bluuebunny/crossref_metadata_2025_split huggingface dataset:
TREATMENT OUTCOME AND ITS PREDICTORS AMONG DIABETIC PATIENTS ATTENDING AT SELECTED HOSPITALS OF SOUTHERN ETHIOPIA
Authors: Gebre Bereket Beyene , Hoyiso Dawit , Woldu Kinfe 
Published: July 2023

AbstractBackgroundDiabetes is one of the most serious health crises of the twenty-first century, with the World Health Organization (WHO) estimating that hyperglycemia is the third leading cause of early death globally, following high blood pressure and cigarette use. It is an important cause of blindness, kidney failure, lower limb amputation and other long-term consequences that impact significantly on quality of life. People get sick, die, and health care costs go up as a result.ObjectivesDiabetes mellitus treatment outcome and determinants in patients undergoing diabetes treatments in selected hospitals in southern Ethiopia, 2021.MethodsA source population included all diabetic patients who attended chosen hospitals in southern Ethiopia. A cross-sectional study with an institutional focus was conducted in a few hospitals in southern Ethiopia. A pre-tested questionnaire was used to collect information about the patient. Following data cleaning and error checking, the data was loaded into Epi-data to eliminate errors before being exported to SPSS for analysis. The descriptive data were presented using tables and graphics. The result variable was evaluated using a logistic regression model to uncover predictors once the underlying assumptions of the regression analysis were verified. All independent variables with p0.25 were considered for multivariate analysis. The level of significance was then set at p0.05, and the final model was AOR with 95% CL.Result277 (65.6%) of the whole sample had positive treatment outcomes. The existence of complication AOR 95%CI; 0.425 (0.217,.832), and elevated fasting blood glucose AOR 95%CI; 0.080 (0.034, 0.188) were revealed to be independent predictors. Given the low size of treatment outcome, providing health information regarding sticking to prescribed medicine and monitoring fasting blood glucose level will result in a positive clinical outcome.
Machine learning illuminates how diet influences the evolution of yeast galactose metabolism
Authors: Harrison Marie-Claire , Ubbelohde Emily J. , LaBella Abigail L. , Opulente Dana A. , Wolters John F. , Zhou Xiaofan , Shen Xing-Xing , Groenewald Marizeth , Hittinger Chris Todd , Rokas Antonis 
Published: July 2023

AbstractHow genomic differences contribute to phenotypic differences across species is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. In recent years, machine learning has been successfully used in diverse analyses of biological big data. Using a random forest classification algorithm trained on these genomic, metabolic, and/or environmental data, we predicted growth on several carbon sources and conditions with high accuracy from presence/absence patterns of genes and of growth in other conditions. Known structural genes involved in assimilation of these sources were important features contributing to prediction accuracy, whereas isolation environmental data were poor predictors. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.6%) or growth data in 120 other conditions (83.3%) but not from isolation environment data (65.7%). When we combined genomic and growth data, we noted that prediction accuracy was even higher (93.4%) and that, after theGALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol. These data raised the hypothesis that several species in two orders, Serinales and Pichiales (containingCandida aurisand the genusOgataea, respectively), have an alternative galactose utilization pathway because they lack theGALgenes. Growth and biochemical assays of several of these species confirmed that they utilize galactose through an oxidoreductive D-galactose pathway, rather than the canonicalGALpathway. We conclude that machine learning is a powerful tool for investigating the evolution of the yeast genotype-phenotype map and that it can help uncover novel biology, even in well-studied traits.
Distinct disease-modifying therapies are associated with different blood immune cell profiles in people with relapsing-remitting multiple sclerosis
Authors: Canto-Gomes João , Boleixa Daniela , Teixeira Catarina , da Silva Ana Martins , González-Suárez Inés , Cerqueira João , Correia-Neves Margarida , Nobrega Claudia 
Published: July 2023

AbstractDisease modifying therapies (DMTs) used for treating people with relapsing-remitting multiple sclerosis (pwRRMS) target the immune system by different mechanisms of action. However, there is a lack of a comprehensive comparison of their effects on the immune system. Herein, we evaluated the numbers of circulating B cells, CD4+and CD8+T cells, regulatory T cells (Tregs), natural killer (NK) cells and NKT cells, and their subsets, in pwRRMS who were treatment-naïve or treated with different DMTs. Compared to treatment-naïve pwRRMS, common and divergent effects on immune system cells were observed on pwRRMS treated with different DMTs, with no consistent pattern across all therapies in any of the cell populations analysed. PwRRMS treated with fingolimod, dimethyl fumarate (DMF), or alemtuzumab have reduced numbers of CD4+and CD8+T cells, as well as Treg subsets, with fingolimod causing the most pronounced decrease in T cell subsets. In contrast, teriflunomide and interferon (IFN) β have minimal impact on T cells, and natalizumab marginally increases the number of memory T cells in the blood. The effect of DMTs on the B cell, NKT and NK cell subsets is highly variable with alemtuzumab inducing a strong increase in the number of the most immature NK cells and its subsets. This study highlights the absence of a consistent pattern of the impact of various DMTs on immune system cells, with variations in both direction and magnitude of effect thus reenforcing the notion that distinct immune cell subsets are potential players in MS pathophysiology and/or DMT efficacy.
Targeting pioneer transcription factor Ascl1 to promote optic nerve regeneration
Authors: Dong Bryan C , Luo Ximei , Qi Cheng , Qian Jiang , Qian Cheng , Zhou Feng-Quan 
Published: July 2023

ABSTRACTIn adult mammalian central nervous system (CNS) neurons, axon regeneration after injury remains limited due to unfavorable gene regulatory programs. Factors enabling comprehensive epigenetic and transcriptional transitions, for instance, pivotal transcription factors that mediate neurogenesis and morphogenesis may be sufficient to promote CNS axon regeneration. Based on the analyses of multiple public whole-genome RNA and chromatin accessibility sequencing dataset of mouse retina development, as well as previous functional studies on the regeneration-capable dorsal root ganglion neurons, we hypothesize that the overexpression of pioneer transcription factor Achaete-Scute homolog 1 (Ascl1) would promote axon regeneration in the adult mammalian CNS neurons. We employed the optic nerve crush in mice, a common model for studying CNS axon regeneration, neuron survival and glaucoma, to investigate the effect of Ascl1 overexpression on the post-injury optic nerve regeneration. We found that Ascl1 could sufficiently promote regenerated axons past the crush site and significantly preserve the survival of retinal ganglion cells. Mechanistically, we revealed that effects of Ascl1 was mediated by known pro-regeneration factor Sox11 but not others. Together, our study established an effective workflow combined with the integrated computational inference and experimental validation for discovering functionally important target for promoting CNS neuron axon regeneration and survival.
The potential ofPseudomonas fluorescensSBW25 to produce viscosin enhances wheat root colonization and shapes root-associated microbial communities in a plant genotype dependent manner in soil systems
Authors: Guan Ying , Bak Frederik , Hennessy Rosanna Catherine , Herms Courtney Horn , Elberg Christine Lorenzen , Dresbøll Dorte Bodin , Winding Anne , Sapkota Rumakanta , Nicolaisen Mette Haubjerg 
Published: July 2023

AbstractMicroorganisms interact with plant roots through colonization of the root surface i.e. the rhizoplane or the surrounding soil i.e. the rhizosphere. Beneficial rhizosphere bacteria such asPseudomonasspp. can promote plant growth and protect against pathogens by producing a range of bioactive compounds, including specialized metabolites like cyclic lipopeptides (CLPs) known for their biosurfactant and antimicrobial activities. However, the role of CLPs in natural soil systems during bacteria-plant interactions is underexplored. Here,Pseudomonas fluorescensSBW25, producing the CLP viscosin, was used to study the impact of viscosin on bacterial root colonization and microbiome assembly in two cultivars of winter wheat (Heerup and Sheriff). We inoculated germinated wheat seeds with SBW25 wild-type or a viscosin-deficient mutant, and grew the plants in agricultural soil. After two weeks, enhanced root colonization of SBW25 wild-type compared to the viscosin-deficient mutant was observed, while no differences were observed between wheat cultivars. In contrast, the impact on root-associated microbial community structure was plant genotype specific, and SBW25 wild-type specifically reduced the relative abundance of an unclassified oomycete andPhytophthorain Sheriff and Heerup, respectively. This study provides new insights into the natural role of viscosin and specifically highlights the importance of viscosin in wheat root colonization under natural soil conditions and in shaping the root microbial communities associated with different wheat cultivars. Further, it pinpoints the significance of microbial microdiversity, plant genotype and microbe-microbe interactions when studying colonization of plant roots.
Identifying risk genes for embryo aneuploidy using ultra-low coverage whole-genome sequencing
Authors: Sun Siqi , Aboelenain Mansour , Ariad Daniel , Haywood Mary E. , Wageman Charles R. , Duke Marlena , Bag Aishee , Viotti Manuel , Katz-Jaffe Mandy , McCoy Rajiv C. , Schindler Karen , Xing Jinchuan 
Published: July 2023

AbstractBackgroundAneuploidy, the state of a cell containing extra or missing chromosomes, frequently arises during human meiosis and is the primary cause of early miscarriage and maternal age-related in vitro fertilization (IVF) failure. IVF patients exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using ultra-low coverage whole-genome sequencing (ulc-WGS) is a standard test for identifying and selecting IVF-derived embryos with a normal chromosome complement. The wealth of embryo aneuploidy data and ulc-WGS data from PGT-A has potential for discovering variants in paternal genomes that are associated with aneuploidy risk in their embryos.MethodsUsing ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in parental genomes. We then used the imputed variants and aneuploidy calls from the embryos to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system.ResultsWe identified one locus on chromosome 3 that is significantly associated with maternal meiotic aneuploidy risk. One candidate gene,CCDC66,encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice.ConclusionsOur work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that can be leveraged for similar association studies using ulc-WGS data.
Reactivation of herpesvirus type-6 and IgA/IgM-mediated responses to activin-A underpin Long COVID, including affective symptoms and chronic fatigue syndrome
Authors: Vojdani Aristo , Almulla Abbas F. , Zhou Bo , Al-Hakeim Hussein K. , Maes Michael 
Published: July 2023

AbstractBackgroundPersistent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), reactivation of dormant viruses, and immune-oxidative responses are involved in Long COVID.ObjectivesTo investigate whether Long COVID and depressive, anxiety and chronic fatigue syndrome (CFS) symptoms, are associated with IgA/IgM/IgG to SARS-CoV-2, human Herpesvirus type 6 (HHV-6), Epstein-Barr Virus (EBV), and immune-oxidative biomarkers.MethodsWe examined 90 Long COVID patients and 90 healthy controls. We measured serum IgA/IgM/IgG against HHV-6 and EBV and their deoxyuridine 5′-triphosphate nucleotidohydrolase (duTPase), SARS-CoV-2, and activin-A, C-reactive protein (CRP), advanced oxidation protein products (AOPP), and insulin resistance (HOMA2-IR).ResultsLong COVID patients showed significant elevations in IgG/IgM-SARS-CoV-2, IgG/IgM-HHV-6 and HHV-6-duTPase, IgA/IgM-activin-A, CRP, AOPP, and HOMA2-IR. Neural network analysis yielded a highly significant predictive accuracy of 80.6% for the Long COVID diagnosis (sensitivity: 78.9%, specificity: 81.8%, area under the ROC curve=0.876); the topmost predictors were: IGA-activin-A, IgG-HHV-6, IgM-HHV-6-duTPase, IgG-SARS-CoV-2, and IgM-HHV-6 (all positively) and a factor extracted from all IgA levels to all viral antigens (inversely). The top-5 predictors of affective symptoms due to Long COVID were: IgM-HHV-6-duTPase, IgG-HHV-6, CRP, education, IgA-activin-A (predictive accuracy of r=0.636). The top-5 predictors of CFS due to Long COVID were in descending order: CRP, IgG-HHV-6-duTPase, IgM-activin-A, IgM-SARS-CoV-2, and IgA-activin-A (predictive accuracy: r=0.709).ConclusionReactivation of HHV-6, SARS-CoV-2 persistence, and autoimmune reactions to activin-A combined with activated immune-oxidative pathways play a major role in the pathophysiology of Long COVID as well as the severity of affective symptoms and CFS due to Long COVID.
Elucidating the Assembly of Gas Vesicles by Systematic Protein-Protein Interaction Analysis
Authors: Iburg Manuel , Anderson Andrew P. , Wong Vivian T. , Anton Erica D. , He Art , Lu George J. 
Published: July 2023

ABSTRACTGas vesicles (GVs) are gas-filled microbial organelles formed by unique 3-nm thick, amphipathic, force-bearing protein shells, which can withstand multiple atmospheric pressures and maintain a physically stable air bubble with megapascal surface tension. However, the molecular process to assemble this shell remains elusive: while 6-8 assembly factor proteins were identified as essential, none of them have a defined function. As one of the first steps to elucidate the assembly mechanism, we devise a high-throughputin vivoassay to determine the interactions of all 11 proteins in a GV operon. Complete or partial deletions of the operon establish the interdependence relationship of the interaction on the background GV proteins with additional information on assembly tolerance and cellular burden. Clusters of GV protein interactions are revealed, which establishes the plausible protein complexes important for the assembly process of these protein organelles. We anticipate our findings will set the stage for solving the molecular mechanism of GV assembly and designing GVs that efficiently assemble in heterologous hosts during biomedical applications.
Temporal changes in the positivity rate of common enteric viruses among paediatric admissions in coastal Kenya, in the period spanning the COVID-19 pandemic, 2019-2022
Authors: Lambisia Arnold W. , Murunga Nickson , Mutunga Martin , Cheruiyot Robinson , Maina Grace , Makori Timothy O. , Nokes D. James , Agoti Charles N. 
Published: July 2023

AbstractBackgroundThe non-pharmaceutical interventions (NPIs) implemented to curb the spread of SARS-CoV-2 early in the COVID-19 pandemic years, disrupted the activity of other respiratory viruses. There is limited data from low-and-middle income countries (LMICs) to determine whether COVID-19 NPIs also impacted the epidemiology of enteric viruses. We investigated the changes in infection patterns of common enteric viruses among hospitalised children who presented with diarrhoea to a referral hospital in coastal Kenya, in the period spanning the COVID-19 pandemic.MethodsA total of 870 stool samples from children under 13 years of age admitted to Kilifi County Hospital between January 2019, and December 2022 were screened for rotavirus group A (RVA), norovirus genogroup II (GII), astrovirus, sapovirus, and adenovirus type F40/41 using real-time reverse-transcription polymerase chain reaction. The proportions positive across the four years were compared using the chi-squared test statistic.ResultsOne or more of the five virus targets were detected in 282 (32.4%) cases. A reduction in the positivity rate of RVA cases was observed from 2019 (12.1%, 95% confidence interval (CI) 8.7% - 16.2%) to 2020 (1.7%, 95% CI 0.2% – 6.0%; p< 0.001). However, in the 2022, RVA positivity rate rebounded to 23.5% (95% CI 18.2% - 29.4%). For norovirus GII, the positivity rate fluctuated over the four years with its highest positivity rate observed in 2020 (16.2%; 95% C.I, 10.0% – 24.1%). No astrovirus cases were detected in 2020 and 2021, but the positivity rate in 2022 was similar to that in 2019 (3.1% (95% CI 1.5% - 5.7%) vs 3.3% (95% CI 1.4% – 6.5%)). A higher case fatality rate was observed in 2021 (9.0%) compared to the 2019 (3.2%), 2020 (6.8%) and 2022 (2.1%) (p <0.001).ConclusionOur study finds that in 2020 the transmission of common enteric viruses, especially RVA and astrovirus, in Kilifi Kenya may have been disrupted due to the COVID-19 NPIs. After 2020, local enteric virus transmission patterns appeared to return to pre-pandemic levels coinciding with the removal of most of the government COVID-19 NPIs.
The impact of team support, financial incentives and public sector motivation on employee motivation: an empirical study of the public sector in Vietnam
Authors: Nguyen Hoa Dinh , Chau Thi Ngoc , Huynh Quyen Vo Thuc 
Published: July 2023

PurposeThis study aims to investigate the relationship of employee motivation to team support, financial incentives and public sector motivation in various agencies under the Binh Dinh People's Committee in Vietnam. These agencies fulfil state management functions in many fields, such as investment, finance, construction, sports, culture and tourism.Design/methodology/approachThis study applies the quantitative method to test team support, financial incentives and public service motivation (PSM) in relation to employee motivation in the public sector. The data are analysed using covariance-based structural equation modelling (SEM), with a sample size of 263 employees who work at provincial government agencies.FindingsThe study results show that team support, financial incentives and PSM have a positive influence on employee motivation in the public sector.Originality/valueThe findings provide theoretical evidence that team support, financial incentives and PSM are key predictors of employee motivation in the public sector in the context of an emerging economy. Consequently, the authors propose that managers in the public sector should motivate employees by communicating with employees about the employees' roles in improving the local people's lives to stimulate the PSM of employees. In addition, managers should always provide constructive feedback that recognises employees' achievements and pay bonuses based on job performance and successful projects to improve public service.
Variational quantum simulation of partial differential equations: applications in colloidal transport
Authors: Leong Fong Yew , Koh Dax Enshan , Ewe Wei-Bin , Kong Jian Feng 
Published: July 2023

Purpose
This study aims to assess the use of variational quantum imaginary time evolution for solving partial differential equations using real-amplitude ansätze with full circular entangling layers. A graphical mapping technique for encoding impulse functions is also proposed.

Design/methodology/approach
The Smoluchowski equation, including the Derjaguin–Landau–Verwey–Overbeek potential energy, is solved to simulate colloidal deposition on a planar wall. The performance of different types of entangling layers and over-parameterization is evaluated.

Findings
Colloidal transport can be modelled adequately with variational quantum simulations. Full circular entangling layers with real-amplitude ansätze lead to higher-fidelity solutions. In most cases, the proposed graphical mapping technique requires only a single bit-flip with a parametric gate. Over-parameterization is necessary to satisfy certain physical boundary conditions, and higher-order time-stepping reduces norm errors.

Practical implications
Variational quantum simulation can solve partial differential equations using near-term quantum devices. The proposed graphical mapping technique could potentially aid quantum simulations for certain applications.

Originality/value
This study shows a concrete application of variational quantum simulation methods in solving practically relevant partial differential equations. It also provides insight into the performance of different types of entangling layers and over-parameterization. The proposed graphical mapping technique could be valuable for quantum simulation implementations. The findings contribute to the growing body of research on using variational quantum simulations for solving partial differential equations.
Does a farmer's knowledge of minimum support price (MSP) affect the farm-gate price? Evidence from India
Authors: Korekallu Srinivasa Aditya , Praveen K.V. , Surendran Padmaja Subash , Nithyashree M.L. , Jha Girish K. 
Published: July 2023

PurposeThis paper examines whether farmers' knowledge of the minimum support prices (MSPs) affects farm-gate prices. MSP is the minimum guaranteed price for agricultural commodities announced by the Government of India for 24 commodities. Most farmers in India prefer to sell their produce at the farm-gate due to a small marketable surplus and hence do not directly benefit from MSP. The authors test the common argument in the political discourse that if farmers have knowledge of MSP, then they can bargain with traders during the farm-gate transaction and demand a better price close to MSP.Design/methodology/approachThe authors use matching methods to examine the impact of knowledge of MSP on farm-gate prices.FindingsUsing nationally representative data, the authors show that there is no empirical evidence that the knowledge of MSP of the crops leads to higher bargaining power and better farm-gate prices.Practical implicationsPrice information (MSP in this case) alone cannot improve the bargaining power of farmers and result in a better price realization. As a safety net, MSP fails in the absence of procurement of products by the government. This also raises the question of the equitability of the price support system in India and calls for a rethink of the MSP policy.Originality/valueThis study is the first of its kind to examine the anchoring effect of knowledge of MSP on farm-gate prices using a nationally representative dataset.