bluuebunny/crossref_metadata_2025_split huggingface dataset:
Midwifery and nurse staffing of inpatient maternity services – a systematic scoping review of associations with outcomes and quality of care
Authors: Turner Lesley , Griffiths Peter , Kitson-Reynolds Ellen 
Published: March 2021

AbstractObjectiveTo undertake a scoping literature review of studies examining the quantitative association between staffing levels and outcomes for mothers, neonates, and staff. The purpose was to understand the strength of the available evidence, the direction of effects, and to highlight gaps for future research.Data SourcesSystematic searches were conducted in Medline (Ovid), Embase (Ovid), CINAHL (EBCSCO), Cochrane Library, TRIP, Web of Science and Scopus.Study Selection and Review methodsTo be eligible, staffing levels had to be quantified for in-patient settings, such as ante-natal, labour/delivery or post-natal care. Staff groups include registered midwives, nurse midwives or equivalent, and assistant staff working under the supervision of registered professionals. Studies of the quality of care, patient outcomes and staff outcomes were included. All quantitative designs were included, including controlled trials, time series, cross-sectional, cohort studies and case controlled studies.Data were extracted and sources of bias identified by considering the study design, measurement of exposure and outcomes, and risk adjustment. Studies were grouped by outcome noting the direction and significance of effects.ResultsThe search yielded a total of 3280 records and 21 studies were included in this review. There were three randomised controlled trials, eleven cohort studies, one case control study and six cross sectional studies. Seventeen were multicentre studies and nine of them had over 30,000 participants.Reduced incidence of epidural use, augmentation, perineal damage at birth, postpartum haemorrhage, maternal readmission, and neonatal resuscitation were associated with increased midwifery staff. Few studies have suggested a negative impact of increasing staffing rates, although a number of studies have found no significant differences in outcomes. Impact on the mode of birth were unclear. Increasing midwifery support staff was not associated with improved patient outcomes. No studies were found on the impact of low staffing levels for the midwifery workforce.Conclusions and Implications for practiceAlthough there is some evidence that higher midwifery staffing is associated with improved outcomes, current research is insufficient to inform service planning. Studies mainly reported outcomes relating to labour, highlighting a gap in research evidence for the antenatal and postnatal periods. Further studies are needed to assess the costs and consequences of variations in maternity staffing, including the deployment of maternity care assistants and other staff groups.
The potential of digital health technologies in African context, Ethiopia
Authors: Manyazewal Tsegahun , Woldeamanuel Yimtubezinash , Blumberg Henry M. , Fekadu Abebaw , Marconi Vincent C. 
Published: March 2021

ABSTRACTThe World Health Organization (WHO) recently put forth a Global Strategy on Digital Health 2020 - 2025 with several countries having already achieved key milestones. We aimed to understand whether and how digital health technologies (DHTs) are absorbed in Africa, tracking Ethiopia as a key node. We conducted a systematic review, searching PubMed-MEDLINE, ScienceDirect, African Journals Online, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform databases from inception to 02 February 2021 for studies of any design that investigated the potential of DHTs in clinical or public health practices in Ethiopia. This review was designed to inform our ongoing DHT-enabled randomized controlled trial (RCT) (ClinicalTrials.gov ID: NCT04216420). We found 23,897 potentially-relevant citations, among which 47 studies met the inclusion criteria, comprising a total of 594,999 patients, healthy individuals, and healthcare professionals. The studies involved seven DHTs: mHealth (25 studies, 573,623 participants); electronic health records (13 studies, 4,534 participants); telemedicine (3 studies, 445 participants); cloud-based application (2 studies, 2,382 participants); genomics data (1 study, 47 participants); information communication technology (2 studies, 551 participants), and artificial intelligence (1 study, 13,417 participants). The studies targeted six health conditions: maternal and child health (15), infectious diseases (11), non-communicable diseases (3), dermatitis (1), surgery (3), and general health conditions (14). The outcomes of interest were feasibility, usability, willingness or readiness, effectiveness, quality improvement, and knowledge or attitude towards DHTs. Four studies involved RCTs. The analysis showed that although DHTs are a relatively recent phenomenon in Ethiopia, their potential harnessing clinical and public health practices are highly visible. Their adoption and implementation in full capacity requires more training, access to better devices such as smartphones, and infrastructure. DHTs hold much promise tackling major clinical and public health backlogs and strengthening the healthcare ecosystem in Africa. More RCTs are needed on emerging DHTs including artificial intelligence, big data, cloud, genomics data, cybersecurity, telemedicine, and wearable devices to provide robust evidence of their potential use in such settings and to materialize the WHO’s Global Digital Health Strategy.
Relative burdens of the COVID-19, malaria, tuberculosis, and HIV/AIDS epidemics in sub-Saharan Africa
Authors: Bell David , Hansen Kristian Schultz 
Published: March 2021

ABSTRACTCOVID-19 has had considerable global impact, but in sub-Saharan Africa is one of several infectious disease priorities. Prioritization is normally guided by disease burden, but the highly age-dependent nature of COVID-19 and other infectious diseases makes comparisons challenging unless considered through metrics that incorporate life years lost and time lived in adverse health. We therefore compared 2020 mortality and Disability-Adjusted Life Years lost (DALYs lost) estimates for malaria, tuberculosis, and HIV/AIDS in sub-Saharan African populations with 12+ months of COVID-19 burden (up to end March 2021), applying known age-related mortality to United Nations estimates of age structure. We further compared exacerbations of disease burden predicted from the COVID-19 public health response. Data was derived from public sources, predicted exacerbations derived from those published by international agencies. For sub-Saharan African populations north of South Africa, recorded COVID-19 DALYs lost in 2020 was 3.7%, 2.3%, and 2.4% of those estimated for tuberculosis, HIV/AIDS and malaria respectively. Predicted exacerbations of these comparator diseases were greater than the estimated COVID-19 burden. Including South Africa and Lesotho, COVID-19 DALYs lost were <12% of those for comparator diseases and dominated by them in all age groups below 65 years. The analysis suggests a relatively low impact from COVID-19. While all four epidemics continue, tuberculosis, HIV/AIDS, and malaria remain far greater health priorities based on disease burden. Resource diversion to COVID-19 therefore runs a high risk of increasing the overall disease burden and causing net harm, further increasing global inequities in health and life expectancy.
Tolerance of Wildlife in Protected Area Borderlands
Authors: Merz Leandra , Pienaar Elizabeth F. , Fik Timothy , Muyengwa Shylock 
Published: March 2021

AbstractIncreases in human-wildlife conflict globally threaten human wellbeing and biodiversity conservation. Sustainable solutions that promote coexistence of people and wildlife are needed, especially in human-dominated landscapes surrounding protected areas. People’s attitudes toward wildlife influence their behaviours, including tolerance for human-wildlife interactions, poaching, and habitat degradation. Better understanding of how to improve people’s attitudes toward wildlife is instrumental to promoting coexistence between people and wildlife in shared spaces. Efforts to promote coexistence often fail because they are based on the inaccurate assumption that people’s attitudes towards wildlife are directly and proportionally related to wildlife-based financial incentives and costs. In reality, people’s attitudes towards wildlife are far more complex. We analysed surveys (n=237) from Mozambique to examine people’s attitudes toward wildlife in the buffer zones surrounding protected areas using logistic regression and Getis-Ord hot-spot analysis (GI*). Mozambique, which is under-represented in the wildlife-based research literature, is characterized by extreme poverty and rewilding efforts. We found that most respondents were tolerant of wildlife and tolerance was positively correlated with people’s age, gender, and agreement with rules governing wildlife conservation. People’s tolerance for wildlife was also reinforced if they receive benefits from wildlife and are situated further from the park fence. Predation, human harm, and crop loss were not significant predictors of tolerance. We found no evidence of spatial patterns in tolerance for wildlife. Our results suggest that wildlife conservation programs are more likely to be successful if benefits are distributed equitably and community members are actively involved in decision making.
Mathematical Modeling and Analysis of Mitochondrial Retrograde Signaling Dynamics
Authors: Chiu Shao-Ting , Tseng Wen-Wei , Wei An-Chi 
Published: March 2021

AbstractMitochondria, semi-autonomous eukaryotic organelles, participate in energy production and metabolism, making mitochondrial quality control crucial. As most mitochondrial proteins are encoded by nuclear genes, quality control depends on proper mitochondria-nucleus communication, designated mitochondrial retrograde signaling. Early studies focused on retrograde signaling participants and specific gene knockouts. However, mitochondrial signal modulation remains elusive. Using yeast, we simulated signal propagation following mitochondrial damage and proposed a mathematical model based ordinary differential equations. Mitochondrial retrograde signaling decisions were described using a Boolean model. Dynamics were analyzed through an ordinary differential equation-based model and extended to evaluate the model response to noisy damage signals. Simulation revealed localized protein concentration dynamics, including waveforms, frequency response, and robustness under noise. Retrograde signaling is bistable with three localized steady states, and increased damage compromises robustness. We elucidated mitochondrial retrograde signaling, thus providing a basis for drug design against yeast and fungi.Author SummaryThe yeast RTG pathway regulates mitochondrial metabolism and mitochondrial quality through passing mitochondrial signal to the nucleus to modulate gene expressions. Using microscopic data of RTG proteins translocation from cytosol to nucleus, the parameters were found by fitting 16 knockout conditions simulated from the proposed differential equation-based model. Further, through dose response, ultrasensitivity, frequency response and noise interference, we demonstrated a switch-like property of RTG pathway activation, a capacity charging property of RTG protein translocation, a low pass filter property of signals in different frequencies, and noise amplification due to mitochondrial signal. The proposed mathematical model enables us to understand the dynamics and mechanisms in mitochondrial retrograde signaling in yeast and provides potential antifungal treatment strategies.
GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning
Authors: Gröschel Matthias I , Owens Martin , Freschi Luca , Vargas Roger , Marin Maximilian G , Phelan Jody , Iqbal Zamin , Dixit Avika , Farhat Maha R 
Published: March 2021

ABSTRACTIntroductionMultidrug-resistantMycobacterium tuberculosis(Mtb) is a significant global public health threat. Genotypic resistance prediction fromMtbDNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens.MethodsWe present Translational Genomics platform for Tuberculosis (GenTB), a web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB’s predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data.ResultsAll four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs was 77.6% (95% CI 76.6 - 78.5%) and 75.4% (95% CI 74.5 - 76.4%) respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4 - 75.3%) and Mykrobe at 71.9% (95% CI 70.9 - 72.9%). The higher sensitivities were at an expense of ≤1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5 - 97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (<10x across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variantsConclusionGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online athttps://gentb.hms.harvard.edu, and the source code is available athttps://github.com/farhat-lab/gentb-site.
MQuad enables clonal substructure discovery using single cell mitochondrial variants
Authors: Kwok Aaron Wing Cheung , Qiao Chen , Huang Rongting , Sham Mai-Har , Ho Joshua W. K. , Huang Yuanhua 
Published: March 2021

AbstractMitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available viahttps://github.com/single-cell-genetics/MQuad.
Analysis of half a billion datapoints across ten machine-learning algorithms identify key determinants of insulin gene transcription
Authors: Wong Wilson K.M. , Thorat Vinod , Joglekar Mugdha V. , Dong Charlotte X. , Lee Hugo , Chew Yi Vee , Bhave Adwait , Hawthorne Wayne J. , Engin Feyza , Pant Aniruddha , Dalgaard Louise T. , Bapat Sharda , Hardikar Anandwardhan A. 
Published: March 2021

AbstractMachine learning (ML)-workflows enable unprejudiced/robust evaluation of complex datasets. Here, we analyzed over 490,000,000 data points to compare 10 different ML-workflows in a large (N=11,652) training dataset of human pancreatic single-cell (sc-)transcriptomes to identify genes associated with the presence or absence of insulin transcript(s). Prediction accuracy/sensitivity of each ML-workflow were tested in a separate validation dataset (N=2,913). Ensemble ML-workflows, in particular Random Forest ML-algorithm delivered high predictive power (AUC=0.83) and sensitivity (0.98), compared to other algorithms. The transcripts identified through these analyses also demonstrated significant correlation with insulin in bulk RNA-seq data from human islets. The top-10 features, (includingIAPP, ADCYAP1, LDHAandSST) common to the three Ensemble ML-workflows were significantly dysregulated in scRNA-seq datasets from Ire-1αβ-/-mice that demonstrate dedifferentiation of pancreatic β-cells in a model of type 1 diabetes (T1D) and in pancreatic single cells from individuals with type 2 Diabetes (T2D). Our findings provide direct comparison of ML-workflows in big data analyses, identify key determinants of insulin transcription and provide workflows for future analyses.
Prohibit, protect, or adapt? The changing role of volunteers in palliative and hospice care services during the COVID-19 pandemic. A multinational survey (CovPall)
Authors: Walshe Catherine , Garner Ian , Dunleavy Lesley , Preston Nancy , Bradshaw Andy , Cripps Rachel L , Bajwah Sabrina , Sleeman Katherine E , Hocaoglu Mevhibe , Maddocks Matthew , Murtagh Fliss EM , Oluyase Adejoke O , Fraser Lorna K , Higginson Irene J 
Published: March 2021

BackgroundVolunteers are common within palliative care services, and provide support that enhances care quality. The support they provided, and any role changes, during the COVID-19 pandemic are unknown.AimsTo understand volunteer deployment and activities within palliative care services, and to identify what may affect any changes in volunteer service provision, during the COVID-19 pandemic.MethodsMulti-national online survey disseminated via key stakeholders to specialist palliative care services, completed by lead clinicians. Data collected on volunteer roles, deployment, and changes in volunteer engagement. Analysis included descriptive statistics, a multivariable logistic regression, and analysis of free-text comments using a content analysis approach.Results458 respondents: 277 UK, 85 rest of Europe, and 95 rest of the world. 68.5% indicated volunteer use pre-COVID-19. These were across a number of roles (from 458): direct patient/family facing support (58.7%), indirect support (e.g. driving) (52.0%), back office (48.5%) and fundraising (45.6%). 11% had volunteers with COVID-19. Of those responding to a question on change in volunteer deployment (328 of 458) most (256/328, 78%) indicated less or much less use of volunteers. Less use of volunteers was associated with being an in-patient hospice, (OR=0.15, 95%CI = 0.07-0.3 p<.001). This reduction in volunteers was felt to protect potentially vulnerable volunteers and with policy changes preventing volunteers from supporting services. However, adapting was also seen where new roles were created, or existing roles pivoted to provide virtual support.Discussion and conclusionVolunteers were mostly prevented from supporting many forms of palliative care, particularly in in-patient hospices, which may have quality and safety implications given their previously central roles. Volunteer re-deployment plans are needed that take a more considered approach, using volunteers more flexibly to enhance care while ensuring safe working practices. Consideration needs to be given to widening the volunteer base away from those who may be considered to be most vulnerable to COVID-19.
Transcription initiation at a consensus bacterial promoter proceeds via a “bind-unwind-load-and-lock” mechanism
Authors: Mazumder Abhishek , Ebright Richard H , Kapanidis Achillefs N 
Published: March 2021

AbstractTranscription initiation starts with unwinding of promoter DNA by RNA polymerase (RNAP) to form a catalytically competent RNAP-promoter complex (RPO). Despite extensive study, the mechanism of promoter unwinding has remained unclear, in part due to the transient nature of intermediates on path to RPo. Here, using single-molecule unwinding-induced fluorescence enhancement to monitor promoter unwinding, and single-molecule fluorescence resonance energy transfer to monitor RNAP clamp conformation, we analyze RPo formation at a consensus bacterial core promoter. We find that the RNAP clamp is closed during promoter binding, remains closed during promoter unwinding, and then closes further, locking the unwound DNA in the RNAP active-centre cleft. Our work defines a new, “bind-unwind-load-and-lock,” model for the series of conformational changes occurring during promoter unwinding at a consensus bacterial promoter and provides the tools needed to examine the process in other organisms and at other promoters.Significance statementTranscription initiation, the first step and most important step in gene expression for all organisms, involves unwinding of promoter DNA by RNA polymerase (RNAP) to form an open complex (RPo); this step also underpins transcriptional regulation and serves as an antibiotic target. Despite decades of research, the mechanism of promoter DNA unwinding has remained unresolved. Here, we solve this puzzle by using single-molecule fluorescence to directly monitor conformational changes in the promoter DNA and RNAP in real time during RPo formation. We show that RPo forms via a “bind-unwind-load-and-lock” mechanism, where the promoter unwinds outside the RNAP cleft, the unwound template DNA loads into the cleft, and RNAP “locks” the template DNA in place by closing the RNAP clamp module.
Mismatch between bird species sensitivity and the protection of intact habitats across the Americas
Authors: Cazalis Victor , Barnes Megan D. , Johnston Alison , Watson James E.M. , Şekercioğlu Cagan H. , Rodrigues Ana S.L. 
Published: March 2021

AbstractProtected areas, the most prevalent international policy mechanism for biodiversity conservation, are highly heterogeneous in their effectiveness at buffering ecosystems and species’ habitats from human pressure. Protected areas with intense human pressure cannot protect species that are highly sensitive to human activities. Here, we use 60 million bird observations from the eBird citizen science platform to estimate the sensitivity to human pressure of each bird species breeding in the Americas (Nearctic and Neotropical regions). We find that high-sensitivity species, while found in all ecoregions, are concentrated in the tropical biomes. Ecoregions with large proportions of high-sensitivity species do not have more intact protected habitat, resulting in a low coverage of intact protected habitat for many high-sensitivity species. What is more, 139 high-sensitivity species have little or no intact protected habitat within their distributions while being threatened with extinction. Finally, we show that protected area intactness is decreasing faster in ecoregions with many high-sensitivity species. Our results highlight a major mismatch between species conservation needs and the coverage of intact protected habitats, and will likely hamper the long-term effectiveness of protected areas at retaining species. We highlight ecoregions where the protection and management of intact habitats, complemented by the restoration of degraded ones, is urgently needed to avoid extinctions.