bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Electrochemical Synthesis and Characterization of Zn(II) Metal Organic Framework for Photo-catalytic and Sensing Applications
Authors: Kaur Manpreet , Dhaliwal Karamjit Singh , Kaur Harpreet , Malik Ashok Kumar 
Published: March 2022

Abstract
Multidentate 1,3,5-benzenetricarboxylic acid (organic linker), Zn (II) based Zn-BTC has been synthesized via electrochemical method. Quantitative and Qualitative analyses of synthesized metal-organic framework (MOF) have been done using Fourier Transform Infrared (FTIR) Spectroscopy, Energy Dispersive X- Ray Spectroscopy (EDS) and Photoluminescence (PL). Powder X-Ray Diffraction (PXRD) and Scanning Electron Microscopy (SEM) have been used for crystallographic and morphological & topographical analyses, respectively. Crystallographic studies confirm the formation of face-centered cubic (fcc) crystal structure with good crystallinity. Photo-catalytic activity of synthesized MOF has been tested using Methylene Blue (MB) dye as a test contaminant in aqueous media under sunlight irradiation. Recorded results reveal that the synthesized MOF efficiently degrade MB dye upto 96% under sunlight exposure after 270 min. Photoluminescence studies indicate that Zn-BTC could be used as an efficient material for sensing of nitroaromatic compounds (NACs): 4-Nitroaniline (4-NA), 2-Nitroaniline (2-NA), 3- Nitroaniline (3-NA), 2,4-Dinitrotoulene (2,4-DNT), 4-Nitrotoulene (4-NT) in N,N’-Dimethylformamide (DMF) by fluorescence quenching and shows maximum quenching efficiency towards 3-NA (72.80%). Notably, the variation in luminescence intensity of 3-NA@Zn-BTC shows a linear relationship over its different concentrations from 0-1000 ppb range with KSV = 2.7 x 104 M-1 and R2 = 0.9924 with limit of detection 0.889 ppb (6.43µM) (LOD). The possible ways of luminescence quenching are successfully explained by the combination of Photoinduced Electron Transfer (PET) and Resonance Energy Transfer (RET) mechanisms. Additionally, the Density Functional Theory (DFT) calculations have been employed to support the experimental results. Zn-BTC fully demonstrates the power of a multi component MOF, which provides a feasible pathway for the design of novel material towards fast responding luminescence sensing and photocatalytic degradation of pollutants.
Presenting an Agile Supply Chain Mathematical Model for Covid 19 (Corona) Drugs Using Metaheuristic Algorithms (Case Study: Pharmaceutical Industry)
Authors: Shayannia Seyed Ahmad 
Published: March 2022

Abstract
This article focuses on the strategic issue of an agile supply chain. Different goals are considered in supply chain design. Today, the concept of sustainability in supply chains plays an important role in goal setting. In general, sustainability leads to less economic, social, and environmental risks, and attention to this type of sustainability covers many criteria in the literature. Therefore, in this study, optimization of the stability of a supply chain network has been considered. In addition, a lesser-known aspect of sustainability, the political aspect, has been added to the previous aspects. This paper assumes four levels, namely supplier, wholesaler, retailer, and customer. An optimal network structure is designed to consider economic, social and environmental sustainability and the new measure of political sustainability. The design of this supply chain is done by defining a mathematical model that has several objective functions, including economic, political, and environmental goals. These objective functions select the relationships and links between the levels in a supply chain network. For this purpose, a directional graph corresponding to a supply chain is designed. Then, with the help of an ideal multi-objective planning problem, an optimal configuration is obtained, which is a subgraph of the main network graph. The target problem has been modeled for a holding company which is a supply chain for Covid-19 drugs. Then the problem was solved using Multi-Objective Particle Swarm Optimization (MOPSO) metaheuristics algorithm and NSGAII multi-objective genetic algorithm. A comparison was carried out between these two methods and the results indicated that MOPSO performed better.
The Significance of Nonlinear Terms in the Governing Equations to Describe the Large Deformation of Thin-Walled Cylindrical Shell Under Transient Pressures
Authors: Li Yanze , Fu Jiawei , Qian Linfang , Wang Haoyu 
Published: March 2022

Abstract
The significance of the nonlinear terms in the governing equations to describe the transiently large deformation of a thin-walled cylindrical shell under impact pressures is discussed in this paper. Firstly, the first P-K stress tensor is adopted to depict the motion equations of the shell with respect to the original configuration. More nonlinear terms are retained when the first P-K stress is transformed into the symmetrical second P-K stress. Moreover, the Green strain tensor is employed in the geometrical equation to describe the nonlinearly large deformation of shell, and the curvature change is expressed to involve large deformation as well. Without considering all these nonlinear terms, the governing equation can degenerate to that in references. The circumferential displacement is kept in the motion equations, and the wall thickness change is taken into account. Dividing these nonlinear terms into two parts according to their affiliations, four groups of governing equations involving different nonlinear terms are obtained. Then, the deformation behavior of a shell subjected to a specified loading is solved using different governing equations and compared with FEM results to reveal the significance of these nonlinear terms. It is found that the nonlinear terms from geometrical equations should be kept while the nonlinear terms in equilibrium equations can be ignored.
Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy
Authors: Qi Bin , Wang Hai-Yan , Ma Xiao , Chi Yu-Feng , Gui Chun 
Published: March 2022

Abstract
Background: Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets.Methods: A dataset related to DCM was downloaded from the GEO database. R software was applied to the genetic differential analysis of DCM patients and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis was performed to establish a protein interaction network (PPI). The CIBERSORT algorithm was used to assess immune infiltration in patients with DCM versus normal controls, the hub genes were screened by the MOCDE app, and the hubs were validated in multiple datasets.Results: A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10 and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed a total of 20 significant entries, mainly focused on cytokines involved in immune response related, etc. A protein interaction network consisting of 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T cells were found by analysis of T cell-related subpathways, where INSR, HLA-B, IFITM1 and HBEGF were significantly differentially expressed. Finally, HLA-B was found to be significantly differentially expressed by multiple microarray verification.Conclusions: HLA-B is closely related to the pathogenesis of DCM, and its altered expression may be associated with the immune response involving T cells, which ultimately leads to DCM. The detailed mechanism needs to be further explored.
The Relationship between Attachment to Pets and Psychopathological Symptoms: The Mediating Effect of Adult Attachment Style
Authors: Lass-Hennemann Johanna , Schäfer Sarah K. , Sopp M. Roxanne , Michael Tanja 
Published: March 2022

Abstract
Background: Research suggests that companion animals have a beneficial effect on mental health. However, there are also studies finding null or negative relationships between pet ownership and mental health. It has been suggested that attachment to pets accounts for these diverging findings. Interestingly, there is some research suggesting that attachment to pets differs from attachment to humans with studies showing that humans with an insecure attachment style form a particularly strong attachment to their companion animals. Human attachment style also seems to play a role in mental health with secure attachment being associated with superior mental health. Thus, one may assume that attachment to humans plays a role in the relationship between attachment to pets and mental health. However, to our knowledge, only one study so far has investigated attachment to both pets and humans, while assessing mental health.Methods: Thus, in this cross-sectional online survey (N = 610) we assessed attachment to pets and humans. We further collected pet specific data as well as mental health burden in a sample of German dog owners (Mage = 33.12; 92.79% female). We hypothesized that the relationship between attachment to one’s dog and mental health is mediated by attachment to other people.Results: We found that a stronger attachment to one’s dog was associated with lower comfort with depending on or trusting in others, whereby lower comfort with depending on or trusting in others was related to higher mental health burden. Moreover, a stronger attachment to one’s dog was also related to a greater fear of being rejected and unloved (Anxiety), which was, in turn, associated with a higher mental health burden.Conclusions: Our findings suggest that a strong attachment to pets does not have a detrimental effect on mental health by itself, but that this effect is fully mediated by an insecure attachment to other people.
Elevated miR-145-5p is Associated with Chronic Obstructive Pulmonary Disease (COPD) Associated Skeletal Muscle Dysfunction and Triggers Apoptotic Cell Death in C2C12 Myotubes
Authors: Jin Jing , Li Fanyi , Fan Caihong , Wu Yu , He Chunhui 
Published: March 2022

Abstract
Skeletal muscle dysfunction is a common comorbidity of chronic obstructive pulmonary disease (COPD), and the molecular mechanisms regarding to the pathogenesis of this disease have not been elucidated. In this study, a novel miR-145-5p was significantly upregulated in the serum collected from patients with COPD-associated muscle atrophy, in contrast with the normal participants. Then, the primary myotubes was isolated and cultured in vitro, and we evidenced that silencing of miR-145-5p suppressed cell death and elongated cell survival during cell culture process. Consistently, upregulation of miR-145-5p induced cell apoptosis and restrain cell viability in the C2C12 cells, suggesting that miR-145-5p contributes to myotube cell death. Further experiments evidenced that miR-145-5p decreased the expression levels of phosphorylated PI3K (p-PI3K), Akt (p-Akt) and mTOR (p-mTOR) to inactivate the PI3K/Akt/mTOR pathway in the C2C12 cells, and this pathway was also reactivated by miR-145-5p ablation in the primary myotube cells. Finally, we proved that the protective effects of miR-145-5p ablation on the primary myotube cells were abrogated by co-treating cells with PI3K inhibitor LY294002. Taken together, we concluded that miR-145-5p promoted myotube cell death to facilitate COPD-associated muscle dysfunctions via inactivating the PI3K/Akt/mTOR pathway.
Demographic and Public Health Characteristics of COVID-19 Mortality in a State in South-South Nigeria
Authors: Owhonda Golden C , Patrick Foster A , Eze-Emiri Chidinma N , Igwe Ezinne O , Arugu Godswill Melford , Des-Wosu Ihuoma , Nwadiuto Ifeoma 
Published: March 2022

Abstract
Background
COVID-19 has accounted for approximately six million deaths globally. Several risk factors have been identified. However, population profile varies in different population groups.
Aim
The study aim is to describe the population profile of COVID-19 mortality in Rivers State, Nigeria using captured population-based health records.
Study design:
Retrospective cohort study
Methods
Using electronic State Health Records, a secondary data analysis was conducted on recorded COVID-19 mortality. Data were obtained from the Public Health Emergency Operations Centre (PHEOC) at the State Ministry of Health. Data was accessed from the PHEOC database, and it included COVID-19 related mortality. Data were collected on demographics, pre-existing comorbidity, symptoms, facility managed, patient status, treatment outcome, and dates of related events. Cohort characteristics were described using means and proportions.
Results
There were 191 COVID-19 deaths identified. The mean age was 57.08; of which 144 were male (75.4%). The 51–65-year age group had the highest mortality count (38.9%). Over 50% of the patients were hypertensive and diabetes was the second most common comorbidity (28.8%). Running nose, cough, fever and breathing difficulties were the most reported COVID-19 symptoms.
Conclusion
Data from this study shows higher mortality in men from COVID-19; and, among cases with hypertension and diabetes. Additionally, age and the presence of comorbidities may be associated with COVID-19 mortality. Future research in this area could further explain these findings.
Association Between Constipation and the Development of Asthma: A Meta-Analysis
Authors: Liu Lu , Zhang Xiangli , Jiang Zhengdong , Wang Guizuo , Wu Hua , Chen Ruilin , Zhang Yongqing , Li Manxiang , Yang Shumei 
Published: March 2022

Abstract
Background
Constipation has been hypothesized to be associated with the increased risk of wheezing or asthma. However, the relation remains a subject of debate. We conducted this meta-analysis to assess whether constipation influence the risk of wheezing/asthma.
Methods
PubMed, Embase, and Web of Science were systematically searched for studies published between 1955 and January 2022. Two reviewers independently extracted data and assessed the quality of each study. Results were pooled using fixed-effect models or random-effects models as appropriate.
Results
In total, 3 original articles with 178762 participants which met the criteria were included in this meta-analysis. Constipation was associated with an increased risk of wheezing/asthma in later life (RR = 2.14, 95% CI = 1.30 to 3.54, P < 0.01).
Conclusions
The meta-analysis suggests an association between constipation and the subsequent development of wheezing/asthma. Well-designed and highly standardized prospective studies that adequately address concerns for potential confounding factors are required to validate the risk identified in our current meta-analysis.
A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea Hypopnea Syndrome
Authors: Ye Yanqing , Yan Zelin , Huang Yuanshou , Li Li , Wang Shiming , Huang Xiaoxing , Zhou Jingmeng , Chen Liyi , Ou Chun-Quan , Chen Huaihong 
Published: March 2022

Abstract
Background: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSAHS patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSAHS patients. Methods: We performed a retrospective cohort study including 1,920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSAHS. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSAHS. Moreover, we compared the performance of our model with two most commonly used screening tools, Epworth sleepiness scale (ESS) and Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires.Results: Severe OSAHS was associated with male, BMI≥ 27 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥ 9.5×109/L, hemoglobin ≥ 175g/L, triglycerides ≥ 1.7 mmol/L. The AUC of the final model was 0.75 (95% CI: 0.67-0.82), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 71% and 68%. Among patients having the information of ESS or SBQ, the AUC of our model was statistically significantly greater than that of ESS (0.74 vs 0.58, P=0.006) and not less than that of SBQ (0.78 vs 0.66, P=0.073). Conclusion: Based on common clinical examination on admission, we develop a novel model and a nomogram for identifying severe OSAHS from inpatient with suspected OSAHS, which provides physicians with a visual and easy-to-use tool for screening severe OSAHS.
Comprehensive assessment of depression risk and its behavioral determinants among adolescents: a multi-country study
Authors: Sultana Sabera , Rahman Md. Mizanur , Hashizume Masahiro 
Published: March 2022

Abstract
Background: Mental illness has become a widespread public health concern internationally. This study aimed to generate a depression risk index for adolescents. Furthermore, we developed risk indexes for potential lifestyle and social risk factors of depression among adolescents. In addition, we investigated the country-specific prevalence for each risk index. Finally, we conducted comprehensive assessment of the associations between depression risk and lifestyle and social risk factors.Methods: We used the most recent data available from 20 nationally representative Global School-based Student Health Surveys. Our analytical sample included 51,597 adolescents. The outcome of interest was depression risk, which considered feelings of anxiety, loneliness, and suicidal attempts or tendencies. We developed four lifestyle risk indexes (dietary habits, physical activity, sedentary behavior, and tobacco use) and three social risk indexes (exposure to school violence, interactions with parents, and interactions with friends) which were considered as exposure variables in this study. Multilevel logistic regression models were used to estimate adjusted odds ratios (ORs) with 95% confidence intervals (CIs).Results: In total, 26.4% of the participants had a risk of depression. Increased odds of depression risk were found among participants with high risk index values for unhealthy dietary habits (OR, 1.15; 95% CI, 1.07–1.23), unhealthy sedentary behavior (OR, 1.53; 95% CI, 1.43–1.63), tobacco use (OR, 1.57; 95% CI, 1.41–1.76), exposure to school violence (OR, 3.06; 95% CI, 2.88–3.26), insufficient interactions with parents (OR, 2.09; 95% CI, 1.92–2.28), and insufficient interactions with friends (OR, 1.82; 95% CI, 1.69–1.96) compared with participants with low risk index values. However, there were no significant associations between physical inactivity and risk of depression among adolescent boys or girls. Conclusions: High prevalence rates for depression risk, as well as unhealthy lifestyle factors and social behaviors among adolescents were found in every country, and higher risk index values were associated with increased risk of depression.
Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data
Authors: ALWESHAH MOHAMMED , Jebril Hadeel , Alkhalaileh Saleh , Kassaymeh Sofian , AlZubi Tareq Mahmod 
Published: March 2022

Abstract
Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.
Stressfulness of the Design Influences Consistency of Cognitive Measures and Their Correlation With Animal Personality Traits in Wild Mice (Mus Musculus)
Authors: Delacoux Mathilde , Guenther Anja 
Published: March 2022

Abstract
Individual variation in cognition is being increasingly recognized as an important evolutionary force but contradictory results so far hamper a general understanding of consistency and association with other behaviors. Partly, this might be caused by external factors imposed by the design. Stress, for example, is known to influence cognition, with mild stress improving learning abilities, while strong or chronical stress impairs them. Also, there might be intraspecific variation in how stressful a given situation is perceived. We investigated two personality traits (stress coping and voluntary exploration), spatial learning with 2 mazes and problem-solving in low- and high-stress tests with a group of 30 female wild mice. For each test, perceived stress was assessed by measuring body temperature change with infrared thermography. None of the learning measures were significantly repeatable between the 2 stress conditions, indicating that the stress level impacts learning. We found correlations between learning and personality traits; however, they differed between the 2 stress conditions and between the cognitive tasks suggesting that different mechanisms underlie these processes. These findings could explain some of the contradicting findings in the literature and argue for very careful design of cognitive test setups to draw evolutionary implications.