan Province were retrospectively collected to compare the immunotherapy used. Dynamic follow-up was performed to observe seizures and the use of antiepileptic drugs.Results: The seizure-free rates at 6 months and 12 months after the onset of viral encephalitis were 77.8% and 80.8%, respectively. In total, 79.1% of autoimmune encephalitis cases were seizure-free at 6 months after onset, and the seizure-free rate at 12 months was 91.9%. A total of 75.0% of viral encephalitis and 67.7% of autoimmune encephalitis patients discontinued antiepileptic drugs and were seizure-free at 12 months after onset. Patients with viral encephalitis treated with glucocorticoids alone had a lower risk of seizures after the acute phase than those treated with glucocorticoids combined with immunoglobulin (P < 0.05). The risk of seizures in patients with autoimmune encephalitis treated with glucocorticoids combined with immunoglobulin was lower than that in patients treated with glucocorticoids and immunoglobulin alone (P < 0.05).Conclusions: Immunotherapy may improve the seizure prognosis of patients with acute encephalitis. The prognosis of seizures due to viral encephalitis may be determined as early as 6 months after onset, while the seizure outcome of autoimmune encephalitis is further improved 12 months after onset.
Potential Economic Value of Water Resource Sustainability for Sustainable Environment: A Case Study in South Sumatra, Indonesia
Authors: Juniah Restu , Toha Muhammad Taufik , Zakir Syaifudin , Rahmi Hisni 
Published: May 2021

Abstract
Effective water resource management offers a very significant value across social, economic, commercial and environmental application. For this fundamental reason, adequate sustainability becomes equally crucial. Therefore, activities geared towards the depletion in quantity, quality, and loss of potable water resources, particularly for communities, demand urgent reconsiderations. Erosion in coal mining land causes depletion in water quantity and quality, as well as inadequate drinking water resources for surrounding communities, making water resources unsustainable. Meanwhile, reclamation reduces erosion, but is unable to restore water depletion optimally, thus, these resources remain unsustainable. these resources remain unsustainable. However, recycling depleted water utilization for drinking provides economic value for the environment, as well as the community’s drinking water resources’ availability and sustainability. The objectives of this study were to develop water resource sustainability concept for a sustainable environment by analyzing the potential economic value and secondly, calculate the water resource value due to erosion, reclamation, domestic and economic importance, by recycling efforts. The method used in this study was the Extended NPV. Furthermore, the total potential economic value of water resources loss resulting in water resource unsustainability was IDR 1,137,621,671,375 or IDR 1.14 trillion, while the potential economic value of depleted water utilization for drinking was IDR 2,298,339,797,000 or IDR 2.3 trillion. Therefore, this utilization provides potential economic value worth IDR 1.16 trillion, for the resources’ sustainability in the TAL area of PTBA. The study’s results found and recommended depleted water utilization for drinking as a suitable method to replace water resources lost due to erosion, community drinking water resource loss, and to discover a sustainable environment’s water resources sustainability concept. In addition, the study formulates environmental economics as a new mining science related to natural resource economics as well as mining, for sustainable water resources and mining environment.
Magnetohydrodynamics Mixed Convective Couple Stress Hybrid Nanofluid Darcy-Forchheimer Flow through a Rotating Porous Space
Authors: Alharbi F.M , Jawad Muhammad , Zubair Muhammad , Naeem Muhammad , Amin Ibn-i- , Jan Rashid 
Published: May 2021

Abstract
In this study, we consider the magnetohydrodynamics mixed convective couple stress hybrid nanofluid Darcy-Forchheimer flow through a rotating porous space with velocity slip condition. The nonlinear thermal stratification and thermal radiation of Magnetohydrodynamics (MHD) are discussed in detail. For relative analysis, we have taken the nanoparticals samples of Aluminum oxides (Al2O3) and Titanium dioxide (TiO2). The rotation in the disk is produces for the generation of the flow in the system.Furthermore, the variable permeability and porosity of porous space is regarded as Darcy-Forchheimer expression. The resulting nonlinear system of ODE’s are solved by Homotopy Analysis Method (HAM). The governing of several sundry parameters i.e. “Couple Stress, coefficient of inertia, radiation parameter, magnetic parameter, Prandtl number, heat source or sink parameter” are presented both graphically as well as in numerical tables. The behavior of the flow predicted that the increase of both mixed convection and couple stress parameters cause increase in the momentum profile. Temperature of the system rises for higher values of radiation parameter and magnetic parameter. The higher local heat transfer rate of Aluminum oxides (Al2O3) and Titanium oxide (TiO2)or water is examined as compared to hybrid nanofluid.
Are we asking the right questions? Working with the LGBTQ+ community to prioritise healthcare research
Authors: Crowe Sally , Barker Eleanor , Roberts Meg , Lloyd Lucy , de Barros Clara M , Rebelo-Harris Ben , Meads Catherine , Saunders Catherine L 
Published: May 2021

Abstract
Conversations about research priorities with members of the public are rarely designed specifically to include people who identify as Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ+) and are not researchers. We carried out a rapid review of published research priority sets covering LGBTQ+ topics, and an online workshop. The rapid review identified 18 LGBTQ+ research priority sets. Some focussed on specific populations such as women or men, younger or older people or people living within families. Five addressed transgender and gender non- conforming populations. All of the research priority sets originated from English-speaking, high and middle-income countries (UK, US, Canada, and Australia), and date from 2016 onwards. Prioritization approaches were wide-ranging from personal commentary to expert workshops and surveys. Participants involved in setting priorities mostly included research academics, health practitioners and advocacy organisations, two studies involved LGBTQ+ public in their process. Research priorities identified in this review were then grouped into themes which were prioritised during the workshop. For the workshop, participants were recruited using local (Cambridge, UK) LGBTQ+ networks and a national advert to a public involvement in research matching website to take part in an online discussion workshop. Those that took part were offered payment for their time in preparing for the workshop and taking part. Participants personal priorities and experiences contributed to a consensus development process and a final ranked list of seven research themes. Participants’ experiences of healthcare, mental health advocacy, care homes, caring responsibilities, schools and family units added additional contexts. These ranged from living with neurodiversity, and disability as well as experiencing social and economic inequalities and multigenerational health issues. From the workshop the three research themes prioritised were: healthcare services delivery, prevention, and particular challenges /intersectionality of people identifying as lesbian, gay, bisexual, transgender or queer. Research themes interconnected in many ways and this was demonstrated by the comments from workshop participants. This paper offers insights into why these priorities were important from participants’ perspectives and detail about how to run an inclusive and respectful public involvement research exercise. The workshop outcomes are currently being used to inform research funding applications by CLS.
Effects of Switching From Degludec to Glargine U300 in Patients With Type 1 Diabetes Having Large Day-to-day Glycemic Variability: a Retrospective Study
Authors: Sawamura Toshitaka , Karashima Shigehiro , Ohbatake Azusa , Higashitani Takuya , Katsuda Yuko , Yamamoto Rika , Aono Daisuke , Kometani Mitsuhiro , Takeda Yoshiyu , Yoneda Takashi 
Published: May 2021

Abstract
Background: Degludec (Deg) and Glargine U300 (Gla-300) are new insulin analogues with longer and smoother pharmacodynamic action than Glargine U100. Both improve glycemic variability (GV) unlike Glargine U100. However, it is not clear which insulin analogue has a better effect on GV in insulin-dependent type 1 diabetes. We evaluated the effects of switching from Deg to Gla-300 on day-to-day GV in patients with insulin-dependent type 1 diabetes treated with Deg.Methods: We conducted a retrospective study on 22 insulin-dependent type 1 diabetes patients having large day-to-day GV or frequent hypoglycemia who were treated with multiple insulin injection therapy including Deg and were advised to switch from Deg to Gla-300. We evaluated day-to-day GV and frequency of hypoglycemia in two groups. The first group included patients whose treatment was changed to Gla-300, and the second group included patients who opted to continue receiving Deg. We evaluated the change in standard deviation (SD) of fasting blood glucose (SD-FBG) calculated from self-monitoring of blood glucose (SMBG) and frequency of hypoglycemia (total, severe, and nocturnal).Results: SD-FBG and frequency of nocturnal hypoglycemia decreased in Gla-300 group compared to those in Deg group. The change in SD-FBG had a negative correlation with SD-FBG and hemoglobin A1c (HbA1c) at baseline and positive correlation with serum albumin levels at baseline in Gla-300 group. On the other hands, the change in SD-FBG had no correlation with these markers in Deg group.Conclusions: Switching from Deg to Gla-300 effectively stabilized blood glucose levels and decreased nocturnal hypoglycemia in insulin-dependent type 1 diabetes treated with Deg, especially in cases with low serum albumin, large GV, and high HbA1c.
A Machine Learning Framework for Predicting Drug-drug Interactions
Authors: Mei Suyu , Zhang Kun 
Published: May 2021

Abstract
Understanding drug-drug interaction is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods commonly integrate multiple heterogeneous data sources to increase model performance but result in a high model complexity. To elucidate the molecular mechanisms behind drug-drug interactions and reserve rational biological interpretability is a major concern in computational modeling. In this study, we propose a simple representation of drug target profiles to depict drug pairs, based on which an l2-regularized logistic regression model is built to predict drug-drug interactions. In addition, we develop several statistical metrics to measure the communication intensity, interaction efficacy and action range between two drugs in the context of human protein-protein interaction networks and signaling pathways. Cross validation and independent test show that the simple feature representation via drug target profiles is effective to predict drug-drug interactions and outperforms the existing data integration methods. Statistical results show that two drugs easily interact when they target common genes, or their target genes communicate with each other via short paths in protein-protein interaction networks or through cross-talks between signaling pathways. The unravelled mechanisms provide biological insights into potential pharmacological risks of known drug-drug interactions and drug target genes.