bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of frailty on outcomes of endovascular treatment for acute ischaemic stroke in older patients
Authors: Tan Benjamin Y Q , Ho Jamie S Y , Leow Aloysius S , Chia Magdalene L J , Sia Ching Hui , Koh Ying Ying , Seetharaman Santhosh K , Yang Cunli , Gopinathan Anil , Teoh Hock Luen , Sharma Vijay K , Seet Raymond C S , Chan Bernard P L , Yeo Leonard L L , Tan Li Feng 
Published: April 2022

Abstract

Background
frailty has been shown to be a better predictor of clinical outcomes than age alone across many diseases. Few studies have examined the relationship between frailty, stroke and stroke interventions such as endovascular thrombectomy (EVT).

Objective
we aimed to investigate the impact of frailty measured by clinical frailty scale (CFS) on clinical outcomes after EVT for acute ischemic stroke (AIS) in older patients ≥70 years.

Methods
in this retrospective cohort study, we included all consecutive AIS patients age ≥ 70 years receiving EVT at a single comprehensive stroke centre. Patients with CFS of 1–3 were defined as not frail, and CFS &gt; 3 was defined as frail. The primary outcome was modified Rankin Score (mRS) at 90 days. The secondary outcomes included duration of hospitalisation, in-hospital mortality, carer requirement, successful reperfusion, symptomatic intracranial haemorrhage and haemorrhagic transformation.

Results
a total of 198 patients were included. The mean age was 78.1 years and 52.0% were female. Frail patients were older, more likely to be female, had more co-morbidities. CFS was significantly associated with poor functional outcome after adjustment for age, NIHSS and time to intervention (adjusted odds ratio [aOR] 1.54, 95% confidence interval [CI] 1.04–2.28, P = 0.032). There was trend towards higher mortality rate in frail patients (frail: 18.3%; non-frail: 9.6%; P = 0.080). There were no significant differences in other secondary outcomes except increased carer requirement post discharge in frail patients (frail: 91.6%; non-frail: 72.8%; P = 0.002).

Conclusions
frailty was associated with poorer functional outcome at 90 days post-EVT in patients ≥ 70 years.
Reduced humoral response 3 months following BNT162b2 vaccination in SARS-CoV-2 uninfected residents of long-term care facilities
Authors: Trigueros Macedonia , Pradenas Edwards , Palacín Dolors , Muñoz-López Francisco , Ávila-Nieto Carlos , Trinité Benjamin , Bonet-Simó Josep Maria , Isnard Mar , Moreno Nemesio , Marfil Silvia , Rovirosa Carla , Puig Teresa , Grau Eulàlia , Chamorro Anna , Martinez Ana , Toledo Ruth , Font Marta , Ara Jordi , Carrillo Jorge , Mateu Lourdes , Blanco Julià , Clotet Bonaventura , Prat Nuria , Massanella Marta ,   on behalf of the CoronAVI@S and the KING cohort extension studies, Reyes Carrión S , Salarich Solà N , Vidal A , Alvarez Viñallonga R , Tornero J , Vilamala E , Suarez C , Gonzalo T , Perez L , Sans D , Blancas Loras A , Garcia Archer A , Borràs J , Cervelló S , Llados G , España S , Santos J R , Loste C , López C , Casafont I , Estany C , Rodriguez C , Moreno-Muñoz J , Prats A , Herrero C , Garcia A , Montero M , Tornero P , Gonzalez Palomares N , Grajea A , Ortiz L , Miranda C , Abad E , Figueroa D , Mancera A , Gonzalez Alonso S , Perez M , Esteban L , Ortiz M , Valls L , Ceron L , Baena T , Puig C , Cucurell M , Puig J 
Published: April 2022

Abstract

Background
SARS-CoV-2 vaccination is the most effective strategy to protect older residents of long-term care facilities (LTCF) against severe COVID-19, but primary vaccine responses are less effective in older adults. Here, we characterised the humoral responses of institutionalised seniors 3 months after they had received the mRNA/BNT162b2 vaccine.

Methods
plasma levels of SARS-CoV-2-specific total IgG, IgM and IgA antibodies were measured before and 3 months after vaccination in older residents of LTCF. Neutralisation capacity was assessed in a pseudovirus neutralisation assay against the original WH1 and later B.1.617.2/Delta variants. A group of younger adults was used as a reference group.

Results
three months after vaccination, uninfected older adults presented reduced SARS-CoV-2-specific IgG levels and a significantly lower neutralisation capacity against the WH1 and Delta variants compared with vaccinated uninfected younger individuals. In contrast, COVID-19-recovered older adults showed significantly higher SARS-CoV-2-specific IgG levels after vaccination than their younger counterparts, whereas showing similar neutralisation activity against the WH1 virus and an increased neutralisation capacity against the Delta variant. Although, similarly to younger individuals, previously infected older adults elicit potent cross-reactive immune responses, higher quantities of SARS-CoV-2-specific IgG antibodies are required to reach the same neutralisation levels.

Conclusions
although hybrid immunity seems to be active in previously infected older adults 3 months after mRNA/BNT162b2 vaccination, humoral immune responses are diminished in COVID-19 uninfected but vaccinated older residents of LTCF. These results suggest that a vaccine booster dose should be prioritised for this particularly vulnerable population.
High Counts in Hematologic Malignancies Predict Low Metaphase Yield for Cytogenetic Analysis
Authors: Chacko Mary P , Yuvarani Sugumaran , Kalaiyarasi Jayachandran P , Radhakrishnan Venkatraman , Gowri Mahasampath , Kamath Vandana 
Published: April 2022

Abstract

Objectives
This study examined the impact of various preanalytical variables on metaphase yield in hematologic malignancies.

Methods
Marrow samples from patients with hematologic malignancies that were subjected to cytogenetic analysis were categorized into two groups: one with samples that yielded an adequate number of metaphases, defined as at least 20, and a second with a low number of metaphases (LNM), having fewer than 20 metaphases. Age, sex, bone marrow nucleated cell (MNC) count, and peripheral blood counts (hemoglobin, total WBC count, and platelet count) were analyzed for an association with LNM.

Results
Of 455 samples, 17% (79/455) belonged to the LNM group, including 6% (27/455) that yielded no metaphases. MNCs and WBCs were higher in the LNM group (P &lt; .001 for both). MNCs were higher in LNM groups in both acute myeloid leukemia (P = .008) and acute lymphoblastic leukemia (P = .001). Receiver operating characteristic curves showed moderate prediction of MNC and WBC counts for LNM with areas under the curves of 0.7. Other analyzed parameters showed no significant associations with LNM.

Conclusions
Low metaphase yields occur frequently in hematologic malignancies with high counts. This could reflect biological characteristics of these malignancies that merit further investigation.
Invited Commentary: Conducting and Emulating Trials to Study Effects of Social Interventions
Authors: Rojas-Saunero L Paloma , Labrecque Jeremy A , Swanson Sonja A 
Published: April 2022

AbstractAll else being equal, if we had 1 causal effect we wished to estimate, we would conduct a randomized trial with a protocol that mapped onto that causal question, or we would attempt to emulate that target trial with observational data. However, studying the social determinants of health often means there are not just 1 but several causal contrasts of simultaneous interest and importance, and each of these related but distinct causal questions may have varying degrees of feasibility in conducting trials. With this in mind, we discuss challenges and opportunities that arise when conducting and emulating such trials. We describe designing trials with the simultaneous goals of estimating the intention-to-treat effect, the per-protocol effect, effects of alternative protocols or joint interventions, effects within subgroups, and effects under interference, and we describe ways to make the most of all feasible randomized trials and emulated trials using observational data. Our comments are grounded in the study results of Courtin et al. (Am J Epidemiol. 2022;191(8):1444–1452).
Gallbladder Disease and Risk of Type 2 Diabetes in Postmenopausal Women: A Women’s Health Initiative Study
Authors: Ako Ako Adams , Michael Yvonne L , Robinson Lucy F , Wactawski-Wende Jean , Shadyab Aladdin H , Garcia Lorena , Nriagu Bede N , Saquib Nazmus , Nassir Rami , Liu Simin , Wallace Robert B 
Published: April 2022

AbstractStudies have suggested that adults with gallbladder disease have increased risk of type 2 diabetes. This prospective cohort study assessed the risk of type 2 diabetes in postmenopausal women with gallbladder disease. Data from women enrolled in the Women’s Health Initiative from 1993 to 2005, aged 50–79 years (mean = 63.2; standard deviation, 7.2), were analyzed. Cox proportional hazards regression models were used to estimate the risk of type 2 diabetes associated with gallbladder disease. There were 8,896 new cases of type 2 diabetes after 1,025,486 person-years of follow-up. Gallbladder disease was significantly associated with type 2 diabetes (hazard ratio = 1.52; 95% confidence interval (CI): 1.38,1.67). The observed risk of type 2 diabetes in women with both gallbladder disease and central obesity was 37% higher than expected (relative excess risk due to interaction = 0.37, 95% CI: 0.11,0.63) on the additive scale. The hazard ratios for type 2 diabetes associated with gallbladder disease were 1.25 (95% CI: 1.19,1.32) and 1.48 (95% CI: 1.34,1.63) in women with and without central obesity, respectively, on the multiplicative scale. Results of this study support further studies to determine whether interventions in older women with gallbladder disease would reduce type 2 diabetes risk, especially among those with central obesity. Future research should examine the pathophysiological basis of the association between gallbladder disease and type 2 diabetes.
Alcohol Addiction: One Entity or Different Entities? A DSM-4-Based Attempt Toward a Geographicization of Alcohol Addiction and Abuse
Authors: Allamani Allaman , Voller Fabio , Bravi Stefano , Pepe Pasquale , Biffino Marco , Buscema Paolo Massimo , Maurelli Guido , Massini Giulia , Einstein S , Manthey Jakob , Rehm Jürgen 
Published: April 2022

Abstract

Aim
To examine whether in Europe perceptions of ‘alcoholism’ differ in a discrete manner according to geographical area.

Method
Secondary analysis of a data set from a European project carried out in 2013–2014 among 1767 patients treated in alcohol addiction units of nine countries/regions across Europe. The experience of all 11 DSM-4 criteria used for diagnosing ‘alcohol dependence’ and ‘alcohol abuse’ were assessed in patient interviews. The analysis was performed through Multiple Correspondence Analysis.

Results
The symptoms of ‘alcohol dependence’ and ‘alcohol abuse’, posited by DSM-IV, were distributed according to three discrete geographical patterns: a macro-area mainly centered on drinking beer and spirit, a culture traditionally oriented toward wine and a mixed intermediate alcoholic beverage situation.

Conclusion
These patterns of perception seem to parallel the diverse drinking cultures of Europe.
Narrative and Personal Identity
Authors: Schroeder Mark 
Published: April 2022

AbstractIn this paper I explore how and why personal identity might be essentially narrative in nature. My topic is the question of personal identity in the strict sense of identity—the question of which person you are, and how that person is extended in space, time, and quality. In this my question appears to contrast with the question of personal identity in the sense sought by teenagers and sufferers of mid-life crises who are trying to ‘find themselves’. But in fact it will be key to my argument that these questions are not distinct and independent. Whereas Parfit was concerned, in his work on personal identity, with how he was extended over time, the teenager who is finding themselves is concerned with how they are extended in quality. Indeed, the core of my argument is that because narrative is the key to understanding how we are extended in quality, and quality is just one more dimension along which we are extended, along with space and time, narrative must also be the clue as to how we are extended in space and time. You, I will argue, are the protagonist in the best story of your life.
Heterogeneity in resource competition covaries with individual variation in long-term social relationships
Authors: Levengood Alexis L , Strickland Kasha , Foroughirad Vivienne , Mann Janet , Cristescu Romane H , Krzyszczyk Ewa , Frère Céline H 
Published: April 2022

Abstract
Resource competition among conspecifics is central to social evolution, as it serves as one of the primary selective pressures of group living. This is because the degree of competition for resources impacts the costs and benefits of social interactions. Despite this, how heterogeneity in resource competition drives variation in the type and quantity of long-term social relationships individuals foster has been overlooked. By measuring male mating competition and female foraging competition in a highly social, long-lived mammal, we demonstrate that individual variation in long-term intrasexual social relationships covaries with preferred habitat and experienced resource competition, and this effect differs based on the sex of the individual. Specifically, greater resource competition resulted in fewer social preferences, but the magnitude of the effect varied by both habitat and sex, whereas for social avoidances, both the directionality and magnitude of the effect of resource competition varied by habitat and sex. Together our work shows how fine-scale variation in individual socioecological niches (i.e., unique physical and social environments) can drive extensive variation in individual social behavior (here long-term relationships) within a population, thereby broadening current theories of social evolution.
GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions
Authors: Du Zhi-Hua , Wu Yang-Han , Huang Yu-An , Chen Jie , Pan Gui-Qing , Hu Lun , You Zhu-Hong , Li Jian-Qiang 
Published: April 2022

AbstractMotivationInteraction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network.ResultsIn this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale.AvailabilityPython code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation
Authors: Peng Li , Yang Cheng , Huang Li , Chen Xiang , Fu Xiangzheng , Liu Wei 
Published: April 2022

AbstractCircular RNAs (circRNAs) are a class of structurally stable endogenous noncoding RNA molecules. Increasing studies indicate that circRNAs play vital roles in human diseases. However, validating disease-related circRNAs in vivo is costly and time-consuming. A reliable and effective computational method to identify circRNA–disease associations deserves further studies. In this study, we propose a computational method called RNMFLP that combines robust nonnegative matrix factorization (RNMF) and label propagation algorithm (LP) to predict circRNA–disease associations. First, to reduce the impact of false negative data, the original circRNA–disease adjacency matrix is updated by matrix multiplication using the integrated circRNA similarity and the disease similarity information. Subsequently, the RNMF algorithm is used to obtain the restricted latent space to capture potential circRNA–disease pairs from the association matrix. Finally, the LP algorithm is utilized to predict more accurate circRNA–disease associations from the integrated circRNA similarity network and integrated disease similarity network, respectively. Fivefold cross-validation of four datasets shows that RNMFLP is superior to the state-of-the-art methods. In addition, case studies on lung cancer, hepatocellular carcinoma and colorectal cancer further demonstrate the reliability of our method to discover disease-related circRNAs.
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity
Authors: Bondhus Leroy , Varma Roshni , Hernandez Yenifer , Arboleda Valerie A 
Published: April 2022

AbstractThe spatial and temporal domain of a gene’s expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate’s stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates’ stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.
Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information
Authors: Lou Zhengzheng , Cheng Zhaoxu , Li Hui , Teng Zhixia , Liu Yang , Tian Zhen 
Published: April 2022

AbstractMotivationIn recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA–disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA–disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging.ResultsTo address such a challenge, we propose a novel method called mixed neighborhood information for miRNA–disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA–disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA.Availability and implementationhttps://github.com/chengxu123/MINIMDA and http://120.79.173.96/
MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction
Authors: Du Bing-Xue , Zhao Peng-Cheng , Zhu Bei , Yiu Siu-Ming , Nyamabo Arnold K , Yu Hui , Shi Jian-Yu 
Published: April 2022

AbstractMotivationDuring lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.ResultsTo address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery.Availability and implementationThe code and data underlying this article are freely available at https://github.com/dubingxue/MLGL-MP.