bluuebunny/crossref_metadata_2025_split huggingface dataset:
Escalation to intensive care for the older patient. An exploratory qualitative study of patients aged 65 years and older and their next of kin during the COVID-19 pandemic: the ESCALATE study
Authors: Warner Bronwen E , Harry Alice , Wells Mary , Brett Stephen J , Antcliffe David B 
Published: April 2023

Abstract

Background
older people comprise the majority of hospital medical inpatients so decision-making regarding admission of this cohort to the intensive care unit (ICU) is important. ICU can be perceived by clinicians as overly burdensome for patients and loved ones, and long-term impact on quality of life considered unacceptable, effecting potential bias against admitting older people to ICU. The COVID-19 pandemic highlighted the challenge of selecting those who could most benefit from ICU.

Objective
this qualitative study aimed to explore the views and recollections of escalation to ICU from older patients (aged ≥ 65 years) and next of kin (NoK) who experienced a COVID-19 ICU admission.

Setting
the main site was a large NHS Trust in London, which experienced a high burden of COVID-19 cases.

Subjects
30 participants, comprising 12 patients, 7 NoK of survivor and 11 NoK of deceased.

Methods
semi-structured interviews with thematic analysis using a framework approach.

Results
there were five major themes: inevitability, disconnect, acceptance, implications for future decision-making and unique impact of the COVID-19 pandemic. Life was highly valued and ICU perceived to be the only option. Prior understanding of ICU and admission decision-making explanations were limited. Despite benefit of hindsight, having experienced an ICU admission and its consequences, most could not conceptualise thresholds for future acceptable treatment outcomes.

Conclusions
in this study of patients ≥65 years and their NoK experiencing an acute ICU admission, survival was prioritised. Despite the ordeal of an ICU stay and its aftermath, the decision to admit and sequelae were considered acceptable.
A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India
Authors: Hitchings Matt D T , Patel Eshan U , Khan Rifa , Srikrishnan Aylur K , Anderson Mark , Kumar K S , Wesolowski Amy P , Iqbal Syed H , Rodgers Mary A , Mehta Shruti H , Cloherty Gavin , Cummings Derek A T , Solomon Sunil S 
Published: April 2023

Abstract
Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers’ cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.
CREPE: a Shiny app for transcription factor cataloguing
Authors: Rosado-Tristani Diego A , Rodríguez-Martínez José A 
Published: April 2023

Abstract

Summary
Transcription factors (TFs) are proteins that directly interpret the genome to regulate gene expression and determine cellular phenotypes. TF identification is a common first step in unraveling gene regulatory networks. We present CREPE, an R Shiny app to catalogue and annotate TFs. CREPE was benchmarked against curated human TF datasets. Next, we use CREPE to explore the TF repertoires of Heliconius erato and Heliconius melpomene butterflies.

Availability and implementation
CREPE is available as a Shiny app package available at GitHub (github.com/dirostri/CREPE).

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
Deciphering associations between gut microbiota and clinical factors using microbial modules
Authors: Wang Ran , Zheng Xubin , Song Fangda , Wong Man Hon , Leung Kwong Sak , Cheng Lixin 
Published: April 2023

Abstract

Motivation
Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe–microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa.

Results
We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe–microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules.

Availability and implementation
https://github.com/rwang-z/microbial_module.git.
spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
Authors: Boniolo Fabio , Hoffmann Markus , Roggendorf Norman , Tercan Bahar , Baumbach Jan , Castro Mauro A A , Robertson A Gordon , Saur Dieter , List Markus 
Published: April 2023

Abstract

Motivation
Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene–miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity.

Results
We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking.

Availability and implementation
https://bioconductor.org/packages/devel/bioc/html/SPONGE.html.
Effective design and inference for cell sorting and sequencing based massively parallel reporter assays
Authors: Gilliot Pierre-Aurélien , Gorochowski Thomas E 
Published: April 2023

Abstract

Motivation
The ability to measure the phenotype of millions of different genetic designs using Massively Parallel Reporter Assays (MPRAs) has revolutionized our understanding of genotype-to-phenotype relationships and opened avenues for data-centric approaches to biological design. However, our knowledge of how best to design these costly experiments and the effect that our choices have on the quality of the data produced is lacking.

Results
In this article, we tackle the issues of data quality and experimental design by developing FORECAST, a Python package that supports the accurate simulation of cell-sorting and sequencing-based MPRAs and robust maximum likelihood-based inference of genetic design function from MPRA data. We use FORECAST’s capabilities to reveal rules for MPRA experimental design that help ensure accurate genotype-to-phenotype links and show how the simulation of MPRA experiments can help us better understand the limits of prediction accuracy when this data are used for training deep learning-based classifiers. As the scale and scope of MPRAs grows, tools like FORECAST will help ensure we make informed decisions during their development and the most of the data produced.

Availability and implementation
The FORECAST package is available at: https://gitlab.com/Pierre-Aurelien/forecast. Code for the deep learning analysis performed in this study is available at: https://gitlab.com/Pierre-Aurelien/rebeca.
Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy
Authors: Royer Jessica , Larivière Sara , Rodriguez-Cruces Raul , Cabalo Donna Gift , Tavakol Shahin , Auer Hans , Ngo Alexander , Park Bo-yong , Paquola Casey , Smallwood Jonathan , Jefferies Elizabeth , Caciagli Lorenzo , Bernasconi Andrea , Bernasconi Neda , Frauscher Birgit , Bernhardt Boris C 
Published: April 2023

AbstractTemporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management.Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization.Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy.Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
Why people engage in corrupt collaboration: an observation at the multi-brain level
Authors: Zhang Dandan , Zhang Shen , Lei Zhen , Li Yiwei , Li Xianchun , Gu Ruolei 
Published: April 2023

Abstract
Recent studies suggest that corrupt collaboration (i.e. acquiring private benefits with joint immoral acts) represents a dilemma between the honesty and reciprocity norms. In this study, we asked pairs of participants (labeled as A and B) to individually toss a coin and report their outcomes; their collective benefit could be maximized by dishonestly reporting (a corrupt behavior). As expected, the likelihood of corrupt behavior was high; this probability was negatively correlated with player A’s moral judgment ability but positively correlated with player B’s empathic concern (EC). Functional near-infrared spectroscopy data revealed that the brain-to-brain synchronization in the right dorsolateral prefrontal cortex was associated with fewer corrupt behaviors, and that it mediated the relationship between player A’s moral judgment ability and corrupt collaboration. Meanwhile, the right temporal–parietal junction synchronization was associated with more corrupt behaviors, and that it mediated the relationship between player B’s EC and corrupt collaboration. The roles of these 2 regions are interpreted according to the influence of the honesty and reciprocity norms on corrupt collaboration. In our opinion, these findings provide insight into the underlying mechanisms and modulating factors of corrupt collaboration.
A multidisciplinary approach to re-intervention in an Ebstein patient: a case report
Authors: Pius Charlene , Aggarwal Suneil , Rao Archana , Ashrafi Reza 
Published: April 2023

Abstract

Background
Ebstein’s anomaly is a rare condition due to incomplete delamination of the tricuspid valve (TV) leaflets with downward displacement of the proximal leaflet attachments. It is associated with a smaller functional right ventricle (RV) and tricuspid regurgitation (TR) that is typically treated with TV replacement or repair. However, future re-intervention poses challenges. We describe a multidisciplinary team approach to re-intervention in a pacing-dependant Ebstein patient with severe bioprosthetic TV regurgitation.

Case summary
A 49-year-old female patient underwent bioprosthetic TV replacement for severe TR in Ebstein’s. Post-operatively, she developed complete atrioventricular (AV) block necessitating the implantation of a permanent pacemaker which included a coronary sinus (CS) lead as the ventricular lead. Five years later, she presented with syncope due to a failing ventricular pacing lead, and a new RV lead was positioned across the TV bioprosthesis due to the lack of CS options. Two years later, she presented with breathlessness and lethargy with severe TR identified on transthoracic echocardiography. She successfully underwent a percutaneous leadless pacemaker implant, extraction of existing pacing system, and implantation of valve-in-valve TV.

Discussion
Patients with Ebstein’s anomaly typically undergo TV repair or replacement. Following surgical intervention, owing to the anatomical location, patients can develop AV block requiring a pacemaker. Pacemaker implantation may involve a CS lead to avoid placing a lead across the new TV in efforts to avoid lead induced TR. Over time, these patients not uncommonly require re-intervention that can be challenging especially in pacing-dependant patients with leads across the TV.
Rapid virtual fractional flow reserve using 3D computational fluid dynamics
Authors: Newman Thomas , Borker Raunak , Aubiniere-Robb Louise , Hendrickson Justin , Choudhury Dipankar , Halliday Ian , Fenner John , Narracott Andrew , Hose D Rodney , Gosling Rebecca , Gunn Julian P , Morris Paul D 
Published: April 2023

Abstract

Aims
Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.

Methods and results
This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0–44.4s) vs. 607.5 s (490–964 s), P &lt; 0.0001). The novel GPU technique was 99.6% (IQR 99.3–99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80–13.39) Wh vs. 2.60 (2.16–3.12) Wh, P &lt; 0.0001).

Conclusion
This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.
A machine learning approach to predicting 30-day mortality following paediatric cardiac surgery: findings from the Australia New Zealand Congenital Outcomes Registry for Surgery (ANZCORS)
Authors: Betts Kim S , Marathe Supreet P , Chai Kevin , Konstantinov Igor , Iyengar Ajay , Suna Jessica ,   ANZCORS Collaborative, Andrews David , Brink Johann , Brizard Christian , Finucane Kirsten , d’Udekem Yves , Karl Tom R , Liava’a Matt , Orr Yishay , Venugopal Prem , Alphonso Nelson 
Published: April 2023

Abstract

OBJECTIVES
We aim to develop the first risk prediction model for 30-day mortality for the Australian and New Zealand patient populations and examine whether machine learning (ML) algorithms outperform traditional statistical approaches.

METHODS
Data from the Australia New Zealand Congenital Outcomes Registry for Surgery, which contains information on every paediatric cardiac surgical encounter in Australian and New Zealand for patients aged &lt;18 years between January 2013 and December 2021, were analysed (n = 14 343). The outcome was mortality within the 30-day period following a surgical encounter, with ∼30% of the observations randomly selected to be used for validation of the final model. Three different ML methods were used, all of which employed five-fold cross-validation to prevent overfitting, with model performance judged primarily by the area under the receiver operating curve (AUC).

RESULTS
Among the 14 343 30-day periods, there were 188 deaths (1.3%). In the validation data, the gradient-boosted tree obtained the best performance [AUC = 0.87, 95% confidence interval = (0.82, 0.92); calibration = 0.97, 95% confidence interval = (0.72, 1.27)], outperforming penalized logistic regression and artificial neural networks (AUC of 0.82 and 0.81, respectively). The strongest predictors of mortality in the gradient boosting trees were patient weight, STAT score, age and gender.

CONCLUSIONS
Our risk prediction model outperformed logistic regression and achieved a level of discrimination comparable to the PRAiS2 and Society of Thoracic Surgery Congenital Heart Surgery Database mortality risk models (both which obtained AUC = 0.86). Non-linear ML methods can be used to construct accurate clinical risk prediction tools.
Harmonious relations: quality transmission among composers in the very long run
Authors: Borowiecki Karol Jan , Ford Nicholas Martin , Marchenko Maria 
Published: April 2023

Abstract
Most creative professionals develop and refine their talents by learning from others. In most empirical settings, estimating how this learning process fosters quality is challenging. This paper explores the transmission of quality among music composers over more than seven centuries. How does a composer’s quality influence the quality of the composers they teach? Using a unique dataset of 17,433 composers, we show a strong relationship between student and teacher quality. Moreover, this quality transmission persists across multiple generations. Our results provide new insights on drivers of creativity, as well as the influence of teachers on students’ achievements.
Treatment of multiple valve disease: surgery, structural intervention, or both?
Authors: Berti Sergio , Bonanni Michela , D’agostino Andreina , Celi Simona , Mariani Massimiliano 
Published: April 2023

Abstract
Multiple valve heart disease refers to the simultaneous presence of several valvular anomalies, while mixed valvular heart disease refers to the combination of stenotic lesions or regurgitation affecting the same valve. The pathophysiology of multiple and mixed valvular heart disease depends on the combination of affected valves and the severity of the individual valvular defects. Imaging is essential for diagnosis and assessment of disease severity. The treatment of combined valvular defects currently represents a challenge for both cardiac surgeons and interventional cardiologists since only few data in the literature address the clinical and therapeutic decision-making process related to these complex lesions. These are heterogeneous conditions, which differ from each other in the combination of the valves involved, in the pathogenesis with which they develop, in the severity of the individual lesions, in the associated surgical risk, in the possibility of being repaired, and in the undergoing transcatheter treatments. In addition to the severity of the individual valve defects, the choice of treatment also depends on the ventricular function and the general condition of the patient. This work aims to provide a review of the state of the art regarding the possible management strategies of the most common multiple valve diseases in clinical practice.
Predicting left ventricular functional recovery in ischaemic cardiomyopathy: needs and challenges
Authors: Radesich Cinzia , Cappelletto Chiara , Indennidate Carla , Perotto Maria , Di Lenarda Andrea 
Published: April 2023

Abstract
Left ventricular (LV) systolic function is an essential parameter for the evaluation of patients with ischaemic heart disease, and therapeutic choices are significantly driven by LV ejection fraction (LVEF) in the early stage of the disease and during follow-up. After an acute coronary syndrome, ventricular dysfunction may be reversible when caused by transient myocardial stunning. Therefore, the identification of clinical, laboratory, and instrumental predictors of improvement in LV systolic function (in addition to LVEF) is essential for an adequate prognostic stratification. In the setting of chronic ischaemic heart disease, there is no evidence that an improvement in LV systolic function is invariably associated with a better prognosis and LVEF is only one of many parameters that should be considered for the risk stratification. This state-of-the-art review will critically analyse the scientific evidence regarding known predictors of LVEF recovery, trying to elucidate their pathophysiological principles and clinical value.