bluuebunny/crossref_metadata_2025_split huggingface dataset:
Promoting continence in older people
Authors: Schlögl Mathias , Umbehr Martin H , Habib Muhammad Hamza , Wagg Adrian , Gordon Adam L , Harwood Rowan 
Published: September 2022

AbstractThe prevalence of urinary incontinence (UI) is strongly associated with increasing age. Twenty five percent of women over 80 years of age have clinically significant symptoms in population surveys, but prevalence is as high as 70% in older hospital in-patients and residents of care homes with nursing. UI substantially affects quality of life and well-being, and generates significant economic burden for health and social care. Sadly, UI is considered as taboo by society, leading to isolation, depression and reluctance to seek help. As with all aspects of care of older people, a multi-modal approach to assessment and management is needed. Key to effective management of incontinence is recognition. As a minimum, clinicians should actively ask patients about continence, especially in older adults living with frailty. Careful evaluation and establishment of any underpinning diagnosis and aetiological factors requires comprehensive, multimodal, usually multidisciplinary, assessment. A lack of awareness of the problem and what can be done about it exists in both laypeople and clinicians, this needs correcting. An interdisciplinary approach to research and management must be the way into the future.
Low-density lipoprotein cholesterol in oldest old with acute myocardial infarction: Is lower the better?
Authors: Liu Hui-Hui , Zhang Meng , Chen Run-Zhen , Zhou Jin-Ying , Qian Jie , Dou Ke-Fei , Yan Hong-Bing , Li Jian-Jun 
Published: September 2022

Abstract

Background
the relationship between low-density lipoprotein cholesterol (LDL-C) and adverse outcomes among the older people remains controversial.

Objective
to further clarify the association between admission LDL-C levels and cardiovascular mortality (CVM) among oldest old individuals (≥80 years) with acute myocardial infarction (AMI).

Design
a prospective cohort study.

Setting
two-centre.

Subjects
a consecutive sample of 1,224 oldest old individuals with AMI admitted to Beijing FuWai and Shenzhen FuWai hospitals.

Methods
all individuals were subdivided according to baseline LDL-C levels (&lt;1.8, 1.8–2.6 and ≥ 2.6 mmol/l) and further stratified by high-sensitivity C-reactive protein (hsCRP) concentrations (&lt;10 and ≥10 mg/l). The primary outcome was CVM. The time from admission to the occurrence of CVM or the last follow-up was analysed in Kaplan–Meier and Cox analyses.

Results
the median age of the overall population was 82 years. During an average of 24.5 months’ follow-up, 299 cardiovascular deaths occurred. Kaplan–Meier analysis showed that LDL-C &lt; 1.8 mmol/l group had the highest CVM among oldest old individuals with AMI. Multivariate Cox regression analysis further revealed that compared with those with LDL-C levels &lt;1.8 mmol/l, subjects with LDL-C levels ≥2.6 mmol/l (hazard ratio: 0.67, 95% confidence interval: 0.46–0.98) had significantly lower risk of CVM, especially in those with high hsCRP levels. Moreover, when categorising according to LDL-C and hsCRP together, data showed that individuals with low LDL-C and high hsCRP levels had the highest CVM.

Conclusions
LDL-C &lt; 1.8 mmol/l was associated with a high CVM after AMI in oldest old individuals, especially when combined with high hsCRP levels, which may need to be confirmed by randomised controlled trials.
Sarcoidosis in the older person: diagnostic challenges and treatment consideration
Authors: Brennan Michelle , Breen David 
Published: September 2022

Abstract

Background
Sarcoidosis is a multi-system disorder with an increasing propensity to present in older patients. Diagnostic uncertainty is common and understandable given the higher prevalence of co-morbidities in older patients and broad differential for multi-system clinical presentations. Excluding malignancy and infection with a high degree of certainty is challenging and may require repeated confirmatory investigation where the diagnosis remains in doubt.

Summary of main findings
There are a paucity of studies examining late-onset sarcoidosis. Female predominance, pulmonary, ocular, skin and systemic symptoms are common, while more classical presentations such as Lofgren’s syndrome are uncommon. Positivity rates of biopsies vary between studies; however, targeted biopsies of accessible sites with organ involvement are the most successful. Therapeutic management is directed at reducing inflammation, and thereby reducing symptom burden, improving quality of life and avoiding progression of organ damage. While most older patients will require corticosteroid therapy, they are also more prone to developing adverse effects. Most older patients will experience a clinical remission; however, the risk of developing chronic sarcoidosis and organ damage is higher compared with younger counterparts. Patients with evidence of pulmonary fibrosis and pulmonary hypertension are at particular risk.

Impact on clinical practice
Health care providers who care for older adults should be aware of the increasing prevalence of late-onset sarcoidosis and consider the diagnosis in those who present with otherwise unexplained systemic symptoms, thoracic abnormalities on imaging and/or evidence of other organ involvement. Earlier diagnosis and therapeutic intervention to halt the development of pulmonary fibrosis and pulmonary hypertension and monitoring for treatment-related adverse effects will confer a mortality benefit.
The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma
Authors: Zhu Sujie , Kong Weikaixin , Zhu Jie , Huang Liting , Wang Shixin , Bi Suzhen , Xie Zhengwei 
Published: September 2022

Abstract
Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P &lt; 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.
Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments
Authors: Gable Annika L , Szklarczyk Damian , Lyon David , Matias Rodrigues João F , von Mering Christian 
Published: September 2022

AbstractA knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.
Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs
Authors: Xuan Ping , Wang Shuai , Cui Hui , Zhao Yue , Zhang Tiangang , Wu Peiliang 
Published: September 2022

AbstractMotivationLong noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths.ResultsWe propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method’s ability to discover potential disease-related lncRNA candidates.Contactzhang@hlju.edu.cn or peiliangwu@ysu.edu.cnSupplementary informationSupplementary data are available at Briefings in Bioinformatics online.
TSomVar: a tumor-only somatic and germline variant identification method with random forest
Authors: Shi Shuo , Wang Qi , Shang Yunfei , Bu Congfan , Lu Mingming , Jiang Meiye , Zhang Hao , Yu Shuhuan , Zeng Jingyao , Zhang Zaichao , Du Zhenglin , Xiao Jingfa 
Published: September 2022

Abstract
Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding
Authors: Guo Lu-Xiang , You Zhu-Hong , Wang Lei , Yu Chang-Qing , Zhao Bo-Wei , Ren Zhong-Hao , Pan Jie 
Published: September 2022

AbstractA large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.
Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data
Authors: Mercatelli Daniele , Cabrelle Chiara , Veltri Pierangelo , Giorgi Federico M , Guzzi Pietro H 
Published: September 2022

Abstract
Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as ‘signaling hubs’. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called ‘SURFACER’. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.
Prophylactic Medication for the Prevention of Endoscopic Recurrence in Crohn’s Disease: a Prospective Study Based on Clinical Risk Stratification
Authors: Arkenbosch Jeanine H C , Beelen Evelien M J , Dijkstra Gerard , Romberg-Camps Mariëlle , Duijvestein Marjolijn , Hoentjen Frank , van der Marel Sander , Maljaars P W Jeroen , Jansen Sita , de Boer Nanne K H , West Rachel L , Horjus Carmen S , Stassen Laurents P S , van Schaik Fiona D M , van Ruler Oddeke , Jharap Bindia J H , Visschedijk Marijn , Janssen Alfred , Erler Nicole S , Doukas Michail , Ooms Ariadne H A G , Kats-Ugurlu Gursah , van der Woude Christien Janneke , de Vries Annemarie C 
Published: September 2022

AbstractBackgroundTo prevent recurrence after ileocolonic resection [ICR] in Crohn’s disease [CD], postoperative prophylaxis based on risk stratification is recommended in international guidelines. This study aimed to evaluate postoperative CD recurrence after implementation of a clinical management algorithm and to determine the predictive value of clinical and histological risk factors [RFs].MethodsIn this multicentre, prospective cohort study, CD patients [≥16 years] scheduled for ICR were included. The algorithm advised no postoperative medication for low-risk patients, and treatment with prophylaxis [immunosuppressant/biological] for high-risk patients [≥1 RF: active smoking, penetrating disease, prior ICR]. Clinical and histological RFs [active inflammation, granulomas, plexitis in resection margins] for endoscopic recurrence [Rutgeerts’ score ≥i2b at 6 months] were assessed using logistic regression and ROC curves based on predicted probabilities.ResultsIn total, 213 CD patients after ICR were included [age 34.5 years; 65% women] (93 [44%] low-risk; 120 [56%] high-risk: 45 [38%] smoking; 51 [43%] penetrating disease; 51 [43%] prior ICR). Adherence to the algorithm was 82% in low-risk [no prophylaxis] and 51% in high-risk patients [prophylaxis]. Endoscopic recurrence was higher in patients treated without prophylaxis than with prophylaxis in both low [45% vs 16%, p = 0.012] and high-risk patients [49% vs 26%, p = 0.019]. Clinical risk stratification including the prescription of prophylaxis corresponded to an area under the curve [AUC] of 0.70 (95% confidence interval [CI] 0.61–0.79). Clinical RFs combined with histological RFs increased the AUC to 0.73 [95% CI 0.64–0.81].ConclusionAdherence to this management algorithm is 65%. Prophylactic medication after ICR prevents endoscopic recurrence in low- and high-risk patients. Clinical risk stratification has an acceptable predictive value, but further refinement is needed.
How to optimize the adherence to a guideline-directed medical therapy in the secondary prevention of cardiovascular diseases: a clinical consensus statement from the European Association of Preventive Cardiology
Authors: Pedretti Roberto F E , Hansen Dominique , Ambrosetti Marco , Back Maria , Berger Thomas , Ferreira Mariana Cordeiro , Cornelissen Véronique , Davos Constantinos H , Doehner Wolfram , de Pablo y Zarzosa Carmen , Frederix Ines , Greco Andrea , Kurpas Donata , Michal Matthias , Osto Elena , Pedersen Susanne S , Salvador Rita Esmeralda , Simonenko Maria , Steca Patrizia , Thompson David R , Wilhelm Matthias , Abreu Ana 
Published: September 2022

Abstract
A key factor to successful secondary prevention of cardiovascular disease (CVD) is optimal patient adherence to treatment. However, unsatisfactory rates of adherence to treatment for CVD risk factors and CVD have been observed consistently over the last few decades. Hence, achieving optimal adherence to lifestyle measures and guideline-directed medical therapy in secondary prevention and rehabilitation is a great challenge to many healthcare professionals. Therefore, in this European Association of Preventive Cardiology clinical consensus document, a modern reappraisal of the adherence to optimal treatment is provided, together with simple, practical, and feasible suggestions to achieve this goal in the clinical setting, focusing on evidence-based concepts.
Slugging in horizontal channel flow
Authors: Giddings Josef Adam , Billingham John , Cox Stephen Michael 
Published: September 2022

Abstract
The stratified flow of gas–liquid mixtures is important in many industries, in particular oil and gas, where the formation of liquid slugs is usually undesirable. In this paper, we analyse the hydraulic model for turbulent, two-layer, gas–liquid flow in a horizontal channel developed by Needham et al. (2008, The development of slugging in two-layer hydraulic flows, IMA J. Appl. Math., 73, 274–322) who considered only the thin layer limit, for liquid layers of arbitrary thickness. This allows us to consider the effect of the prescribed inlet velocity ratio and the interaction of the gas layer with the upper wall of the channel on the formation of slug formation.
We show that there is a critical Froude number above which small-amplitude disturbances to the uniform flow are unstable and grow to form non-linear roll waves. We investigate the existence of periodic travelling wave solutions and find that these are strongly influenced by the existence of two equilibrium solutions that are not present when the liquid layer is thin. The system of ordinary differential equations that describe travelling wave solutions has a complicated phase portrait which we study in some typical cases. We find that periodic solutions that have the channel close to filled with liquid can exist, and we identify them as slugs. We also study an initial value problem driven by noise at the inlet of the channel and find that structures locally similar to these periodic travelling waves are generated. We provide numerical evidence for the formation of slugs in cases where the initial liquid to homogeneous velocity ratio is greater than 0.5.