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1.
Cardiovasc Diabetol ; 23(1): 267, 2024 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-39039597

RESUMO

BACKGROUND: Sodium-Glucose Cotransporter-2 Inhibitor (SGLT2i) is a novel oral drug for treating type 2 diabetes mellitus (T2DM) with demonstrated cardiovascular benefits. Previous studies in apolipoprotein E knockout mice have shown that SGLT2i is associated with attenuated progression of atherosclerosis. However, whether this effect extends to T2DM patients with coronary atherosclerosis in real-world settings remains unknown. METHODS: In this longitudinal cohort study using coronary computed tomography angiography (CCTA), T2DM patients who underwent ≥ 2 CCTA examinations at our center between 2019 and 2022 were screened. Eligible patients had multiple study plaques, defined as non-obstructive stenosis at baseline and not intervened during serial CCTAs. Exclusion criteria included a CCTA time interval < 12 months, prior SGLT2i treatment, or initiation/discontinuation of SGLT2i during serial CCTAs. Plaque volume (PV) and percent atheroma volume (PAV) were measured for each study plaque using CCTA plaque analysis software. Patients and plaques were categorized based on SGLT2i therapy and compared using a 1:1 propensity score matching (PSM) analysis. RESULTS: The study included 236 patients (mean age 60.5 ± 9.5 years; 69.1% male) with 435 study plaques (diameter stenosis ≥ 50%, 31.7%). Following SGLT2i treatment for a median duration of 14.6 (interquartile range: 13.0, 20.0) months, overall, non-calcified, and low-attenuation PV and PAV were significantly decreased, while calcified PV and PAV were increased (all p < 0.001). Meanwhile, reductions in overall PV, non-calcified PV, overall PAV, and non-calcified PAV were significantly greater in SGLT2i-treated compared to non-SGLT2i-treated plaques (all p < 0.001). PSM analysis showed that SGLT2i treatment was associated with higher reductions in overall PV (- 11.77 mm3 vs. 4.33 mm3, p = 0.005), non-calcified PV (- 16.96 mm3 vs. - 1.81 mm3, p = 0.017), overall PAV (- 2.83% vs. 3.36%, p < 0.001), and non-calcified PAV (- 4.60% vs. 0.70%, p = 0.003). These findings remained consistent when assessing annual changes in overall and compositional PV and PAV. Multivariate regression models demonstrated that SGLT2i therapy was associated with attenuated progression of overall or non-calcified PV or PAV, even after adjusting for cardiovascular risk factors, medications, and baseline overall or non-calcified PV or PAV, respectively (all p < 0.05). The effect of SGLT2i on attenuating non-calcified plaque progression was consistent across subgroups (all p for interaction > 0.05). CONCLUSIONS: In this longitudinal CCTA cohort of T2DM patients, SGLT2i therapy markedly regressed coronary overall PV and PAV, mainly result from a significant reduction in non-calcified plaque.


Assuntos
Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Diabetes Mellitus Tipo 2 , Placa Aterosclerótica , Valor Preditivo dos Testes , Inibidores do Transportador 2 de Sódio-Glicose , Humanos , Masculino , Inibidores do Transportador 2 de Sódio-Glicose/uso terapêutico , Inibidores do Transportador 2 de Sódio-Glicose/efeitos adversos , Feminino , Pessoa de Meia-Idade , Estudos Longitudinais , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/diagnóstico , Idoso , Resultado do Tratamento , Fatores de Tempo , Estudos Retrospectivos , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/efeitos dos fármacos
2.
ArXiv ; 2024 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-38979488

RESUMO

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.

3.
Cell Death Dis ; 15(6): 406, 2024 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-38858351

RESUMO

Diabetic cardiomyopathy (DCM) is a prevalent myocardial microvascular complication of the myocardium with a complex pathogenesis. Investigating the pathogenesis of DCM can significantly contribute to enhancing its prevention and treatment strategies. Our study revealed an upregulation of lysine acetyltransferase 2 A (Kat2a) expression in DCM, accompanied by a decrease in N6-methyladenosine (m6A) modified Kat2a mRNA levels. Our study revealed an upregulation of lysine acetyltransferase 2 A (Kat2a) expression in DCM, accompanied by a decrease in N6-methyladenosine (m6A) modified Kat2a mRNA levels. Functionally, inhibition of Kat2a effectively ameliorated high glucose-induced cardiomyocyte injury both in vitro and in vivo by suppressing ferroptosis. Mechanistically, Demethylase alkB homolog 5 (Alkbh5) was found to reduce m6A methylation levels on Kat2a mRNA, leading to its upregulation. YTH domain family 2 (Ythdf2) played a crucial role as an m6A reader protein mediating the degradation of Kat2a mRNA. Furthermore, Kat2a promoted ferroptosis by increasing Tfrc and Hmox1 expression via enhancing the enrichment of H3K27ac and H3K9ac on their promoter regions. In conclusion, our findings unveil a novel role for the Kat2a-ferroptosis axis in DCM pathogenesis, providing valuable insights for potential clinical interventions.


Assuntos
Cardiomiopatias Diabéticas , Ferroptose , Heme Oxigenase-1 , Histona Acetiltransferases , Cardiomiopatias Diabéticas/metabolismo , Cardiomiopatias Diabéticas/patologia , Cardiomiopatias Diabéticas/genética , Animais , Ferroptose/genética , Humanos , Heme Oxigenase-1/metabolismo , Heme Oxigenase-1/genética , Camundongos , Histona Acetiltransferases/metabolismo , Histona Acetiltransferases/genética , Masculino , Camundongos Endogâmicos C57BL , Miócitos Cardíacos/metabolismo , Miócitos Cardíacos/patologia , Proteínas de Membrana/metabolismo , Proteínas de Membrana/genética , Adenosina/análogos & derivados , Adenosina/metabolismo
4.
Brief Bioinform ; 25(3)2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38706317

RESUMO

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.


Assuntos
Análise de Sequência de RNA , Análise de Célula Única , Análise de Célula Única/métodos , Análise de Sequência de RNA/métodos , Humanos , Algoritmos , Perfilação da Expressão Gênica/métodos , Biologia Computacional/métodos , RNA-Seq/métodos
5.
Sci Rep ; 13(1): 13280, 2023 08 16.
Artigo em Inglês | MEDLINE | ID: mdl-37587128

RESUMO

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.


Assuntos
Aminoácidos , Antifibrinolíticos , Sequência de Aminoácidos , Fontes de Energia Elétrica , Idioma
6.
IEEE Winter Conf Appl Comput Vis ; 2023: 4976-4985, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-37051561

RESUMO

Deep neural networks (DNNs) have rapidly become a de facto choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify hard-to-learn (HTL) training samples, and improve pathology localization by attending them explicitly, during training in supervised, semi-supervised, and weakly-supervised settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning [15] - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by ~2-3%.

7.
Int J Psychophysiol ; 188: 1-11, 2023 06.
Artigo em Inglês | MEDLINE | ID: mdl-36889599

RESUMO

People tend to dislike and punish unfair behaviors in social interactions, and this disposition may be moderated by the characteristics of their interaction partner. We used a modified ultimatum game (UG) to investigate players' responses to fair and unfair offers from proposers described as having performed either a moral transgression or a neutral behavior, and recorded an electroencephalogram. The participants' behavior in the UG suggests that people quickly demand more fairness from proposers who have committed moral transgressions rather than neutral behavior. Event-related potentials (ERPs) revealed a significant effect of offer type and of proposer type on P300 activity. The prestimulus α-oscillation power in the neutral behavior condition was significantly lower than that in the moral transgression condition. The post-stimulus ß-event-related synchronization (ß-ERS) was larger for the moral transgression condition than the neutral behavior condition in response to the least fair offers, and larger for neutral behavior than the moral transgression condition in response to the fairest offers. In summary, ß-ERS was influenced by both proposer type and offer type, which revealed different neural responses to the offer from either a morally transgressive or a neutral behavior proposer.


Assuntos
Eletroencefalografia , Jogos Experimentais , Humanos , Potenciais Evocados/fisiologia , Comportamento Social , Princípios Morais
8.
bioRxiv ; 2023 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-36993648

RESUMO

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

9.
IEEE Trans Pattern Anal Mach Intell ; 45(3): 2769-2781, 2023 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-35544513

RESUMO

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uniquely suffers from over-smoothing, information squashing, and so on, which limits their potential power for encoding the high-order neighbor structure in large-scale graphs. Although numerous efforts are proposed to address these limitations, such as various forms of skip connections, graph normalization, and random dropping, it is difficult to disentangle the advantages brought by a deep GNN architecture from those "tricks" necessary to train such an architecture. Moreover, the lack of a standardized benchmark with fair and consistent experimental settings poses an almost insurmountable obstacle to gauge the effectiveness of new mechanisms. In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs. We categorize existing approaches, investigate their hyperparameter sensitivity, and unify the basic configuration. Comprehensive evaluations are then conducted on tens of representative graph datasets including the recent large-scale Open Graph Benchmark, with diverse deep GNN backbones. We demonstrate that an organic combo of initial connection, identity mapping, group and batch normalization attains the new state-of-the-art results for deep GNNs on large datasets. Codes are available: https://github.com/VITA-Group/Deep_GCN_Benchmarking.

10.
Biol Psychol ; 176: 108467, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36455804

RESUMO

Empathy for others' pain plays a critical role in human social interactions; however, the influence of moral transgression remains unclear. We examined the effect of moral transgression on the behavioral and underlying neural processes of empathy for others' pain. Participants performed a pain-empathy task separately in a moral transgression condition and a neutral behavior condition, while an electroencephalogram was recorded. Event-related potential (ERP) results showed that empathic response, as reflected in the late positive component, was smaller when participants performed the task in the moral transgression condition than in the neutral behavior condition. Time-frequency results also showed decreased empathic effect on the beta event-related desynchronization response in the moral transgression as compared to the neutral behavior condition. However, empathic response as reflected in the N2 component was comparable between the moral conditions. These findings demonstrate a moral transgression effect on both cognitive evaluations and sensorimotor processes of empathy for others' pain. Furthermore, spontaneous alpha-oscillation power recorded prior to the onset of empathy-inducing stimuli was significantly higher in the moral transgression condition than in the neutral behavior condition. Consequently, differences in sustained attention may be the physiological foundation of the impact of moral transgression of the observed person on the cognitive and sensorimotor processes of empathy for pain.


Assuntos
Eletroencefalografia , Empatia , Humanos , Potenciais Evocados/fisiologia , Princípios Morais , Dor/psicologia
11.
Proc Int Conf Web Search Data Min ; 2022: 1300-1309, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35647617

RESUMO

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://github.com/Shen-Lab/GraphCL_Automated.

12.
Food Chem ; 386: 132755, 2022 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-35509158

RESUMO

The influence of alternating current (AC) electric field and KCl on the structure and gel properties of Konjac Glucomannan (KGM) were studied in this work by high-performance gel permeation chromatography (HPGPC), acid-base titration, solid-state nuclear magnetic resonance (NMR), X-ray diffraction (XRD), simultaneous differential scanning calorimetry/thermo gravimetric analyzer (DSC/TGA) and a rheometer. HPGPC showed KGM was degraded by AC electric field and Acid-base titration showed that under the action of AC electric field and KCl KGM removed part of acetyl groups, which were consistent with the analysis of NMR. XRD and temperature sweep measurements respectively showed that the electrotreatment time and KCl concentration had important effects on the gel formation and its three-dimensional network. Simultaneous DSC/TGA and temperature sweep measurements both demonstrated the gel had good thermal stability.


Assuntos
Eletricidade , Mananas , Varredura Diferencial de Calorimetria , Mananas/química , Termogravimetria
13.
Adv Neural Inf Process Syst ; 35: 1909-1922, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-37192934

RESUMO

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https://github.com/weitianxin/HyperGCL.

14.
Neuropsychologia ; 162: 108025, 2021 11 12.
Artigo em Inglês | MEDLINE | ID: mdl-34560141

RESUMO

Humans are social animals and need to cooperate to survive. However, individuals are not cooperative in every social interaction, and their cooperation may depend on social context. The present study used a social dilemma game to investigate whether an opponent's tendency to be cooperative over time influenced a player's behavior and neural response to outcomes in the game. University students ("players") thought they were playing against other students ("opponents") in the Chicken Game but were actually playing against a programmed computer. Participants were randomly assigned to play with an opponent who tended to be competitive (cooperative 20% of the time) or who tended to be cooperative (cooperative 80% of the time). The results showed that early in the game, participants in both groups adopted a "tit-for-tat" strategy. However, as the game progressed and the opponent's behavioral tendency became more noticeable, players in the competitive-opponent group became generally more cooperative to limit their losses. ERPs analyses indicated that players had a higher P300 and larger theta power in response to the opponent's aggression but not to the opponent's cooperation when their opponent showed a tendency to be cooperative vs. competitive. The results suggest that people adjust their cooperative behavior based on their opponent's behavior in social interaction, and aggression captures more attention than cooperation in this process.


Assuntos
Comportamento Cooperativo , Relações Interpessoais , Agressão , Animais , Potenciais Evocados , Humanos
15.
J Int Med Res ; 49(3): 300060521997618, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33730893

RESUMO

Electrical storm is a life-threatening emergency condition defined as three or more episodes of ventricular tachycardia or ventricular fibrillation (VF) within 24 hours requiring anti-tachycardia therapy, electrical cardioversion, or defibrillation. However, studies of the incidence of electrical storm after chronic total occlusion-percutaneous coronary intervention (CTO-PCI) are limited,7 and post-procedural VF after revascularization of CTO has not been described. The purpose of this article was to present a case of post-operative VF electrical storm after revascularization of CTO of the left anterior descending (LAD) artery to determine whether the electrical storm was caused by reperfusion arrhythmia or compromise of either branch vessels or the collateral circulation during intervention.


Assuntos
Oclusão Coronária , Intervenção Coronária Percutânea , Arritmias Cardíacas , Doença Crônica , Oclusão Coronária/diagnóstico por imagem , Oclusão Coronária/cirurgia , Humanos , Reperfusão , Resultado do Tratamento , Fibrilação Ventricular/etiologia , Fibrilação Ventricular/terapia
16.
Proc Mach Learn Res ; 119: 10871-10880, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33283198

RESUMO

Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning of images. Its introduction to graph convolutional networks (GCNs) operating on graph data is however rarely explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into GCNs. We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning. Moreover, we propose to investigate three novel self-supervised learning tasks for GCNs with theoretical rationales and numerical comparisons. Lastly, we further integrate multi-task self-supervision into graph adversarial training. Our results show that, with properly designed task forms and incorporation mechanisms, self-supervision benefits GCNs in gaining more generalizability and robustness. Our codes are available at https://github.com/Shen-Lab/SS-GCNs.

17.
JMIR Med Inform ; 8(10): e13567, 2020 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-33103657

RESUMO

BACKGROUND: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

18.
BMC Bioinformatics ; 21(1): 118, 2020 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-32192433

RESUMO

BACKGROUND: mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. RESULTS: In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. CONCLUSIONS: We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.


Assuntos
Ferro/química , Fígado/metabolismo , Software , Algoritmos , Animais , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes/genética , Íons/química , Ferro/toxicidade , Fígado/efeitos dos fármacos , Camundongos , Camundongos Endogâmicos C57BL , RNA-Seq
19.
Adv Neural Inf Process Syst ; 32: 15044-15054, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32508484

RESUMO

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms. The meta-optimizer targets at a meta-loss function consisting of both cumulative regret and entropy. Specifically, we learn and interpret the update formula through a population of LSTMs embedded with sample- and feature-level attentions. Meanwhile, we estimate the posterior directly over the global optimum and use an uncertainty measure to help guide the learning process. Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors. The codes are publicly available at: https://github.com/Shen-Lab/LOIS.

20.
Artigo em Inglês | MEDLINE | ID: mdl-28815099

RESUMO

A primary goal of precision medicine is to identify patient subgroups based on their characteristics (e.g., comorbidities or genes) with the goal of designing more targeted interventions. While network visualization methods such as Fruchterman-Reingold have been used to successfully identify such patient subgroups in small to medium sized data sets, they often fail to reveal comprehensible visual patterns in large and dense networks despite having significant clustering. We therefore developed an algorithm called ExplodeLayout, which exploits the existence of significant clusters in bipartite networks to automatically "explode" a traditional network layout with the goal of separating overlapping clusters, while at the same time preserving key network topological properties that are critical for the comprehension of patient subgroups. We demonstrate the utility of ExplodeLayout by visualizing a large dataset extracted from Medicare consisting of readmitted hip-fracture patients and their comorbidities, demonstrate its statistically significant improvement over a traditional layout algorithm, and discuss how the resulting network visualization enabled clinicians to infer mechanisms precipitating hospital readmission in specific patient subgroups.

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