bluuebunny/crossref_metadata_2025_split huggingface dataset:
Secretome Derived from Mesenchymal Stem/Stromal Cells: A Promising Strategy for Diabetes and its Complications
Authors: li ling , Hua Siyu , You Lianghui , Zhong Tianying 
Published: September 2023

:
Diabetes is a complex metabolic disease with a high global prevalence. The health and quality
of life of patients with diabetes are threatened by many complications, including diabetic foot ulcers,
diabetic kidney diseases, diabetic retinopathy, and diabetic peripheral neuropathy. The application of
mesenchymal stem/stromal cells (MSCs) in cell therapies has been recognized as a potential treatment
for diabetes and its complications. MSCs were originally thought to exert biological effects exclusively
by differentiating and replacing specific impaired cells. However, the paracrine function of factors secreted
by MSCs may exert additional protective effects. MSCs secrete multiple compounds, including
proteins, such as growth factors, chemokines, and other cytokines; nucleic acids, such as miRNAs; and
lipids, extracellular vesicles (EVs), and exosomes (Exos). Collectively, these secreted compounds are
called the MSC secretome, and usage of these chemicals in cell-free therapies may provide stronger
effects with greater safety and convenience. Recent studies have demonstrated positive effects of the
MSC secretome, including improved insulin sensitivity, reduced inflammation, decreased endoplasmic
reticulum stress, enhanced M2 polarization of macrophages, and increased angiogenesis and autophagy;
however, the mechanisms leading to these effects are not fully understood. This review summarizes the
current research regarding the secretome derived from MSCs, including efforts to quantify effectiveness
and uncover potential molecular mechanisms in the treatment of diabetes and related disorders. In
addition, limitations and challenges are also discussed so as to facilitate applications of the MSC secretome
as a cell-free therapy for diabetes and its complications.
Thorough Assessment of Machine Learning Techniques for Predicting
Protein-Nucleic Acid Binding Hot Spots
Authors: Zou Xianzhe , Zhang Chen , Tang Mingyan , Deng Lei 
Published: September 2023

Background:
Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful.

Methods:
In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques.

Results:
Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance.

Conclusion:
It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.
Telehealth Satisfaction in Patients Receiving Virtual Atrial Fibrillation Care: Quantitative Exploratory Study
Authors: Rush Kathy L , Burton Lindsay , Seaton Cherisse L , Loewen Peter , O'Connor Brian P , Moroz Lana , Corman Kendra , Smith Mindy A , Andrade Jason G 
Published: September 2023

Background
Telehealth can optimize access to specialty care for patients with atrial fibrillation (AF). Virtual AF care, however, may not fit with the complex needs of patients with AF.

Objective
This study aims to explore the correlation among attitudes toward health care technologies, self-efficacy, and telehealth satisfaction as part of the future planning of virtual AF clinic care.

Methods
Patients with AF older than 18 years from an urban-based, highly specialized AF clinic who had an upcoming telehealth visit were invited to participate in a web-based survey. The survey asked about demographic characteristics; use of technology; general, computer, and health care technology self-efficacy (HTSE) and health care technology attitudes, using a validated 30-item tool; and telehealth satisfaction questionnaire using a validated 14-item questionnaire. Data were analyzed with descriptive statistics, correlational analyses, and linear regression modeling.

Results
Participants (n=195 of 579 invited, for a 34% response rate) were primarily older, male, and White, had postsecondary schooling or more, and had high self-reported overall and mental health ratings. A variety of technologies were used in their daily lives and for health care, with the majority of technologies comprising desktop and laptop computers, smartphones, and tablets. Self-efficacy and telehealth satisfaction questionnaire scores were high overall, with male participants having higher general self-efficacy, computer self-efficacy, HTSE, and technology attitude scores. After controlling for age and sex, only HTSE was significantly related to individuals’ attitudes toward health care technology. Both general self-efficacy and attitude toward health care technology were positively related to telehealth satisfaction.

Conclusions
Consistent with a previous study, only HTSE significantly influenced attitudes toward health care technology. This finding confirms that, in this regard, self-efficacy is not a general perception but is domain specific. Considering participants’ predominant use of the telephone for virtual care, it follows that general self-efficacy and attitude toward health care technology were significant contributors to telehealth satisfaction. Given our patients’ frequent use of technology and high computer self-efficacy and HTSE scores, the use of video for telehealth appointments could be supported.
The EU Settled Status (Wales) data linkage project: Initial findings relating to health and education
Authors: Lloyd-Williams Ffion , Drinkwater Stephen , Curds Matt 
Published: September 2023

ObjectivesFunded by ADR UK (ESRC) from 2020 to 2023, the project aims to anonymously link European Union Settlement Scheme (EUSS) data with other data already held within the SAIL Databank, based at Swansea University, and produce a research-ready dataset that can be used by researchers to obtain policy-relevant findings.
MethodUsing a range of de-identified data in the SAIL Databank, a control group of British citizens in Wales has been matched with EU citizens with similar characteristics using the Census 2011 as a spine to identify country of birth. The SQL coding programme is used to link Census data with several other datasets in the SAIL Databank relating to health, with a focus on mental health, education and employment. The R software package is used for statistical analysis to produce comparisons between the groups and performing significance tests of these comparisons (e.g. Mann Whitney).
ResultsInitial findings indicate small but statistically significant differences in school attendance between British pupils in Wales and pupils from EU14 and EU8 countries and similar differences in school attainment between British pupils in Wales and pupils from EU14 and EU8 countries. Further analysis has been conducted to explore differences between pupils from EU14 and EU8 countries. Census data are also linked to GP attendance data to explore differences in mental health related consultations and referrals for British citizens in Wales and citizens from EU countries and differences between citizens from EU14 and EU8 countries. Detailed findings from this linking of datasets and analysis will be presented.
ConclusionLinking data in this way helps to gain a better understanding of the experiences and outcomes of EU citizens in Wales, generating better evidence to help inform policies and services that address the needs of this population and offers a dataset of great interest to academics.
Walking the line: Does crossing a high stakes exam threshold matter for labour market outcomes?
Authors: Anderson Oliver 
Published: September 2023

ObjectivesThis paper offers new insight into the link between success in high stakes exams and subsequent education and labour market outcomes. It is the first study to look holistically at the impact of crossing an important high stakes threshold on both academic and vocational education choices and ultimately labour market outcomes.
MethodIt does so by comparing those either side of a formerly important threshold in the English education system at the end of compulsory schooling (achieving five general certificate of secondary education A* to C passes) which was commonly regarded as the minimum benchmark for continuing into post-compulsory education.
ResultsI find that crossing this threshold led to an 6.3-6.7 percentage point increase in the proportion of men and women (respectively) going on to take academic qualifications, with little change in the proportion taking vocational qualifications, leading to a net increase in those staying on after compulsory schooling. Women's daily earnings in 2017-18 (11-13 years after leaving compulsory schooling) were 3.1 percentage points higher for those just crossing the threshold, but men's early labour market outcomes were unchanged.
ConclusionThe results for men can be explained by low returns to academic qualifications for marginal learners. The findings for women do not disappear after accounting for subsequent education choices, suggesting that crossing the threshold may play a signalling role for employers as well as education institutions.
Validation of a dynamic method of measuring households and populations from primary care Electronic Health Records: Cross-sectional comparison with Office for National Statistics Census 2021 estimates
Authors: Wilk Marta , Harper Gill , Firman Nicola , Dibben Chris , Fry Rich , Dezateux Carol 
Published: September 2023

ObjectivesWe have developed a dynamic method for identifying household members from Electronic Health Records (EHR). We compared the 2021 Census estimates of household number and demography with similar estimates derived from primary care EHRs on the Census date using primary care EHRs for the population of north east London (NEL).
MethodWe included 2,115,017 patients registered with a general practitioner on the 2021 Census date in NEL and assigned households from encrypted Unique Property Reference Numbers. We compared household number and size by Local Authority (LA), Middle Layer Super Output Area (MSOA) and area’s Index of Multiple Deprivation quintiles (IMDq) to Office for National Statistics (ONS) 2021 Census estimates and by LA to ONS Admin Based Housing Stock (ABHS) 2020 estimates. We assessed differences in EHR and Census 2021 populations by sex, age, LA, MSOA and IMDq. Sensitivity analyses will exclude those without a recent recorded clinical encounter.
ResultsEHR population estimates (2,115,017) were 116,346 (5.8%) higher than Census estimates (1,998,671), higher among men (9.2%) than women (2.5%) in almost all age groups, especially men aged 30-50 years and higher in the most (8.7%), than in the least (2.5%) deprived IMDq. EHR household estimates (660,789) were 68,047 (9.3%) lower than Census estimates (728,836), and 19,719 (3.1%) higher than ABHS occupied addresses (641,070). EHR household size estimates were 15.6%, 29.2%,12.5% and 8.4% lower for household sizes 1,2 3 and 4, and 13.3%, 42.1%, 82.1% and 195.8% higher for household sizes 5, 6,7 and 8 respectively when compared to Census estimates. EHR population and household estimates were respectively 5-10% higher and 5-11% lower for almost all NEL local authorities.
ConclusionEHR- and Census-derived population and household estimates differ, mainly in the prevalence of larger households. While data were extracted on the same date, person-level validation was not possible. Differences may reflect deregistration delay in EHR when changing residence. Analyses based on clinical encounters recency may identify registered patients who are no longer residents.
The impact of record source on multimorbidity measurement and mortality associations: A comparison study
Authors: MacRae Clare 
Published: September 2023

ObjectivesMeasurement of multimorbidity is important; the impact of choice of record source on multimorbidity measurement is uncertain. We compared estimated prevalence of multimorbidity and associations with mortality using different record sources.
MethodCross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of multimorbidity and LTC prevalence using diagnosis codes from primary care (PC), hospital inpatient (HI), and linked PC-HI records, and associations with 12-month mortality were calculated.
ResultsMultimorbidity was more prevalent (32.2% versus 16.4%), included a younger population (62.5 years versus 66.8 years) with more women (54.2% versus 52.6%), and was more strongly associated with mortality (adjusted odds ratio 7.93 [95%CI 7.62-8.25] versus 4.70 [95%CI 4.55-4.85] in people with ≥ 4 LTCs) using linked PC-HI than HI records. Prevalence of LTCs using PC versus HI records was significantly higher in 37/47 and significantly lower in 10/47. Averaging all LTCs, the ratio of PC/linked PC-HI records was higher than HI/linked PC-HI records (84.7% [interquartile range, IQR 76.7 to 90.4] versus 49.0 [IQR 31.9 to 83.6]). Measures of concordance between PC and HI records varied widely across LTCs and the Kappa statistic, a comparison of percentage agreement and chance alone, was slight in five, fair in 13, moderate in 17, and substantial in 12 LTCs, and lowest in mental and behavioural disorders.
ConclusionUsing linked PC-HI records provides higher estimated prevalence of multimorbidity with higher proportions of certain populations and higher association with mortality. Use of single record sources will underestimate the prevalence of many important conditions, especially mental and behavioural disorders.
School performance trajectories and young adult offending: Findings from a national administrative data linkage, United Kingdom
Authors: Wickersham Alice , Cornish Rosie , Scott Stephen , Downs Johnny 
Published: September 2023

ObjectivesCriminal offending and re-offending comes at a significant social and economic cost. Offending prevention therefore presents a high priority policy area. Low educational attainment is a known risk factor for offending, but little is understood about how changes in school performance over time might be associated with offending.
MethodsWe investigated this in a large sample of n~4.3 million pupils using an administrative data linkage between two routinely-collected national datasets: the National Pupil Database (NPD) and the Police National Computer (PNC). First, we conducted growth mixture modelling using NPD data over three statutory testing years (School Years 2, 6 and 11). We then investigated the association between membership of these trajectories and subsequent conviction or caution for any criminal offence between Year 11 and age 21.
ResultsWe derived five school performance trajectories: (1) Average Consistent (n=3,497,167, 81.0%), (2) Average Increasing (n=66,383, 1.5%), (3) Average Declining (n=373,117, 8.6%), (4) Low Increasing (n=98,805, 2.3%), and (5) Low Consistent (n=281,964, 6.5%). The Average Declining group had the highest proportion of individuals who went on to be convicted or cautioned for any first offence up to age 21 (9.8%), followed by the Low Consistent (8.5%), Low Increasing (5.6%), Average Consistent (4.2%) and Average Increasing (1.5%) groups. Furthermore, as the number of offending days between Year 11 and age 21 increased (indicating repeat offending), the likelihood of having been in the Average Declining or Low Consistent groups also increased. We will also present findings from multilevel models accounting for school clustering, different offence types, and interactions.
ConclusionTentatively, findings suggest that changes in school performance could help to identify pupils who are struggling and at increased risk of criminal justice involvement, and therefore might be in need of additional support.
AD|ARC (Administrative Data | Agricultural Research Collection): Linking individual, household and farm business data for agricultural research – Challenges of linking agricultural datasets with individual-level records
Authors: Morrison-Rees Sian , Webster Nicholas , Caskie Paul 
Published: September 2023

ObjectivesTo create an anonymised research-ready data resource of farm households in the UK to generate evidence to support policy development, implementation and evaluation; improve understanding of farm family socio-economic characteristics; and assist stakeholders interested in understanding the health and well-being, resilience and prosperity and spatial properties of farming communities.
MethodsThe ADARC research-ready data resource is being created in each nation of the UK. Each core dataset links agricultural datasets with individual- and household-level population data from the Census 2011. The farm business data is drawn from a number of sources (the Inter Departmental Business Register, EU Farm Structure Survey 2010 and Rural Payments data), which presented many data preparation challenges when linking to the Census of Population. Each core dataset will be linked to the health and education data available for that nation. Where possible, the ADARC datasets have been harmonised to allow federated querying across the UK.
ResultsThe ADARC core datasets are complete in Wales and near completion in England and Scotland. Work is also well advanced in Northern Ireland. To the best of our knowledge, these are the first datasets linking agricultural data to individual- and household-level data at a population level. We will report the challenges experienced in linking farm business data with data at the household and individual level. This will include a description of the data preparation steps, the challenges encountered and solutions utilised at each stage of building this complex dataset from numerous, very different ‘parent’ datasets.
The structure and content of the core datasets will be presented as well as the potential benefits to researchers investigating the individual, household and community dimensions of agricultural research.
ConclusionAgriculture is currently facing a range of challenges with little known about the impact on farmers and farming households. ADARC introduces a new, powerful and versatile resource that will help inform debate and potentially lead to better outcomes on a range of issues relevant to farmers and farming communities.
Linking of the 2021 Census to massive linked administrative data to understand coverage and quality
Authors: Plachta Josie , Collyer Sarah 
Published: September 2023

The Office for National Statistics has built a vast, composite dataset for population statistics by linking data from health, education, and employment sources, known as the Demographic Index (DI). It attempts to contain a record ‘cluster’ for each person in England and Wales. To understand the coverage and quality of the DI, it has been linked to the 2021 Census to a high standard, enabling review of those captured incorrectly: and over- and undercoverage.
Massive data techniques were used to apply deterministic, probabilistic, and associative methods. High quality was achieved by applying clerical matching methods to cases that could not be confirmed by automatic techniques. Due to resource limitations, only a subsample was linked to this high standard. The resulting links were flagged to indicate cases where the DI had correctly captured persons or had made errors. Errors included capturing persons at the wrong address, accidently splitting a person’s records across two clusters, or incorrectly capturing two persons in the same cluster. Unlinked records were flagged as under-coverage (census) or over-coverage (DI).
The 2021 Census was linked to the DI with an estimated precision of 99.4%-99.7% and recall of 99.1%-99.7%. This exceptional quality allows ONS analysts to use this dataset with high confidence in analysing the quality of the DI and its impact on statistics. In general, DI under-coverage was low, with 0.9% of Census records in the subsample not present on the DI. However, DI over-coverage was much higher, with 29.5% of DI records in the subsample not present on the census. 2.3% of census persons in the subsample had been incorrectly split across multiple clusters, and 0.3% had been merged into a cluster with multiple other persons.
The ONS successfully linked the 2021 Census to the DI to a high quality. The linkage suggests that the DI captures most of the current population correctly but captures many persons that are not. These insights must be considered by any users of the data.
Data linkage research to explore the growing share of domestic abuse cases in family courts
Authors: Garside Ludivine 
Published: September 2023

ObjectivesThe caseload of family courts in England and Wales includes a growing share of domestic abuse cases. To increase knowledge about such cases, the research uses new administrative datasets and linkages to describe trends between 2011-2021 and to supplement the information already available from family court national statistics.
MethodThe complete set of family court cases is held in HM Courts & Tribunals Services (HMCTS) systems. Some family court data are collected by the Children and Families Court Advisory Support Service (Cafcass), insofar as courts refer the case to Cafcass. By using their new linkage, we can conduct a descriptive analysis of domestic abuse cases in England and Wales over the ten years since 2011. This project is one of the first to gain approval from HMCTS to use their family court data. Access to de-identified versions of all datasets is through the SAIL Databank trusted research environment.
ResultsThe data linkage builds a more complete picture of the individuals involved, exploring their age group, gender and ethnicity. The new datasets allow to describe how court users approach domestic abuse matters and to draw distinctions between different types of cases: emergency situations; individuals being unrepresented in court (litigants in person); repeat applications; requests for different types of legal measures against the domestic abuse; and presence of children in the household. Furthermore, the data show how courts respond through case management, timeliness of decisions and legal outcomes made. We explore how different types of individuals and cases are associated with legal outcomes, and how the added understanding at population-level can focus and support discussions about the use of family courts to address domestic abuse.
ConclusionData linkage research is providing an evidence base at population level about court users in England and Wales who experience domestic abuse. This can help inform practice to support those families and policies to ease overburdened courts who see the same families and domestic abuse issues return to court.