bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Preliminary Study on Investigation of Blood–Brain Barrier Damage Markers in Patients with Alcohol Use Disorder Before and After Therapy
Authors: Cetin Ihsan , Yazla Ece , Akmese Bediha , Kayadibi Hüseyin 
Published: August 2022

Abstract

Aim
The use of alcohol affects the central nervous system and plays important roles in various neurological disorders through neurotoxicity resulting from blood–brain barrier (BBB) permeability. The BBB is regulated by tight junction proteins interacting closely with endothelial cells. This study evaluated the serum levels of proteins and spectrin degradation products associated with BBB damage in patients with alcohol use disorder.

Methods
This preliminary case–control study was conducted with 30 healthy volunteers and 26 alcohol use disorder patients. The serum levels of spectrin breakdown product 145 (SBDP145), spectrin breakdown product 150 (SBDP150), ubiquitin carboxy-terminal hydrolase L1 (UCHL1), ubiquitin ligase cullin-3 (ULC), occludin and claudin were measured with enzyme-linked immunosorbent assay.

Results
There was no significant difference between the levels of SBDP145, SBDP150, UCHL1, ULC, occludin and claudin before and after treatment in patients with alcohol use disorder. SBDP150 levels were significantly lower in patients than controls (P &lt; 0.001). The area under the curve was 0.841 (0.733–0.949) with the 95% confidence interval for SPDP150.

Conclusion
A decrease of the serum SBDP150 levels appears to be associated with alcohol use disorder. Future studies might clarify whether diminished serum SBDP150 levels are associated with BBB damage in patients with alcohol use disorder.
The polyketide synthase StlA is involved in inducing aggregation in Polysphondylium violaceum
Authors: Yamasaki Daiki T , Araki Tsuyoshi , Narita Takaaki B 
Published: August 2022

ABSTRACT
In the social amoeba Dictyostelium discoideum, the polyketide MPBD (4-methyl-5-pentylbenzene-1,3-diol) regulates the gene expressions of cAMP signaling to make cells aggregation-competent and also induces spore maturation. The polyketide synthase StlA is responsible for MPBD biosynthesis in D. discoideum and appears to be conserved throughout the major groups of the social amoeba (Dictyostelia). In this study, we analyzed the function of StlA in Polysphondylium violaceum by identifying the gene sequence and creating the knockout mutants. We found that Pv-stlA– mutants had defects only in cell aggregation but not in spore maturation, indicating that the function of StlA in inducing spore maturation is species-specific. We also found that MPBD could rescue the aggregation defect in Pv-stlA– mutants whereas the mutants normally exhibited chemotaxis to their chemoattractant, glorin. Our data suggest that StlA is involved in inducing aggregation in P. violaceum by acting on signaling pathways other than chemotaxis in P. violaceum.
CellDrift: inferring perturbation responses in temporally sampled single-cell data
Authors: Jin Kang , Schnell Daniel , Li Guangyuan , Salomonis Nathan , Prasath V B Surya , Szczesniak Rhonda , Aronow Bruce J 
Published: August 2022

AbstractCells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.
NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction
Authors: Yi Ren , Cho Kyunghyun , Bonneau Richard 
Published: August 2022

Abstract

Motivation
Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.

Results
We show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.

Availability and implementation
NetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.

Supplementary information
Supplementary data are available at Bioinformatics online.
‘Listening from a Personal Perspective’: Does Co-Designed Mental Health Education Shift Stigma? A Mixed Method Evaluation Study
Authors: Whitelaw Kerry , Seubert Liza , Lee Kenneth , Etherton-Beer Christopher , Clifford Rhonda , Sheers Carli , Loveny Julie , Brand Gabrielle 
Published: August 2022

AbstractMental health stigma by health care practitioners is well established with few education interventions implemented in Australia. Depth of field is a growing body of health humanities research that draws on healthcare consumers as legitimate experts (by experience) to co-design health professions education.This research aimed to explore whether a co-designed (with mental health consumers) education intervention shifts knowledge, attitudes and behavioural intentions of current and future health care practitioners towards caring for people who have experienced mental health issues.A convergent parallel mixed method design was used to collect data utilising a validated anti-stigma assessment tool at two time points pre- and post-intervention (n = 80) followed by qualitative data from semi-structured interviews (n = 10). Descriptive analysis, Wilcoxon Sign-Rank test to compare pre-post responses and interview data were thematically analysed.Collectively, data showed positive shifts in knowledge, attitudes and behavioural intentions towards people who have experienced mental health issues. Three main themes: Making connections: The power of storytelling; Knowledge and attitudes towards Borderline Personality Disorder: Shape, strengthen, challenge; and Inspiring change in health care practice.Co-designed education can positively impact health care practitioners’ knowledge, attitudes and behavioural intentions and has the potential to reframe mental health education toward recovery-oriented practice.
Skin biomechanics: a potential therapeutic intervention target to reduce scarring
Authors: Hosseini Motaharesadat , Brown Jason , Khosrotehrani Kiarash , Bayat Ardeshir , Shafiee Abbas 
Published: August 2022

Abstract
Pathological scarring imposes a major clinical and social burden worldwide. Human cutaneous wounds are responsive to mechanical forces and convert mechanical cues to biochemical signals that eventually promote scarring. To understand the mechanotransduction pathways in cutaneous scarring and develop new mechanotherapy approaches to achieve optimal scarring, the current study highlights the mechanical behavior of unwounded and scarred skin as well as intra- and extracellular mechanisms behind keloid and hypertrophic scars. Additionally, the therapeutic interventions that promote optimal scar healing by mechanical means at the molecular, cellular or tissue level are extensively reviewed. The current literature highlights the significant role of fibroblasts in wound contraction and scar formation via differentiation into myofibroblasts. Thus, understanding myofibroblasts and their responses to mechanical loading allows the development of new scar therapeutics. A review of the current clinical and preclinical studies suggests that existing treatment strategies only reduce scarring on a small scale after wound closure and result in poor functional and aesthetic outcomes. Therefore, the perspective of mechanotherapies needs to consider the application of both mechanical forces and biochemical cues to achieve optimal scarring. Moreover, early intervention is critical in wound management; thus, mechanoregulation should be conducted during the healing process to avoid scar maturation. Future studies should either consider combining mechanical loading (pressure) therapies with tension offloading approaches for scar management or developing more effective early therapies based on contraction-blocking biomaterials for the prevention of pathological scarring.
Deaf Student–Interpreter Relationships and Feedback Practices in K-12 Mainstream Deaf Education
Authors: Prinzi Lisa M 
Published: August 2022

Abstract
This study explores Deaf individuals’ and sign language interpreters’ perspectives on what it is like to work together in K-12 education. A group of 41 formerly mainstreamed Deaf individuals and interpreters offers insights into interactional dynamics (e.g., the deaf student–interpreter relationship) that influence interpreters’ work, deaf students’ participation and learning, and feedback practices. This study illustrates the significance of the deaf student–interpreter relationship and suggests a correlation between this relationship and deaf students’ participation and positioning in mainstream classrooms. In addition, the findings suggest a correlation between the deaf student–interpreter relationship and feedback practices in mainstream education. Finally, this study finds that deaf students do not always know that they can engage in feedback conversations with interpreters or know how to engage interpreters to ask for what they need to learn. This article concludes with considerations and recommendations for the deaf education and interpreter education communities.
Speeding up cardiac MR segmentation with semi-supervision: applications in cine imaging
Authors: Hooper S , Wu S , Davies R H , Moon J C , Kellman P , Xue H , Langlotz C , Re C 
Published: August 2022

Abstract

Funding Acknowledgements
Type of funding sources: Private company. Main funding source(s): This material is based upon work supported by the Google Cloud Research Credits program with the award GCP19980904.

Background
Segmentation is an important postprocessing step in cardiac magnetic resonance (CMR) imaging that enables quantitative assessment of functional parameters. Deep learning can automate the segmentation process, producing accurate contours of cardiac structures while reducing the time required to analyze images and the interobserver variation compared to manual analysis. However, common approaches to training neural networks (NNs) require large amounts of labeled data, which is costly to generate and slows down the development of CMR segmentation NNs for new applications. Semi-supervision is an approach to alleviate this labeling burden by relying on abundant unlabeled data and a smaller amount of labeled data to train NNs.

Purpose
We propose a novel semi-supervised method to train CMR segmentation NNs. We use the proposed method to train NNs to segment the left ventricle in CMR cine images. Ultimately, we aim to show that semi-supervision can drastically reduce the amount of labeled data required to develop machine learning segmentation applications for CMR while maintaining high performance.

Methods
Our dataset consists of 1,208 short-axis cine CMR images and 1,244 long-axis cine CMR images. An expert annotator manually segmented the endocardium on the end-diastolic and end-systolic short-axis and long-axis images and the epicardium on the end-diastolic short-axis images. We split the dataset randomly by patient into 60% training, 20% validation, and 20% testing data. We train semi-supervised segmentation networks using a supervised cross-entropy loss to learn from the labeled training data and a cosine embedding loss in addition to a pseudo-labeling step to learn from the unlabeled training data. To evaluate how performance changes with different amounts of labeled training data, we vary the percent of training data that has labels from &lt;1%-100%. We evaluate the predicted segmentation masks using the Dice coefficient.

Results
Using only 100 labeled image slices, the semi-supervised segmentation NNs achieve a mean Dice coefficient within 1.10% of networks trained with fully labeled training sets, corresponding to &gt;85% reduction in required labeled training data (Table 1). The proposed semi-supervised method improves performance over naïve training by 6.21% for the most limited labeled data setting (i.e., 10 labeled image slices; Figure 1).

Conclusion
We have shown that NNs trained with limited labeled data achieve high performance on left ventricle segmentation in short-axis and long-axis CMR cines. The proposed approach is flexible and broadly applicable to different CMR segmentation tasks, enabling rapid development of segmentation networks for many cardiac structures and applications. Table 1Figure 1
Improving the diagnostic accuracy of apical hypertrophic cardiomyopathy using machine learning
Authors: Shiwani H , Hughes R K , Camaioni C , Augusto J B , Knott K , Rosmini S , Khoury S , Malcolmson J , Kellman P , Xue H , Burke L , Pierce I , Moon J C , Davies R H 
Published: August 2022

Abstract

Funding Acknowledgements
Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Dr Hughes is supported by the British Heart Foundation (grant number FS/17/82/33222).

Introduction
The imaging criteria for diagnosis of apical hypertrophic cardiomyopathy (ApHCM) is a maximum wall thickness (MWT) ≥15mm. CMR enables detection of subtle phenotypic features (e.g. loss of apical tapering, cavity obliteration) and coupled with characteristic electrocardiogram changes, ApHCM can be diagnosed without overt hypertrophy. However, these patients are not captured by current diagnostic criteria.

Purpose
We sought to use a machine learning tool to quantify wall thickness and identify patients with ‘relative’ ApHCM that do not reach current diagnostic thresholds.

Methods
CMR images from 4118 healthy participants from the UK Biobank were segmented automatically with a clinically validated machine learning algorithm and wall thickness measured at all point in the myocardium by solving a solution to Laplace’s equation. MWT were pooled into 16 AHA segments and indexed to body surface area (BSA). The non-indexed and indexed segmental upper limit of normal was calculated as the mean + 3 standard deviations (the equivalent of 95% confidence interval after correcting for multiple [16] comparisons using the Bonferroni method).

Results
73 overt ApHCM subjects (MWT&gt;15mm) and 31 relative ApHCM subjects underwent CMR scanning. In healthy controls, the non-indexed (and indexed) upper limits were calculated for the apical-anterior 10.2mm (5.2 mm/m2), apical-septal 11.1mm (5.6 mm/m2), apical-inferior 10.5mm (5.3 mm/m2) and apical-lateral 10.1mm (5.2 mm/m2) segments. With a non-indexed cut-off, all (73 of 73) overt ApHCM and 84% (26 of 31) relative ApHCM were classified as having an abnormally thick apex. 3% (127 of 4118) of the healthy UK Biobank cohort were classified as abnormal, as expected. Using an indexed cut-off, all overt ApHCM and 87% (27/31) relative ApHCM were classified as abnormal, and 3% (123 of 4118) of the healthy UK Biobank cohort were misclassified.

Conclusion
We can successfully classify 87% of relative ApHCM patients from a normative reference range derived from a large cohort of healthy patients – a significant improvement on existing methods. We show that the specificity and sensitivity is increased when MWT is indexed to BSA. For practical clinical application, we recommend a cut-off of 10mm or an indexed cut-off of 5mm/m2 in any apical segment to diagnose apical LVH. Overt and relative apical HCM examplesHealthy controls AHA maps (non-indexed)
Adverse events prediction in nonischemic dilated cardiomyopathy by two machine learning models integrating clinical and cardiovascular magnetic resonance data
Authors: Coriano' M , Bray G , De Micheli L , Cecere A G , Perazzolo Marra M , Lanera C , Gregori D , Tona F 
Published: August 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
Risk stratification of patients with nonischemic dilated cardiomyopathy (NIDCM) is challenging. Machine learning (ML) models represent novel technologic tools that promise to improve predictive ability of cardiovascular adverse outcomes.

Purpose
To implement two ML models to predict the risk of adverse outcome (composite endpoint of all-cause death, heart transplantation, implantation of ventricular assistant device), in patients with NIDCM using clinical and radiological data.

Methods
We retrospective collected data from patients affected by NIDCM referred to our cardiologic unit between 2002 and 2018. Two ML models, an extreme gradient boosting machine (XGBoost) and an artificial neural network (ANN), were developed using cardiovascular magnetic resonance (CMR) and clinical parameters. Both models were trained on 67% of patients and evaluated based on the other 33% as internal validation. To investigate the contribution of different features in our models, we used Shapley additive explanations (SHAP) analysis.

Results
The final cohort resulted of 161 patients (mean age 48.9±13.6 years, 71% males), with a mean follow up duration of 79.1 months. During the follow-up 29 adverse event were observed (16 deaths, 12 heart transplantation, 1 implantation of ventricular assistant device). ANN model resulted in an accuracy of 76% with an area under the receiver operating characteristic (ROC) curve of 0.66; SHAP analysis identified male sex, percentage of late gadolinium enhancement (LGE) and LGE distribution in the lateral wall of myocardium as the main markers of adverse outcomes. XGBoost model resulted in an accuracy of 78% with a ROC curve of 0.76; SHAP analysis identified NT-proBNP level, age and serum creatinine mainly related with adverse outcomes. About CMR parameters, left ventricular diameter and ejection fraction better related with adverse outcomes.

Conclusions
Our models achieved a good performance in prediction of adverse events, suggesting that ML could improve our ability of risk stratification of NIDCM. XGBoost better performed than ANN, and SHAP analysis suggested that both CMR parameters and clinical parameters contribute to the definition of risk. ANN performance and SHAP analysisXGBoost performance and SHAP analysis
Talent Discovery and Poaching Under Asymmetric Information
Authors: Ferreira Daniel , Nikolowa Radoslawa 
Published: August 2022

Abstract
We develop a model of the market for knowledge workers in which talent is discovered on the job. In the model, asymmetric information and firm-specific human capital combine to generate several predictions relating firm heterogeneity to talent discovery and poaching. We show that high-quality (i.e., large and high-productivity) firms are more likely to become talent poachers, while lower-quality firms are more likely to invest in talent discovery. Job-to-job flows are adversely selected, which implies that internally promoted managers are more productive than those who are externally promoted. The model generates several additional predictions linking firm heterogeneity to the distribution of managerial talent, productivity, compensation and promotions.
The effectiveness and usability of a novel mobile phone-based self-care intervention for patients with heart failure: a mixed-methods pilot study
Authors: Son Youn-Jung , Kim Eun Young 
Published: August 2022

AbstractAimsTo evaluate the preliminary effectiveness and usability of a novel mobile phone-based self-care intervention (named WithUs) for patients with heart failure (HF).Methods and resultsWe conducted a mixed-methods pilot study with group pre-test–post-test design and usability testing. Participants were 26 Korean HF patients (20 men; median age 62 years) who took part in this study after hospital discharge. Data were collected at baseline and after the 24-week intervention, using questionnaires and interviews. The outcome variables were eHealth literacy, disease knowledge, and self-care behaviours. The Wilcoxon signed-rank test was used to compare the pre- and post-intervention results. Thematic analysis was used for analysing qualitative data. The WithUs programme improved the eHealth literacy (P = 0.005), disease knowledge (P &lt; 0.001), and self-care behaviours (P &lt; 0.001) of patients with HF. The median score of total app usability was 4.1 out of 5 (interquartile range = 4.0–5.0). Four themes were identified which complemented the quantitative results. These were increased awareness of the importance of routine self-care behaviours, enhanced motivation toward self-care behaviours, challenges adapting to the new app, and suggestions for improving app adoption.ConclusionThe app was effective in improving adherence to self-care behaviours, eHealth literacy, and disease knowledge in patients with HF. The usability evaluation results showed that the app is usable and has the potential to support HF patients in self-care. However, the app needs to be modified to improve its adoption.