bluuebunny/crossref_metadata_2025_split huggingface dataset:
Urban-rural disparities in immunization coverage among children aged 12–23 months in Ethiopia: multivariate decomposition analysis
Authors: Asresie Melash Belachew , Fekadu Gedefaw Abeje , Dagnew Gizachew Worku 
Published: September 2023

Abstract
Background
Immunization is one of the most cost-effective public health interventions for improving children’s health and survival. In Ethiopia, low immunization coverage and disparity across residences are major public health problems. However, the factors that contributed to the urban-rural disparity have not been thoroughly investigated. Therefore, the objective of this study was to examine the change and contributing factors in full immunization coverage across geographic locations (urban-rural) in Ethiopia.

Methods
We analyzed data on children aged 12 to 23 months obtained from the 2019 mini-Ethiopian demographic and health survey. A total of 996 weighted samples (299 in urban and 697 in rural areas) were included in the analysis. A multivariate decomposition analysis technique was used to determine the disparity and identify factors that contribute to the disparity across geographical locations. Statistical significance was defined at a 95% confidence interval with a p-value of less than 0.05.

Results
The percentage of children aged 12–23 months who received full immunization increased from 36.84% (95% CI:31.59, 42.41) in rural areas to 64.59% (95% CI:47.10, 78.89) in urban areas. The decomposition analysis showed that the observed urban-rural disparity was attributed to a change in the effect of population characteristics (coefficient) across residences. Specifically, receiving 1–3 (β = 0.0895, 95% CI: 0.0241, 0.1550) and 4 or more (β = 0.1212, 95% CI: 0.0224, 0.2199) antenatal care visits, delivering at a health facility (β = 0.1350, 95% CI: 0.0227, 0.2472), and the source of information about immunization status from vaccination cards (β = 0.2666, 95% CI:0.1763, 0.3569) significantly contributed to the widening urban-rural disparity. On the other hand, being of high wealth status (β=-0.141, 95% CI: -0.1945, -0.0876), receiving postnatal care (β=-0.0697, 95% CI: -0.1344, -0.0051), and having four or more living children (β=-0.1774, 95% CI: -0.2971, -0.0577) significantly contributed to narrowing the urban-rural disparity.

Conclusions
There was a significant urban-rural disparity in immunization coverage in Ethiopia, with urban children more likely to complete immunization. The change in the composition of population characteristics was not significant for the observed disparity. The observed disparity in full immunization coverage was mainly driven by the coefficients related to maternal healthcare utilization, household wealth status, the number of living children, and the source of immunization information. Therefore, strengthening maternal health services utilization, encouraging mothers to maintain their children’s immunization records, and addressing economic inequality, particularly in rural areas, may narrow the urban-rural disparity and enhance immunization coverage nationwide.
Exploring the impact of the national tender system on the use of costly drugs treating rheumatoid arthritis patients in ten rheumatology centers in Norway (2010–2019)
Authors: Brkic Alen , Diamantopoulos Andreas P , Hoff Mari , Haavardsholm Espen Andre , Fevang Bjørg Tilde Svanes , Brekke Lene Kristin , Loli Liz , Zettel Camilla , Bakland Gunnstein , Mielnik Pawel , Haugeberg Glenn 
Published: September 2023

Abstract
Background
Biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) are highly effective in treating rheumatoid arthritis (RA), albeit high drug cost has restricted their use in many countries. As a countermeasure, Norway implemented pharmaceutical tendering as a cost-reducing strategy. The aim of this study was to assess the annual proportion of different b/tsDMARDs registered to treat RA patients under the influence of a Norwegian pharmaceutical tendering between 2010 and 2019.

Method
The data is collected from ten Norwegian outpatient centers. The included patients are categorized as naïve, non-naïve, and current b/tsDMARD users. 13 individual b/tsDMARDs are assessed and compared with the tender rankings from each year. Overview of subcutaneous (sc) with per oral vs. intravenous (iv) and biosimilars vs. non-biosimilar are also described.

Result
The tender-winning b/tsDMARD was the most or second most used drug in nine out of ten years for naïve users, seven for non-naïve users, and twice for current users. The average sum of the highest and second highest proportion among naïve, non-naïve, and current b/tsDMARD users were 75%, 53%, and 50% during the ten years, respectively. The tender-winning drug was iv in eight out of ten years. However, the average total proportion of sc and per oral b/tsDMARDs was about 70% for naïve b/tsDMARD users, 50% for non-naïve b/tsDMARD users, and 60% for current b/tsDMARD users. The main contributors to sc and per oral b/tsDMARD were etanercept (reference and biosimilar) and certolizumab pegol. The main contributors to iv b/tsDMARD were rituximab reference and infliximab biosimilar. Despite low-ranking offers, rituximab reference (offered as a second-line drug) often achieved a high proportion among non-naïve and current b/tsDMARD users. After the introduction of biosimilars, their average proportion was about 40%, 40%, and 20% for naïve, non-naïve, and current b/tsDMARD users, respectively.

Conclusion
Based on observed data, a higher tender rank was associated with a higher proportion among naïve and non-naïve b/tsDMARD users. However, in most cases, sc b/tsDMARDs achieved a higher proportion with lower tender ranks than iv b/tsDMARDs with higher tender ranks.
The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews
Authors: Hair Kaitlyn , Bahor Zsanett , Macleod Malcolm , Liao Jing , Sena Emily S. 
Published: September 2023

Abstract
Background
Researchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews.

Methods
We evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1845–79,880 citations). We compared the performance of ASySD with EndNote’s automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM).

Results
ASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of > 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset.

Conclusions
For duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.
Re: Molecular testing to deliver personalised chemotherapy recommendations
Authors: Gibbs Peter , Hong Wei , Tie Jeanne 
Published: September 2023

Abstract
Background
There is an increasing focus over time on the discovery and validation of biomarkers in cancer medicine, which can inform the identification of patients that are most likely to benefit from treatment, which therapy is most likely to be effective, and treatments that may not be safe.

Body
Creating the necessary evidence base for biomarker-informed management is a different challenge to developing a new therapy, and many biomarkers have been adopted into routine clinical practice without phase III randomised studies where the primary endpoint was to evaluate the direct impact of a biomarker-informed approach. This has generated a robust discussion in the research and clinical community regarding the most appropriate trial methodologies for biomarker validation, and the level of evidence required to support the incorporation of individual biomarker-driven approaches as a standard of care. This ongoing debate is key to optimising clinical trial design and ultimately delivering the best possible care to patients in an environment increasingly focused on personalised and patient-focused management.

Conclusion
Ongoing deliberation as to the optimal design of biomarker-driven clinical trials is critical to informing future clinical trial design and will ultimately greatly benefit patients and the clinicians that care for them.
Detection of diabetic patients in people with normal fasting glucose using machine learning
Authors: Lv Kun , Cui Chunmei , Fan Rui , Zha Xiaojuan , Wang Pengyu , Zhang Jun , Zhang Lina , Ke Jing , Zhao Dong , Cui Qinghua , Yang Liming 
Published: September 2023

Abstract
Background
Diabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG.

Methods
The physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors.

Results
The prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application (http://www.cuilab.cn/dring).

Conclusions
DRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.
Erythritol alters gene transcriptome signatures, cell growth, and biofilm formation in Staphylococcus pseudintermedius
Authors: Fujii Tadashi , Tochio Takumi , Nishifuji Koji 
Published: September 2023

Abstract
Background
Erythritol was found to inhibit the growth of microorganisms. The present study aimed to demonstrate the growth inhibition of Staphylococcus pseudintermedius by erythritol and to define the changes in gene transcription signatures induced by erythritol. Changes in the gene transcription profiles were analysed by RNA sequencing and quantitative reverse transcription PCR. Gene ontology analysis was performed to assign functional descriptions to the genes.

Results
Erythritol inhibited S. pseudintermedius growth in a dose-dependent manner. We then performed a transcriptome analysis of S. pseudintermedius with and without 5% (w/w) erythritol exposure to validate the mechanism of growth inhibition. We revealed that erythritol induced up-regulation of three genes (ptsG, ppdK, and ppdkR) that are related to the phosphoenolpyruvate-dependent sugar phosphotransferase system (PTS). Glucose supplementation restored the up-regulation of the PTS-related genes in response to erythritol. In addition, erythritol down-regulated eleven genes that are located in a single pur-operon and inhibited biofilm formation of S. pseudintermedius.

Conclusions
These findings indicated that erythritol antagonistically inhibits PTS-mediated glucose uptake, thereby exerting a growth inhibitory effect on S. pseudintermedius. Moreover, erythritol inhibits the ‘de novo’ IMP biosynthetic pathway that may contribute to biofilm synthesis in S. pseudintermedius.
Smoking, alcohol consumption and risk of Dupuytren’s disease: a Mendelian randomization study
Authors: Wang Zifeng , Wang Zhenyu , Yan Zijian , Xu Zhujie , Gao Aiguo 
Published: September 2023

AbstractBackgroundThe correlation between smoking and alcohol consumption and the development of Dupuytren’s disease (DD) has been acknowledged. However, the definitive causal relationship between these two factors and DD remains elusive. In order to establish a causal connection, we employed the two-sample Mendelian randomization method to evaluate the relationship between smoking and alcohol consumption and DD.MethodsBased on publicly available genome-wide association studies (GWAS), two-sample univariate MR analyses were performed to assess the causal effects of drinks per week, cigarettes per day, smoking initiation, age of initiation, and smoking cessation on DD. We used inverse variance weighted (IVW) to generate the primary results for the MR analysis. Furthermore, we performed sensitivity MR analyses based on various methods to assess the robustness of estimations. Bidirectional MR analyses were used to study the interaction between smoking and alcohol consumption. Multivariate MR analyses were used to obtain independent causal effects of smoking or drinking on DD.ResultsOur two-sample MR, which was predominately based on IVW, revealed a causal relationship between drinks per week and DD (OR = 2.948, 95%CI: 1.746–4.975,P = 5.16E-05). In addition, there is no causal association between cigarettes per day, smoking initiation, age of initiation, smoking cessation and DD. Similar conclusions were reached by other MR methods. The results of the bidirectional MR analyses showed that the causal relationships between age of initiation and drinks per week were robust and significant. Multivariate MR results indicated that the causal effect of alcohol consumption on DD was independent of smoking.ConclusionOur Mendelian Randomization study indicated that there is a causality between drinking alcohol and DD, but no such causality was found between smoking and DD. This is the first study to prove that drinking alcohol could cause DD. This could help people who are trying to prevent DD from happening in the first place.
Endogenous estradiol contributes to vascular endothelial dysfunction in premenopausal women with type 1 diabetes
Authors: Simon Abigayle B. , Derella Cassandra C. , Blackburn Marsha , Thomas Jeffrey , Layman Lawrence C. , Nicholson Matthew S. , Waller Jennifer , Elmarakby Ahmed , Saad Karim M. , Harris Ryan A. 
Published: September 2023

Abstract
Background
Endogenous estrogen is cardio-protective in healthy premenopausal women. Despite this favorable action of estrogen, animal models depict a detrimental effect of estradiol on vascular function in the presence of diabetes. The present study sought to determine the role of endogenous estradiol on endothelial function in women with type 1 diabetes.

Method
32 women with type 1 diabetes (HbA1c = 8.6 ± 1.7%) and 25 apparently healthy women (HbA1c = 5.2 ± 0.3%) participated. Flow-mediated dilation (FMD), a bioassay of nitric-oxide bioavailability and endothelial function was performed during menses (M) and the late follicular (LF) phase of the menstrual cycle to represent low and high concentrations of estrogen, respectively. In addition, a venous blood sample was collected at each visit to determine circulating concentrations of estradiol, thiobarbituric acid reactive substances (TBARS), and nitrate/nitrite (NOx), biomarkers of oxidative stress and nitric oxide, respectively. Data were collected in (1) 9 additional women with type 1 diabetes using oral hormonal birth control (HBC) (HbA1c = 8.3 ± 2.1%) during the placebo pill week and second active pill week, and (2) a subgroup of 9 demographically matched women with type 1 diabetes not using HBC (HbA1c = 8.9 ± 2.1%).

Results
Overall, estradiol was significantly increased during the LF phase compared to M in both type 1 diabetes (Δestradiol = 75 ± 86 pg/mL) and controls (Δestradiol = 71 ± 76 pg/mL); however, an increase in TBARS was only observed in patients with type 1 diabetes (ΔTBARS = 3 ± 13 µM) compared to controls (ΔTBARS = 0 ± 4 µM). FMD was similar (p = 0.406) between groups at M. In addition, FMD increased significantly from M to the LF phase in controls (p = 0.024), whereas a decrease was observed in type 1 diabetes. FMD was greater (p = 0.015) in patients using HBC compared to those not on HBC, independent of menstrual cycle phase.

Conclusion
Endogenous estradiol increases oxidative stress and contributes to endothelial dysfunction in women with diabetes. Additionally, HBC use appears to be beneficial to endothelial function in type 1 diabetes.
Triglyceride-glucose index and heart failure: a systematic review and meta-analysis
Authors: Khalaji Amirmohammad , Behnoush Amir Hossein , Khanmohammadi Shaghayegh , Ghanbari Mardasi Kimiya , Sharifkashani Sourena , Sahebkar Amirhossein , Vinciguerra Caterina , Cannavo Alessandro 
Published: September 2023

Abstract
Background
Insulin resistance (IR) is a major metabolic disorder observed in heart failure (HF) and is tightly associated with patients’ poor prognosis. The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of IR in HF. Yet, whether TyG is a reliable clinical marker is still under debate. Hence, we aimed to respond to this relevant question via a systematic review and meta-analysis of existing studies.

Methods
A systematic search was conducted in PubMed, Embase, Scopus, and Web of Science to find studies investigating the TyG index in patients with HF or its association with the incidence of HF. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) were pooled through random-effect meta-analysis. HRs were calculated using TyG as a continuous variable (1 unit increase) and by comparing the group with the highest TyG to the lowest TyG group.

Results
Thirty studies, involving 772,809 participants, were included in this systematic review. Meta-analysis of seven studies comparing the highest-TyG to the lowest-TyG group showed a significantly increased risk of HF in the former group (HR 1.21, 95% CI 1.14 to 1.29, P < 0.01). The same result was found when pooling the HRs for a one-unit increase in the TyG index (HR 1.17, 95% CI 1.08 to 1.26). Similarly, a more elevated TyG index was associated with a higher incidence of HF in patients with type 2 diabetes or coronary artery disease. Additionally, the incidence of adverse events (readmission and mortality) in patients with HF was associated with TyG.

Conclusion
Our findings support the TyG index as a valuable marker to assess the risk of HF incidence in different populations and as a prognostic marker in patients with HF. Further studies should be conducted to confirm these associations and investigate the clinical utility of the TyG index.
Streamlining N-terminally anchored yeast surface display via structural insights into S. cerevisiae Pir proteins
Authors: Martinić Cezar Tea , Lozančić Mateja , Novačić Ana , Matičević Ana , Matijević Dominik , Vallée Béatrice , Mrša Vladimir , Teparić Renata , Žunar Bojan 
Published: September 2023

AbstractSurface display co-opts yeast’s innate ability to embellish its cell wall with mannoproteins, thus converting the yeast’s outer surface into a growing and self-sustaining catalyst. However, the efficient toolbox for converting the enzyme of interest into its surface-displayed isoform is currently lacking, especially if the isoform needs to be anchored to the cell wall near the isoform’s N-terminus, e.g., through a short GPI-independent protein anchor. Aiming to advance such N-terminally anchored surface display, we employed in silico and machine-learning strategies to study the 3D structure, function, genomic organisation, and evolution of the Pir protein family, whose members evolved to covalently attach themselves near their N-terminus to the β-1,3-glucan of the cell wall. Through the newly-gained insights, we rationally engineered 14 S. cerevisiae Hsp150 (Pir2)-based fusion proteins. We quantified their performance, uncovering guidelines for efficient yeast surface display while developing a construct that promoted a 2.5-fold more efficient display of a reporter protein than the full-length Hsp150. Moreover, we developed a Pir-tag, i.e., a peptide spanning only 4.5 kDa but promoting as efficient surface display of a reporter protein as the full-length Hsp150. These constructs fortify the existing surface display toolbox, allowing for a prompt and routine refitting of intracellular proteins into their N-terminally anchored isoforms.
Graphical abstract