bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors That Influence Mental Health Of University And College Students In The UK: A Rapid Review
Authors: Campbell Fiona , Blank Lindsay , Cantrell Anna , Baxter Susan , Blackmore Christopher , Dixon Jan , Goyder Elizabeth 
Published: February 2022

Abstract
Background: Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students. Objectives: To identify factors associated with mental health of students in higher education.Methods: We undertook a rapid review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010-2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.Findings: We included 30 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy. Conclusion: Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.
A mechanosensitive vascular niche for Drosophila hematopoiesis
Authors: Crozatier michele , Tian Yushun , Morin-Poulard Ismaël , Vanzo Nathalie 
Published: February 2022

Abstract
Hematopoietic stem and progenitor cells maintain blood cell homeostasis by integrating various cues provided by specialized microenvironments or niches. Biomechanical forces are emerging as key regulators of hematopoiesis. Here we report that mechanical stimuli provided by blood flow in the vascular niche control Drosophila hematopoiesis. In vascular niche cells, the mechanosensitive channel Piezo transduces mechanical forces through intracellular calcium upregulation, leading to Notch activation and repression of FGF ligand transcription, known to regulate hematopoietic progenitor maintenance. Our results provide insight into how the vascular niche integrates mechanical stimuli to regulate hematopoiesis.
Identification and quantification of Cinnamomum camphora seeds extract and development of citric acid and polyphenols based carboxymethyl chitosan-gum Arabic hybrid film for intelligent packaging applications
Authors: Alnadari Fawze , Bassey Anthony Pius , Abdin Mohamed , Salama Mohamed Abdelbaset , Nasiru Mustapha Muhammad , Dai Zhuqing , Hu Yuhang , Zeng Xiaoxiong 
Published: February 2022

Abstract
In this study, carboxymethyl chitosan with gum Arabic (CG) based novel functional films containing Cinnamomum camphora seeds extract (CCSE) at varying concentrations (0.5, 1.2 and 2% in w/w) were successfully fabricated. Moreover, sixteen phenolic compounds were identified by LC-ESI-QTOF-MS and the most abundant organic acid was citric acid (present at 1062.55 µg/g dry weight in CCSE). SEM showed that higher extract concentration indicated a homogeneous surface and more stability of films. CCSE inclusion into CG resulted in a marked increase (33.01 to 63.23 MPa) of tensile properties, with optimum thickness. The swelling degree, water solubility, and water vapor permeability were markedly (p < 0.05) reduced from 54.21 to 31.70%, 32.05 to 14.02%, and 3.46 to 1.12 × 10−11g−1s−1Pa−1, respectively. The open control and CG samples showed much higher peroxide values of sunflower oil (69.49 ± 0.68 and 20.15 ± 0.31 mEq/kg, respectively) while CG-CCSE2 exhibited a low value (9.79 ± 0.28 mEq/kg) after 28 days incubation. The good antioxidant and antimicrobial properties exhibited by the fabricated films demonstrated their potential use in the food packaging industry.
Fabrication and Response Optimization of Moringa Oleifera-Functionalized Nanosorbents for the Removal of Diesel Range Organics from Contaminated Water
Authors: Chima Ubah Promise , Dashti Arezoo Fereidonian , Saaid Mardiana , Imam Saifullahi Shehu , Adnan Rohana 
Published: February 2022

Abstract
The purpose of this research is to synthesize environmentally friendly nanosorbents capable of Diesel Range Organics (DRO) adsorption from contaminated water. Central Composite Design (CCD) analysis of response surface methodology (RSM) was employed in a model fitting of the variables predicting the adsorption efficiency of Moringa Oleifera-functionalized zerovalent iron particles (ZINPs) for the removal of DRO. The effects of the reaction parameters on the response were screened using 24 factorial design to determine the statistically significant independent variables. A quadratic model predicting the DRO adsorption efficiency of ZINPs with an F-value of 276.84 (p-value <0.0001) was developed. Diagnostic plots show that the predicted values were in excellent agreement with actual experimental values (R2 = 0.99). The maximum percentage removal of DRO of 92.6% was achieved after optimization, using the synthesized ZINPs after 8 hours of contact between DRO substrates and ZINPs at pH of 8, the temperature of 25 ºC, with an adsorbent dosage of 2 g/L and at composite desirability of 1. Characterization of ZINPs revealed the formation of quasi nanospheres and nanocubes with an average particle diameter of 50.9 ± 9.7, the crystallite size of 15.31 nm, a crystallinity index of 32.47%, and pore width of 75.69 – 88.59 nm. The adsorption equilibrium data modelling of ZINPs for adsorption of DRO was best described by Langmuir isotherm with the maximum monolayer coverage capacity of 7.194 mg/g. The separation factor T, indicated favourable adsorption. The adsorption kinetic data were consistent with pseudo-second-order kinetics indicating probable chemisorption.
Utility Of Support Vector Machine And Decision Tree To Identify The Prognosis Of Metformin Poisoning In The United States: Analysis Of National Poisoning Data System
Authors: Mehrpour Omid , Saeedi Farhad , Hoyte Christopher , Goss Foster , Shirazi Farshad M 
Published: February 2022

Abstract
Background: With diabetes incidence growing globally, and metformin still being the first-line for its treatment, metformin’s toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classifications methods including support vector machine (SVM) and decision tree (DT). Methods: This is a retrospective cohort study of National Poison Data System data which is the largest data repository of poisoning cases in the United States. The SVM and DT algorithms developed using training and test datasets. Results: The overall accuracy of the SVM and DT model were 85% and 89%, respectively. Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. Furthermore, we found that the accuracy of the SVM was higher than the DT model, so it could perform more efficiently to predict the outcomes.Conclusions: In order to predict the prognosis of metformin poisoning, machine learning algorithms are very effective, and may help clinicians in the process of management and follow up of the metformin poisoning cases.
Decoding an ancient biological control: The kairomonal basis of fruit fly repellence from weaver ants
Authors: Kempraj Vivek , Park Soo Jean , Cameron Donald , Taylor Phillip W. 
Published: February 2022

Abstract
Humans have used weaver ants, Oecophylla smaragdina, as biological control agents to control insect pests in orchards for many centuries. Over recent decades, the effectiveness of weaver ants as biological control agents has been attributed in part to kairomones produced by the ants, but the chemical identity of these kairomones has remained unknown. We have identified the kairomone responsible for deterrence by O. smaragdina, providing a significant advance in understanding the chemical basis of their predator/prey interactions. Olfactometer assays with extracts from weaver ants demonstrated headspace volatiles to be highly repellent to Queensland fruit fly, Bactrocera tryoni. Using electrophysiology and bioassays, we demonstrate that this repellence is induced by a single compound, 1-octanol. Of 16 compounds identified in O. smaragdina headspace, only 1-octanol evoked an electrophysiological response from B. tryoni antennae. Flies had greatly reduced oviposition and spent significantly less time in an olfactometer arm in the presence of 1-octanol or a synthetic blend of headspace volatiles containing 1-octanol than in the presence of a synthetic blend of headspace volatiles without 1-octanol, or clean air. Taken together, our results demonstrate that 1-octanol is the functional kairomone component of O. smaragdina headspace that explains repellence and oviposition deterrence, and is hence an important contributor to the effectiveness of these ants as biological control agents.
Establishment of Yan-Zhang's Staging of Digestive Tract Magnetic Compression Anastomosis in a Rat Model
Authors: Zhang Miaomiao , Lyu Xin , Zhao Guangbin , An Yingfeng , Lyu Yi , Yan Xiaopeng 
Published: February 2022

Abstract
Magnetic compression anastomosis, also known as magnamosis, is a safe and feasible method for gastrointestinal anastomosis. However, the pathological process involved in magnamosis of the digestive tract has not been investigated. This study aimed to establish the stages of digestive tract magnamosis in a rat model. Eighty-four Sprague-Dawley albino rats (200–250 g) were randomly divided into 14 groups (n = 6 per group). All rats underwent colonic magnamosis. Starting from postoperative day (POD) 1, one group of rats was sacrificed every other day to obtain the specimens. Burst pressure at the anastomotic site of each specimen was examined. Gross and histological examination of the anastomotic site was performed to establish the stages of the digestive tract magnamosis. Colonic magnamosis was successfully performed in all rats and the mean anastomosis time was 5.62 ± 0.91 min. The postoperative survival rate was 100%. The lowest anastomotic burst pressure was 78.33 ± 3.44 mmHg on POD3. The anastomotic burst pressure gradually increased and stabilized on POD21. Macroscopic and histological examination showed that the anastomotic mucosal and serosal layer did not heal on POD1. The serosal layer of the anastomosis healed by adhesion on POD3, and the mucosal layer began to heal on POD3-11 and was established by POD21. According to the anastomotic bursting pressure, gastrointestinal magnamosis can be divided into the magnetic maintenance, fragile, strengthening, and stable period, which correspond to serosal adhesion formation, serosal healing, mucosal healing, and stereotyping, respectively, based on histological changes.
Preliminary Application of Three-Dimensional Printing in Congenital Uterine Anomalies Based on Three-Dimensional Transvaginal Ultrasonographic Data
Authors: Wang Li , Chen Xu-Jiao , Liang Jia-He , Zhang Ze-Kai , Cao Tie-Sheng , Zhang Li 
Published: February 2022

Abstract
Background: The three-dimensional (3D) printing technology has remarkable potential as an auxiliary tool for representing anatomical structures, facilitating diagnosis and therapy, and enhancing training and teaching in the medical field. As the most available diagnostic tool and it is routinely used as the first approach in diagnosis of the uterine anomalies, 3D transvaginal ultrasonography (3D-TVS) has been proposed as non-invasive “gold standard” approach for these malformations due to high diagnostic accuracy. Despite holding promise of manufacturing 3D printed models based on 3D-TVS data, relevant reports about 3D-TVS derived gynecological 3D printing haven’t been reported to the best of our knowledge. We found an opportunity to explore the feasibility of building 3D printed models for the abnormal uterus based on the data acquired by 3D-TVS.Methods: The women suspected with congenital uterine anomalies (CUAs) were enrolled in the study. The diagnose of CUAs were made by 3D-TVS scanning and further confirmed under the hysteroscopy examination. One volunteer with normal uterus was enrolled as control. All subjects underwent 3D-TVS scanning for 3D printing data collection. Acquired images were stored and extracted as DICOM files, then processed by professional software to portray and model the boundary of the uterine inner and outer walls separately. After the computer 3D models were constructed, the data were saved and output as STL files for further surface restoration and smoothing. The colors of endometrium and uterine body were specified, respectively, in the print preview mode. Then the uncured photosensitive resin was cleaned and polished to obtain a smooth and transparent solid model after printed models were cooled down.Results: 3D printing models of normal uterus, incomplete septate uterus, complete septate uterus, uterus didelphys and unicornuate uterus were produced on ultrasonographic data of 3D-TVS.Conclusions: Our research and practice made the first try in modeling CUAs successfully based on ultrasonographic data entirely, verifying that it’s a feasible way to build 3D printed models of high-quality through 3D-TVS scanning.
Progression in time of dentate gyrus granule cell layer widening due to excitotoxicity occurs along in vivo LTP reinstatement and contextual fear memory recovery
Authors: Mercado Karina Hernández , Martinez-Moreno Araceli , Rodriguez-Duran Luis F , Escobar Martha L. , Zepeda Angelica 
Published: February 2022

Abstract
The dentate gyrus (DG) is the gateway of sensory information arriving from the perforant pathway (PP) to the hippocampus. The adequate integration of incoming information into the DG is paramount in the execution of hippocampal-dependent cognitive functions. An abnormal DG granule cell layer (GCL) widening due to granule cell dispersion has been reported under hyperexcitation conditions in animal models as well as in patients with mesial temporal lobe epilepsy, but also in patients with no apparent relation to epilepsy. Strikingly, it is unclear whether the presence and severity of GCL widening along time affect synaptic processing arising from the PP and alter the performance in hippocampal-mediated behaviors. To evaluate the above, we injected excitotoxic kainic acid (KA) unilaterally into the DG of mice and analyzed the evolution of GCL widening at 10 and 30 days post-injection (dpi), while analyzing if KA-induced GCL widening affected in vivo long-term potentiation (LTP) in the PP-DG pathway, as well as the performance in learning and memory through contextual fear conditioning. Our results show that at 10 dpi, when a subtle GCL widening was observed, LTP induction, as well as contextual fear memory, were impaired. However, at 30 dpi when a pronounced increase in GCL widening was found, LTP induction and contextual fear memory were already reestablished. These results highlight the plastic potential of the DG to recover some of its functions despite a major structural alteration such as abnormal GCL widening.
Comparison of Machine Learning Methods and Conventional Regression for Predicting Renal Function Recovery and Recovery Time among AKI Patients in ICU
Authors: Zhao Xiujuan , Lu Yunwei , Li Shu , Guo Fuzheng , Xue Haiyan , Jiang Lilei , Wang Zhenzhou , Zhang Chong , Xie Wenfei , Zhu Fengxue 
Published: February 2022

Abstract
Background: Acute kidney injury (AKI) is one of the most frequent complications of critical illness. Early predicted renal function recovery can improve AKI prognosis with appropriate interventions. We aimed to derivate and validate two prediction models with high prediction performance and clinical interpretability for renal function recovery and recovery time after AKI, respectively.Methods: We reviewed patients diagnosed with acute kidney disease in intensive care (MIMIC-III) database between 2001 and 2012. The conventional logistic regression and machine algorithm were used to develop prediction models. The performance measures included the area under the receiver operating characteristic curve (AU-ROC), Precision-Recall (PR) curves, and the classification matrix.Results: A total of 3,989 critically-ill AKI adult patients corresponding with Kidney Disease Improving Global Outcomes (KDIGO) with the length of stay in ICU more than 48 hours were included in our analysis cohort. Comparing the model performance, the gradient boosting machine model for predicting renal function recovery had a higher AU-ROC (0.92 [95%CI 0.89-0.94]) than the logistic regression model (0.88 [95%CI 0.84-0.92]). The ExtraTrees machine learning-based model (R2=0.2367) was developed to predict recovery time after AKI, which yielded a better result than the linear regression (R2=0.2006). Conclusions: In this cohort study, machine learning algorithms showed superior ability to predict the prognosis of AKI patients in ICU compared with the traditional logistic and linear regression models. These models may prove clinically helpful and assist clinicians in providing intervention in time, potentially leading to improved prognosis.
Electroacupuncture Treatment Ameliorates Post-Stroke Intestinal Inflammation Injury via Regulating the Balance of Treg / γδ T Cells
Authors: Wang Ya-Ling , Meng Ling Ling , Chen Yong-Lin , OuYang Ling , Wu Bu-Fan , Peng Rou , Lu Sheng-Feng , Liu Qing , Zhang Tao , Li Xiao-Xiao , Jing Xin-yue , Xu Bin , Fu Shu-ping 
Published: February 2022

Abstract
Much evidence suggests that gut immunity homeostasis plays a vital role in regulating neuroinflammatory response under ischemic stroke. Acupuncture is an effective treatment for post-stroke rehabilitation. However, little is known about the effect of acupuncture on intestinal injury after stroke. Herein, we observed the intestinal inflammatory response and barrier's impairment of the post-stroke rats to explore the mechanism of electroacupuncture (EA) against cerebral ischemia injury from the brain-gut axis. Rats were randomized to the sham operation group (SHAM), the middle cerebral artery occlusion group (MCAO), and the MCAO plus EA treatment group (MEA). Triphenyl tetrazolium chloride (TTC) staining and Longa neurologic score were performed to evaluate the outcomes after ischemic stroke. Inflammatory factors expression levels in the serum, ischemic hemisphere brain, and small intestine were detected. Additionally, the morphology change of small intestine was evaluated via analyzing villus height and smooth muscle thickness. Meanwhile, the expression of tight-junction proteins, including Zonula Occludens-1(ZO-1), Occludin and Claudin-1, was detected to evaluate the impact of EA on mucosal permeability in the small intestine. The percentage of Treg cells (CD45+CD4+Foxp3+) and γδ T cells (CD45+CD4−γδ T +) were measured to assess the effect of EA on T cells. Our data showed that EA could significantly decrease the brain infarction volume and intestine barrier injury of the ischemic stroke rats. At the same time, it effectively suppressed the post-stroke inflammation response of the ischemic brain, small intestine, and peripheral serum. More importantly, EA treatment increased the percentage of Treg cells in the small intestine while reducing the rate of γδ T cells, and ultimately increased the ratio of Treg/ γδ T cells. These results suggested that EA could ameliorate intestinal inflammation damage by regulating the Treg/ γδ T cell polarity shift and improving the intestine barrier integrity post-stroke. It may be one of the mechanisms underlying the anti-ischemic injury effects of acupuncture on stroke.
The Relationship Between Multiple Hazards and Deprivation Using Open Geospatial Data and Machine Learning
Authors: Kabiru Priscilla , Kuffer Monika , Sliuzas Richard , Vanhuysse Sabine 
Published: February 2022

Abstract
Deprived settlements, usually referred to as slums, are often located in hazardous areas. However, there have been very few studies to examine this notion. In this study, we leverage the advancements in open Geospatial data, Earth Observation (EO), and machine learning to create a multi-hazard index and transferrable disaster risk approach to be adapted in LMICs cities, with low-cost methods. Specifically, we identify multi-hazards in Nairobi's select case study area and construct an index. Then, we test the predictability of deprived settlements using the multi-hazard index in comparison to EO texture-based methods. Lastly, we survey 100 households in two deprived settlements (typical and atypical slum) in Nairobi and used the survey outcomes to validate the multi-hazard index. To test the assumption that deprived areas are dominantly located in areas with higher susceptibility to multiple hazards, we contrast morphologically identified deprived settlements to non-deprived settlements. We find that deprived settlements are generally more exposed to hazards. However, there are variations between central and peripheral settlements. In testing the predictability of deprivation using multihazards, the model performs well at classifying deprivation compared to other morphological settlement classes. Specifically, we find the model to perform better at discriminating deprivation in comparison to other morphological settlements. In contrast, the texture-based model is better at classifying all morphological settlements. Lastly, by contrasting the survey outcomes to the household interviews, we conclude that proxies used for the multi-hazard index adequately capture the hazards. However, more localized proxies can be used to improve multi-hazard index performance.
A New Precipitation Prediction Method Based on CEEMDAN-IOWA-BP Coupling
Authors: LIU Fuping , Liu Ying , Yang Chen , Lai Ruixun 
Published: February 2022

Abstract
Precipitation is the most basic part of the water cycle process. Aiming at the problem of low prediction accuracy caused by the nonlinear and unstable characteristics of the precipitation series, a new precipitation prediction method based on the CEEMDAN-IWOA-BP coupling model is proposed. This method first uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original precipitation sequence, and obtains a series of intrinsic mode function (IMF) and residual terms (Res) as inherent potential influencing factors, innovatively introduce TENT chaotic mapping and roulette algorithm to improve the Whale Optimization Algorithm (WOA), use IMFs and Res as the input of the Improve Whale Optimization Algorithm (IWOA) to optimize Back Propagation (BP) neural network prediction model, and finally superimpose the predicted values ​​as ultima result.The present method was applied to predict the annual precipitation from 1958 to 2017 in Sichuan Province.Compared with the prediction results of other models, the CEEMDAN-IWOA-BP coupled model has significantly improved prediction accuracy than the single model, and the prediction error index of the Back Propagation(BP) neural network optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is smaller, and the optimization accuracy and solving ability are significantly enhanced compared with the unimproved WOA.