bluuebunny/crossref_metadata_2025_split huggingface dataset:
MiRNA–gene network embedding for predicting cancer driver genes
Authors: Peng Wei , Wu Rong , Dai Wei , Ning Yu , Fu Xiaodong , Liu Li , Liu Lijun 
Published: February 2023

Abstract
The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene–gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene–miRNA network. First, we constructed a gene–miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.
High-accuracy protein model quality assessment using attention graph neural networks
Authors: Zhang Peidong , Xia Chunqiu , Shen Hong-Bin 
Published: February 2023

AbstractGreat improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.
Improved drug response prediction by drug target data integration via network-based profiling
Authors: Pak Minwoo , Lee Sangseon , Sung Inyoung , Koo Bonil , Kim Sun 
Published: February 2023

AbstractDrug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson’s correlation coefficient.
HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer
Authors: Yuan Le , Lu Hongzhong , Li Feiran , Nielsen Jens , Kerkhoven Eduard J 
Published: February 2023

AbstractBackgroundHorizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species.ResultsIn this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species.ConclusionsThe HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.
Improving drug–drug interactions prediction with interpretability via meta-path-based information fusion
Authors: Zhao Weizhong , Yuan Xueling , Shen Xianjun , Jiang Xingpeng , Shi Chuan , He Tingting , Hu Xiaohua 
Published: February 2023

AbstractDrug–drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.
MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits
Authors: Liang Mang , Cao Sheng , Deng Tianyu , Du Lili , Li Keanning , An Bingxing , Du Yueying , Xu Lingyang , Zhang Lupei , Gao Xue , Li Junya , Guo Peng , Gao Huijiang 
Published: February 2023

AbstractIncorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.
Cognitive biases in surgery: systematic review
Authors: Armstrong Bonnie A , Dutescu Ilinca A , Tung Arthur , Carter Diana N , Trbovich Patricia L , Wong Sherman , Saposnik Gustavo , Grantcharov Teodor 
Published: February 2023

Abstract

Background
Although numerous studies have established cognitive biases as contributors to surgical adverse events, their prevalence and impact in surgery are unknown. This review aimed to describe types of cognitive bias in surgery, their impact on surgical performance and patient outcomes, their source, and the mitigation strategies used to reduce their effect.

Methods
A literature search was conducted on 9 April and 6 December 2021 using MEDLINE, Embase, PsycINFO, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews. Included studies investigated how cognitive biases affect surgery and the mitigation strategies used to combat their impact. The National Institutes of Health tools were used to assess study quality. Inductive thematic analysis was used to identify themes of cognitive bias impact on surgical performance.

Results
Thirty-nine studies were included, comprising 6514 surgeons and over 200 000 patients. Thirty-one types of cognitive bias were identified, with overconfidence, anchoring, and confirmation bias the most common. Cognitive biases differentially influenced six themes of surgical performance. For example, overconfidence bias associated with inaccurate perceptions of ability, whereas anchoring bias associated with inaccurate risk–benefit estimations and not considering alternative options. Anchoring and confirmation biases associated with actual patient harm, such as never events. No studies investigated cognitive bias source or mitigation strategies.

Conclusion
Cognitive biases have a negative impact on surgical performance and patient outcomes across all points of surgical care. This review highlights the scarcity of research investigating the sources that give rise to cognitive biases in surgery and the mitigation strategies that target these factors.
Relative Effectiveness of Coronavirus Disease 2019 Vaccination and Booster Dose Combinations Among 18.9 Million Vaccinated Adults During the Early Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Period—United States, 1 January 2022 to 31 March 2022
Authors: Kompaniyets Lyudmyla , Wiegand Ryan E , Oyalowo Adewole C , Bull-Otterson Lara , Egwuogu Heartley , Thompson Trevor , Kahihikolo Ka‘imi , Moore Lori , Jones-Jack Nkenge , El Kalach Roua , Srinivasan Arunkumar , Messer Ashley , Pilishvili Tamara , Harris Aaron M , Gundlapalli Adi V , Link-Gelles Ruth , Boehmer Tegan K 
Published: February 2023

AbstractBackgroundSmall sample sizes have limited prior studies' ability to capture severe COVID-19 outcomes, especially among Ad26.COV2.S vaccine recipients. This study of 18.9 million adults aged ≥18 years assessed relative vaccine effectiveness (rVE) in three recipient cohorts: (1) primary Ad26.COV2.S vaccine and Ad26.COV2.S booster (2 Ad26.COV2.S), (2) primary Ad26.COV2.S vaccine and mRNA booster (Ad26.COV2.S+mRNA), (3) two doses of primary mRNA vaccine and mRNA booster (3 mRNA).MethodsWe analyzed two de-identified datasets linked using privacy-preserving record linkage (PPRL): insurance claims and retail pharmacy COVID-19 vaccination data. We assessed the presence of COVID-19 diagnosis during January 1-March 31, 2022 in: (1) any claim, (2) outpatient claim, (3) emergency department (ED) claim, (4) inpatient claim, and (5) inpatient claim with intensive care unit (ICU) admission. rVE for each outcome comparing three recipient cohorts (reference: two Ad26.COV2.S doses) was estimated from adjusted Cox proportional hazards models.ResultsCompared with two Ad26.COV2.S doses, Ad26.COV2.S+mRNA and three mRNA doses were more effective against all COVID-19 outcomes, including 57% (95% CI: 52–62) and 62% (95% CI: 58–65) rVE against an ED visit; 44% (95% CI: 34–52) and 54% (95% CI: 48–59) rVE against hospitalization; and 48% (95% CI: 22–66) and 66% (95% CI: 53–75) rVE against ICU admission, respectively.ConclusionsThis study demonstrated that Ad26.COV2.S + mRNA doses were as good as three doses of mRNA, and better than two doses of Ad26.COV2.S. Vaccination continues to be an important preventive measure for reducing the public health impact of COVID-19.
Mitral valve repair update: simple, intuitive and physiological techniques, using running sutures (part II)
Authors: Kofidis Theo 
Published: February 2023

AbstractModern mitral valve techniques are variable and, mostly, well-validated. However, they are skill dependent, and some lack of standardization, regarding the number and positioning of the cords, or the management of the targeted pathology. We continue the introduction of novel, perhaps more intuitive, aiming to simplify mitral valve repair, while achieving a reproducible result, of which some we have already verified.
Serum testosterone and sex hormone–binding globulin are inversely associated with leucocyte telomere length in men: a cross-sectional analysis of the UK Biobank study
Authors: Marriott Ross J , Murray Kevin , Budgeon Charley A , Codd Veryan , Hui Jennie , Arscott Gillian M , Beilby John P , Hankey Graeme J , Wittert Gary A , Wu Frederick C W , Yeap Bu B 
Published: February 2023

Abstract

Objective
Older men on an average have lower testosterone concentrations, compared with younger men, and more age-related comorbidities. Whether lower testosterone concentrations contribute to biological ageing remains unclear. Shorter telomeres are a marker for biological age. We tested the hypothesis that testosterone concentrations are associated with leucocyte telomere length (LTL), in middle- to older-aged men.

Design
Cross-sectional analysis of the UK Biobank study, involving community-dwelling men aged 40-69 years.

Methods
Serum testosterone and sex hormone–binding globulin (SHBG) were assayed. Free testosterone was calculated (cFT). Leucocyte telomere length was measured using polymerase chain reaction. Multivariable models were used to assess associations of hormones with standardised LTL.

Results
In 167 706 men, median age 58 years, adjusting for sociodemographic, lifestyle, and medical factors, total testosterone was inversely associated with standardised LTL, which was 0.09 longer (95% confidence interval [CI], 0.08-0.10, P &lt; .001) in men with total testosterone at median of lowest quintile [Q1] vs highest [Q5]. This relationship was attenuated after additional adjustment for SHBG (0.03 longer, CI = 0.02-0.05, P = .003). The association between cFT and LTL was similar in direction but lower in magnitude. In multivariable analysis, SHBG was inversely associated with standardised LTL, which was 0.12 longer (CI = 0.10-0.13, P &lt; .001) for SHBG at median Q1 vs Q5. Results were similar with testosterone included in the model (0.10 longer, CI = 0.08-0.12, P &lt; .001).

Conclusions
Total testosterone and SHBG were independently and inversely associated with LTL. Men with higher testosterone or SHBG had shorter telomeres, arguing against a role for testosterone to slow biological ageing in men.
Family building after diagnosis of premature ovarian insufficiency: a cross-sectional survey in 324 women
Authors: Cambray Solange , Dubreuil Sophie , Tejedor Isabelle , Dulon Jérôme , Touraine Philippe 
Published: February 2023

AbstractObjectiveThe diagnosis of premature ovarian insufficiency (POI) is a traumatic event for many patients that involves poor fertility prognosis. After such diagnosis, spontaneous pregnancies are rare. The alternatives for building a family are oocyte donation, embryo donation, and adoption. However, we have few information on how many women with POI finally built a family after the diagnosis and which alternative they chose.DesignWe performed a cross-sectional, descriptive study.MethodsWe conducted a survey of all the women who consulted for POI in the department of endocrinology and reproductive medicine at la Pitié Salpêtrière between May 31, 1991, and January 12, 2021. We included patients who continued to be followed up regularly by our department or were contacted by mail or phone between June and September 2021. We excluded patients with Turner syndrome and POI secondary to oncological treatment and patients under 18 at the time of the survey.Results985 patients were referred to the department for POI, and 324 patients were finally analyzed. 41% of the women who wanted to build a family had children after the diagnosis: 53.9% by oocyte donation, 1 woman by embryo donation, 5.6% after ovarian stimulation, 13.5% by adoption, and 25.8% who had spontaneous pregnancy after a mean time of 2.5 years. Spontaneous pregnancy rate was 8.6% in the whole cohort.ConclusionsHaving children after a diagnosis of POI is not uncommon but more often results from oocyte donation. This study will provide enlightened information for newly diagnosed women on the possibilities to build a family after POI diagnosis.
Effects of remote patient management on self-care behaviour in heart failure patients: results from the randomized TIM-HF2 trial
Authors: Deckwart Oliver , Koehler Kerstin , Lezius Susanne , Prescher Sandra , Koehler Friedrich , Winkler Sebastian 
Published: February 2023

Abstract

Aims
Remote patient management (RPM) in heart failure (HF) patients has beneficial clinical effects. This analysis investigates the effects of RPM used in the Telemedical Interventional Management in Heart Failure II (TIM-HF2) trial on HF-specific self-care.

Methods and results
From 2013 to 2017, 1538 HF outpatients (age 70.3 ± 10.5 years, 70% men, 52%/47% NYHA II/III, 65% LVEF ≤45%) with a recent HF hospitalization were included to the study and randomized to usual care (UC) plus RPM (n = 796) or UC only (n = 775), with a 12-month follow-up. Self-reported self-care behaviour at baseline and at end of study was assessed with the 9-item European Heart Failure Self-care Behaviour Scale (EHFScBS-9), obtaining 1321 patients with valid baseline and follow-up questionnaires for the analysis. EHFScBS-9 sum scores increased in the RPM group (n = 667) from 78.7 ± 17 to 84.5 ± 14 and in the UC group (n = 654) from 79.0 ± 17 to 80.0 ± 16 from baseline to 12 months [difference in means (MD) 4.58 (3.02, 6.14); P &lt; 0.001] with highest improvement [8.66 (3.52; 13.81)] in patients living alone and having an inadequate (&lt;70) baseline EHFScBS-9. There were differences between both groups in item ‘I weight myself every day’ [MD −1.13 (−1.24, −1.02); P &lt; 0.001] and item ‘I take my medication as prescribed’ [MD −0.06 (−0.10, −0.01); P = 0.014]. No correlation was found between the EHFScBS-9 score and the efficacy of RPM on the TIM-HF2 primary endpoint of percentage of days lost due to unplanned cardiovascular hospitalization or death of any cause.

Conclusions
RPM improves HF-specific self-care behaviour by achieving a better adherence to recommended HF regimen.

Registration
ClinicalTrials.gov: NCT01878630
High-intensity interval training in cardiac rehabilitation: a multi-centre randomized controlled trial
Authors: McGregor Gordon , Powell Richard , Begg Brian , Birkett Stefan T , Nichols Simon , Ennis Stuart , McGuire Scott , Prosser Jonathon , Fiassam Olivier , Hee Siew Wan , Hamborg Thomas , Banerjee Prithwish , Hartfiel Ned , Charles Joanna M , Edwards Rhiannon T , Drane Aimee , Ali Danish , Osman Faizel , He Hejie , Lachlan Tom , Haykowsky Mark J , Ingle Lee , Shave Rob 
Published: February 2023

Abstract

Background
There is a lack of international consensus regarding the prescription of high-intensity interval training (HIIT) for people with coronary artery disease (CAD) attending cardiac rehabilitation (CR).

Aims
To assess the clinical effectiveness and safety of low-volume HIIT compared with moderate-intensity steady-state (MISS) exercise training for people with CAD.

Methods and results
We conducted a multi-centre RCT, recruiting 382 patients from 6 outpatient CR centres. Participants were randomized to twice-weekly HIIT (n = 187) or MISS (n = 195) for 8 weeks. HIIT consisted of 10 × 1 min intervals of vigorous exercise (&gt;85% maximum capacity) interspersed with 1 min periods of recovery. MISS was 20–40 min of moderate-intensity continuous exercise (60–80% maximum capacity). The primary outcome was the change in cardiorespiratory fitness [peak oxygen uptake (VO2 peak)] at 8 week follow-up. Secondary outcomes included cardiovascular disease risk markers, cardiac structure and function, adverse events, and health-related quality of life. At 8 weeks, VO2peak improved more with HIIT (2.37 mL.kg−1.min−1; SD, 3.11) compared with MISS (1.32 mL.kg−1.min−1; SD, 2.66). After adjusting for age, sex, and study site, the difference between arms was 1.04 mL.kg−1.min−1 (95% CI, 0.38 to 1.69; P = 0.002). Only one serious adverse event was possibly related to HIIT.

Conclusions
In stable CAD, low-volume HIIT improved cardiorespiratory fitness more than MISS by a clinically meaningful margin. Low-volume HIIT is a safe, well-tolerated, and clinically effective intervention that produces short-term improvement in cardiorespiratory fitness. It should be considered by all CR programmes as an adjunct or alternative to MISS.

Trial registration
ClinicalTrials.gov: NCT02784873. https://clinicaltrials.gov/ct2/show/NCT02784873.