bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surgical Performance Anxiety and Wellbeing Among Surgeons
Authors: Miller Robert , Hotton Matthew , Williamon Aaron , Kneebone Roger , Goodacre Tim , O’Leary Denis , Chan James K. K. 
Published: August 2021

Objective:
This national cross-sectional study aims to establish the prevalence and potential impact of performance anxiety among surgeons and investigate its association with psychological traits and wellbeing.

Summary and Background Data:
Despite a growing awareness that human factors, non-technical skills and wellbeing in healthcare affect patient outcomes, an area that has remained unexplored is surgical performance anxiety (SPA).

Methods:
A prospectively registered, cross-sectional study using mixed methods was conducted across the United Kingdom. Data captured included demographics, surgical specialty, trait anxiety, trait perfectionism, SPA, and surgical perfectionism scores. Wellbeing was assessed using The Short Warwick-Edinburgh Mental Wellbeing Scale, whereas qualitative data were collected regarding surgeons’ experiences of SPA.

Results:
A total of 631 responses were collected. Mean age was 41·2 years and mean surgical experience 15·3 years. A total of 62% were male and 52% of consultant/attending grade. A total of 100% felt that SPA affected surgeons, with 87% having experienced it themselves. A total of 65% reported SPA negatively impacted surgical performance and 96% felt SPA negatively impacted surgeons’ wellbeing. Male surgeons reported significantly better wellbeing than female surgeons. Surgeons with SPA reported significantly worse wellbeing compared with surgeons who did not experience SPA. Surgeons in general experienced significantly lower mental wellbeing compared with population norms. Thematic analysis highlighted a reticence to share SPA openly and need for cultural change.

Conclusions:
Surgical performance anxiety is a very common and significant challenge among surgeons across all specialties at all levels of experience in the United Kingdom. It is perceived by surgeons to affect surgical performance adversely and is associated with worse psychological wellbeing. A more open culture of sharing and acknowledgment has been identified to be beneficial.
Effect of Pregnancy on eGFR After Kidney Transplantation: A National Cohort Study
Authors: van Buren Marleen C. , Gosselink Margriet , Groen Henk , van Hamersvelt Henk , de Jong Margriet , de Borst Martin H. , Zietse Robert , van de Wetering Jacqueline , Lely A. Titia ,   on behalf of the PARTOUT working group
Published: August 2021

Background.
The effect of pregnancy on the course of estimated glomerular filtration rate (eGFR) is unknown in kidney transplant recipients (KTRs).

Methods.
We conducted a nationwide multicenter cohort study in KTRs with pregnancy (>20 wk) after kidney transplantation (KT). Annual eGFRs after KT until death or graft loss and additional eGFRs before each pregnancy were collected according to protocol. Changes in eGFR slope before and after each pregnancy were analyzed by generalized estimating equations multilevel analysis adjusted for transplant vintage.

Results.
We included 3194 eGFR measurements before and after pregnancy in 109 (55%) KTRs with 1, 78 (40%) with 2, and 10 (5%) with 3 pregnancies after KT. Median follow-up after first delivery post-KT was 14 y (interquartile range, 18 y). Adjusted mean eGFR prepregnancy was 59 mL/min/1.73 m2 (SEM [standard error of the mean] 1.72; 95% confidence interval [CI], 56-63), after the first pregnancy 56 mL/min/1.73 m2 (SEM 1.70; 95% CI, 53-60), after the second pregnancy 56 mL/min/1.73 m2 (SEM 2.19; 95% CI, 51-60), and after the third pregnancy 55 mL/min/1.73 m2 (SEM 8.63; 95% CI, 38–72). Overall eGFR slope after the first, second, and third pregnancies was not significantly worse than prepregnancy (P = 0.28). However, adjusted mean eGFR after the first pregnancy was 2.8 mL/min/1.73 m2 (P = 0.08) lower than prepregnancy.

Conclusions.
The first pregnancy has a small, but insignificant, effect on eGFR slope in KTRs. Midterm hyperfiltration, a marker for renal reserve capacity, was associated with better eGFR and death-censored graft survival. In this KTR cohort with long-term follow-up, no significant effect of pregnancy on kidney function was detected.
Impact of Red Blood Cells on Function and Metabolism of Porcine Deceased Donor Kidneys During Normothermic Machine Perfusion
Authors: Venema Leonie H. , van Leeuwen L. Leonie , Posma Rene A. , van Goor Harry , Ploeg Rutger J. , Hannaert Patrick , Hauet Thierry , Minor Thomas , Leuvenink Henri G.D. ,   on behalf of the COPE Consortium
Published: August 2021

Background.
Normothermic machine perfusion (NMP) protocols using blood-based solutions are commonly used in the assessment of kidneys before transplantation. This procedure is, nevertheless, limited by blood availability and warrants the search for alternatives. We compared a blood-based solution with a serum-like preservation solution (Aqix) enriched with colloids with and without red blood cells (RBCs).

Methods.
Porcine kidneys retrieved from an abattoir were subjected to 30 min of warm ischemia, followed by 3 h of hypothermic oxygenated machine perfusion at 4 °C. Subsequently, kidneys (n = 6 per group) were evaluated with NMP for 4 h with 5 different solutions: diluted blood, Aqix with BSA ± RBCs, or Aqix with dextran 40 ± RBCs.

Results.
Throughout NMP, markers of renal function and tubular metabolism were favorable in groups with RBCs. The addition of RBCs resulted in 4- to 6-fold higher oxygen consumption rates. Controls had significantly higher ATP levels post-NMP, exhibited decreased production of oxidative stress markers, and had the highest creatinine clearance. In conclusion, this study shows that the addition of RBCs during NMP reduced renal injury, improved function, and was associated with increased renal metabolism.

Conclusions.
Although the RBC-BSA–supplemented Aqix solution was also able to support metabolism and renal function, a blood-based perfusion solution remains superior.
Lattice Boltzmann simulations of drying suspensions of soft particles
Authors: Wouters M. , Aouane O. , Sega M. , Harting J. 
Published: August 2021

The ordering of particles in the drying process of a colloidal suspension is crucial in determining the properties of the resulting film. For example, microscopic inhomogeneities can lead to the formation of cracks and defects that can deteriorate the quality of the film considerably. This type of problem is inherently multiscale and here we study it numerically, using our recently developed method for the simulation of soft polymeric capsules in multicomponent fluids. We focus on the effect of the particle softness on the film microstructure during the drying phase and how it relates to the formation of defects. We quantify the order of the particles by measuring both the Voronoi entropy and the isotropic order parameter. Surprisingly, both observables exhibit a non-monotonic behaviour when the softness of the particles is increased. We further investigate the correlation between the interparticle interaction and the change in the microstructure during the evaporation phase. We observe that the rigid particles form chain-like structures that tend to scatter into small clusters when the particle softness is increased.
This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.
A lattice Boltzmann model for reactive mixtures
Authors: Sawant N. , Dorschner B. , Karlin I. V. 
Published: August 2021

A new lattice Boltzmann model for reactive ideal gas mixtures is presented. The model is an extension to reactive flows of the recently proposed multi-component lattice Boltzmann model for compressible ideal gas mixtures with Stefan–Maxwell diffusion for species interaction. First, the kinetic model for the Stefan–Maxwell diffusion is enhanced to accommodate a source term accounting for the change in the mixture composition due to chemical reaction. Second, by including the heat of formation in the energy equation, the thermodynamic consistency of the underlying compressible lattice Boltzmann model for momentum and energy allows a realization of the energy and temperature change due to chemical reactions. This obviates the need for ad-hoc modelling with source terms for temperature or heat. Both parts remain consistently coupled through mixture composition, momentum, pressure, energy and enthalpy. The proposed model uses the standard three-dimensional lattices and is validated with a set of benchmarks including laminar burning speed in the hydrogen–air mixture and circular expanding premixed flame.
This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.
Comprehensive analysis of the immunogenomic landscape and clinical features in cervical cancer
Authors: Fan Xinyue , He Chunbo 
Published: August 2021

AbstractImmunotherapy has changed the standard of treatment for many cancers. However, the same treatments showed disappointing outcomes in cervical cancer so far. Thus, understanding the mechanisms that support the immune tolerance of cervical cancer will provide a way to design new strategies to enhance immunotherapies. Here, we characterized cellular compositions of the immune infiltrates in cervical cancer and investigated if the tumor immune landscape is a predictor for patient prognosis. The fraction of ten immune infiltrates of cervical and other cancers were analyzed by using QuanTIseq software base on the bulk mRNA sequencing data from The Cancer Genome Atlas Program (TCGA). Cervical cancer is one of the cancers that had the lowest percentage of total immune infiltrates, but it had the highest ratio for CD8 T cells to all immune infiltrates among all solid cancers. Both the principal components (PCA) analysis and heatmap with dendrogram analysis showed that cervical cancer had a similar immune infiltrated microenvironment with other squamous cell carcinomas, such as head and neck cancer and lung squamous cell cancer. The PCA and heatmap with dendrogram analysis showed that cervical cancer and HPV positive head and neck cancers were clustered more closer and partially separated with HPV negative head and neck cancer. Further analysis showed that HPV-positive cervical and head and neck cancers had a significantly higher level of CD8 T cells and M1-liked macrophages, but a lower level of M2 macrophages. The survival analysis showed that a higher level of CD8 T cells was associated with a better patient prognosis. However, immuno-suppressive immune infiltrates including M2 macrophages and Treg cells that are known to suppress anti-tumor immunity also demonstrated positive patient overall survival. Our study provided a conceptual framework to understand the tumor immune microenvironment of cervical cancer. Our results also demonstrated that the immune infiltrates can be a prognosis marker for cervical cancer.Simple SummaryCervical cancer is the most common gynecologic cancer and the fourth leading cause of cancer-related death in women worldwide. There are relatively limited treatment options for late-stage cervical cancer. Immunotherapy is a new therapeutic treatment developed with great success in treating many cancers, but the same treatment has not been producing satisfactory results in many cases of cervical cancer. In the present study, we provided a comprehensive immune characterization specifically for cervical cancer. We determined the prognostic value of a specific subtype of tumor-infiltrating immune cells for clinical outcomes and demonstrated that HPV infection affected the immune cell infiltration and induce pro-inflammatory phenotypes. Our study provides a systematic insight into the tumor immune microenvironment of cervical cancers and offers a conceptual framework for the future design of rational combination treatment strategies to improve immunotherapy outcomes.
The Need for Transfer Learning in CRISPR-Cas Off-Target Scoring
Authors: Kota Pavan K. , Pan Yidan , Vu Hoang-Anh , Cao Mingming , Baraniuk Richard G. , Bao Gang 
Published: August 2021

AbstractMotivationThe scalable design of safe guide RNA sequences for CRISPR gene editing depends on the computational “scoring” of DNA locations that may be edited. As there is no widely accepted benchmark dataset to compare scoring models, we present a curated “TrueOT” dataset that contains thoroughly validated datapoints to best reflect the properties of in vivo editing. Many existing models are trained on data from high throughput assays. We hypothesize that such models may suboptimally transfer to the low throughput data in TrueOT due to fundamental biological differences between proxy assays and in vivo behavior. We developed new Siamese convolutional neural networks, trained them on a proxy dataset, and compared their performance against existing models on TrueOT.ResultsOur simplest model with a single convolutional and pooling layer surprisingly exhibits state-of-the-art performance on TrueOT. Adding subsequent layers improved performance on a proxy dataset while compromising performance on TrueOT. We demonstrate improved generalization on TrueOT with a Siamese model of higher complexity when we apply transfer learning techniques. These results suggest an urgent need for the CRISPR community to agree upon a benchmark dataset such as TrueOT and highlight that various sources of CRISPR data cannot be assumed to be equivalent.Availability and ImplementationOur code base and datasets are available on GitHub at github.com/baolab-rice/CRISPR_OT_scoring.
Forecasting Preventive Dental Quality Measures
Authors: Nagarajan Radhakrishnan , Panny Aloksagar , Ryan Megan , Murphy Scott , Vujicic Marko , Nycz Gregory 
Published: August 2021

AbstractDental quality measures objectively measure the efficiency and performance of dental providers and organizations. While these measures in conjunction with established benchmarks are used routinely for self-assessment, forecasting them ahead of time in a data-driven and evidence-based manner has the potential to assist in assessing future dental treatment needs, oral disease burden, care utilization patterns, and strategic decision making for sustained performance improvement complementing traditional descriptive visualization dashboards. The present study modeled the temporal trends of four key preventive dental quality measures related to caries prevention (Adult New Caries, Sealants (6-9yrs.), Sealants (12-15yrs.), Fluoride Varnish) sampled monthly from (Dec. 2010 to July 2017) averaged across ten Family Health Center Dental Centers (FQHC), Wisconsin, using auto-regressive integrated moving average time series models. Five-month ahead forecasts along with their 95% confidence levels and mean absolute percentage error were determined across the four measures (Adult New Caries: 1.8%, Sealants (6-9yrs.): 0.90%, Sealants (12-15yrs.): 0.30%, Fluoride Varnish: 0.15%). Model diagnostics revealed auto-regressive integrated moving average models to sufficiently capture the temporal patterns of these measures and the forecast estimates of Adult New Caries and Sealants (12-15yrs.) revealed the need for increased efforts for improved preventive care utilization. Forecasting preventive dental quality measures can provide insights into expected treatment needs ahead of time and can assist in optimal resource and staff allocation with potential to prescribe suitable interventions to shift the trajectory from predicted outcomes to desired outcomes in a targeted manner. While the present study investigated organization level preventive dental quality measures, the time series approach presented is as such generic and expected to translate across similar settings.
Predicting the impact of low influenza activity in 2020 on population immunity and future influenza season in the United States
Authors: Lee Kyueun , Jalal Hawre , Raviotta Jonathan M. , Krauland Mary G. , Zimmerman Richard K. , Burke Donald S. , Roberts Mark S. 
Published: August 2021

AbstractBackgroundsThe influenza season of 2020-21 was remarkably low, likely due to implementation of public health preventive measures such as social distancing, mask-wearing, and school closure and due to decreased international travel. This leads to a key public health question: what will happen in the 2021-22 influenza season? To answer this, we developed a multi-season influenza model that accounted for residual immunity from prior infection.MethodWe built a multi-strain, non-age structured compartmental model that captures immunity over multiple influenza seasons. By the end of the influenza season, we sorted the population based on their experience of natural infection and/or vaccination, which determines the susceptibility to influenza infection in the following season. Because the exact parameters of transmission rates and immunity are unknown, we implemented Bayesian calibration against the observed influenza epidemics (influenza hospitalization rates from 2012 to 2020 in the US) to estimate those parameters. In forward projections, we simulated low influenza activity in 2020-21 season by lowering transmission rate by 20%. Compared to the counterfactual case, in which influenza activity remained at the normal level in 2020-21, we estimated the change in the number of hospitalizations in the following seasons with varying level of vaccine uptake and effectiveness. We measured the change in population immunity over time by varying the number of seasons with low influenza activity.ResultWith the low influenza activity in 2020-21, the model estimated 102,000 [95% CI: 57,000-152,000] additional hospitalizations in 2021-22, without change in vaccine uptake and effectiveness. The expected change in hospitalization varied depending on the level of vaccine uptake and effectiveness in the following year. Achieving 50% increase in one of two measures (1.5X vaccine uptake with 1X vaccine efficacy or 1.5X vaccine efficacy with 1X vaccine uptake) was necessary to avert the expected increase in hospitalization in the next influenza season. Otherwise, increases in both measures by 25% averted the expected increase in influenza-hospitalization. If the low influenza activity seasons continue, population immunity would remain low during those seasons, with 48% the population susceptible to influneza infection.ConclusionWe predicted a large compensatory influenza season in 2021-2 due to a light season in 2020-21. However, higher influenza vaccine uptake would reduce this projected increase in influenza.
Identifying SNP associations and predicting disease risk from Genome-wide association studies using LassoNet
Authors: Sajwani Hussain M. , Feng Samuel F. 
Published: August 2021

AbstractIn this paper, we show that under certain conditions LassoNet [1] should outperform threshold of significance (P-value) methods for identifying multi-SNP disease associations and predicting disease risk using data from Genome Wide Association Studies. To demonstrate this, we built a genotype-phenotype simulation to comprehensively benchmark each method’s performance in variant selection and in predicting disease risk. Our results suggest that LassoNet, with its ease of implementation, should be added to the biomedical informatician’s toolkit. We release code to replicate our results.
Adding context to the pneumococcal core genes – a bioinformatic analysis of the intergenic pangenome of Streptococcus pneumoniae
Authors: Nielsen Flemming Damgaard , Møller-Jensen Jakob , Jørgensen Mikkel Girke 
Published: August 2021

AbstractWhole genome sequencing offers great opportunities for linking genotypes to phenotypes aiding in our understanding of human disease and bacterial pathogenicity. However, these analyses often overlook non-coding intergenic regions (IGRs). By disregarding the IGRs, crucial information is lost, as genes have little biological function without expression. In this study, we present the first complete pangenome of the important human pathogen Streptococcus pneumoniae (pneumococcus), spanning both the genes and IGRs. We show that the pneumococcus species retains a small core genome of IGRs that are present across all isolates. Gene expression is highly dependent on these core IGRs, and often several copies of these core IGRs are found across each genome. Core genes and core IGRs show a clear linkage as 81% of core genes are associated with core IGRs. Additionally, we identify a single IGR within the core genome that is always occupied by one of two highly distinct sequences, scattered across the phylogenetic tree. Their distribution indicates that this IGR is transferred between isolates through horizontal regulatory transfer independent of the flanking genes and that each type likely serves different regulatory roles depending on their genetic context.
Interactions between RNA m6A modification, alternative splicing, and poly(A) tail revealed by MePAIso-seq2
Authors: Liu Yusheng , Zhang Yiwei , Lu Falong , Wang Jiaqiang 
Published: August 2021

AbstractRNA post-transcriptional regulation involves 5′-end capping, 3′ poly(A) tailing (including polyadenylation sites, tail length, and non-A residues1, 2), alternative splicing, and chemical modifications including N6-methyladenosine (m6A)3. Studying the interplay of m6A, alternative splicing, alternative polyadenylation sites, poly(A) tail length, and non-A residues in poly(A) tails requires monitoring them simultaneously on one transcript, however strategies to achieve this are lacking. Therefore, we developed a new method, combining m6A-specific methylated RNA immunoprecipitation and the PacBio-based, tail-included, full-length RNA sequencing approach PAIso-seq2, which we’ve named m6A and poly(A) inclusive RNA isoform sequencing 2 (MePAIso-seq2). Using MePAIso-seq2, we revealed that m6A promotes and inhibits a similar number of alternative splicing events in mouse cell lines, showing that m6A does affect alternative splicing. In contrast, no correlation was detected between m6A and alternative polyadenylation sites choice. Surprisingly, we found that m6A-modified RNAs possess longer poly(A) tails and a lower proportion of poly(A) tails containing non-A residues, especially in mouse embryonic stem cells. Together, we developed a new method to detect full-length m6A-modified RNAs to comprehensively study the relationships between m6A, alternative splicing, and poly(A) tailing, laying a foundation for further exploration of the functional coordination of different RNA post-transcriptional modifications.
The OPAQUE1/DISCORDIA2 myosin XI is required for phragmoplast guidance during asymmetric cell division in maize
Authors: Nan Qiong , Liang Hong , Mendoza Janette , Liu Le , Fulzele Amit , Wright Amanda , Bennett Eric J , Rasmussen Carolyn G. , Facette Michelle R 
Published: August 2021

AbstractFormative asymmetric divisions produce cells with different fates and are critical for development. We show the myosin XI protein, OPAQUE1 (O1), is necessary for asymmetric divisions during maize stomatal development. We analyzed stomatal precursor cells prior to and during asymmetric division to determine why o1 mutants have abnormal division planes. Cell polarization and nuclear positioning occur normally in the o1 mutant, and the future site of division is correctly specified. The defect in o1 occurs during late cytokinesis, when the phragmoplast forms the nascent cell plate. Initial phragmoplast guidance in o1 is correct; however, as phragmoplast expansion continues o1 phragmoplasts become misguided. To understand how O1 contributes to phragmoplast guidance, we identified O1-interacting proteins. Maize kinesins related to the Arabidopsis thaliana division site markers PHRAGMOPLAST ORIENTING KINESINs (POKs), which are also required for correct phragmoplast guidance, physically interact with O1. We propose that different myosins are important at multiple steps of phragmoplast expansion, and the O1 actin motor and POK-like microtubule motors work together to ensure correct late-stage phragmoplast guidance.