Optimal Nutrition Formulas for Patients Undergoing Surgery for Colorectal Cancer: A Bayesian Network Analysis

Jiang, Xiaohan; Chen, Xijie; Wang, Xin-You; Chen, Yunzhi; Xie, Qinqin; Peng, Junsheng

doi:10.1080/01635581.2020.1770812

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…A study of 205,840 oncologic operations by McKenna et al [10] reported that the optimal definition of malnutrition varied by cancer type and that there was not a one-size-fits-all approach. A recent indirect comparison study of different immunonutrition formulas in patients undergoing surgery for colorectal cancer con- cluded that glutamine had some superiority for reducing complications and hospital stays, but ultimately determined that the results were close enough that large scale randomized controlled trials would be required [2]. Some institutions have even adopted "surgical wellness" programs to optimize medical conditions, which included providing immune-nutrition supplements and were found to reduce length of stay following pancreatectomy [24].…”

Section: Discussionmentioning

confidence: 99%

“…Appropriate preoperative nutritional status has been found to be an important factor in reducing surgical complication, morbidity, and mortality [1][2][3][4][5][6][7][8][9][10]. Thus, preoperative nutritional screening and subsequent optimization may be useful for many groups of patients, especially those undergoing surgery in the setting of cancer or comorbidities.…”

Section: Introductionmentioning

confidence: 99%

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Routine Preoperative Nutritional Optimization Not Required in Patients Undergoing Breast Reconstruction

et al. 2022

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Purpose There are no data describing the need for preoperative nutritional optimization in patients undergoing breast reconstruction surgery. The purpose of this research is to identify if preoperative breast reconstruction patients are grossly nutritionally deficient and, thus, if routine preoperative nutrition screening and optimization is necessary in this patient population. Methods Adult patients who underwent breast reconstruction surgery between 2015 - 2019 were identified within the National Safety and Quality Improvement Program (NSQIP) database. Variables of interest for this group of patients were collected, and comparisons were made for these variables between three different breast reconstruction modalities (implant-based reconstruction [IBR], expander-based reconstruction [EBR], free tissue-based reconstruction [FTBR]) with χ2 and analysis of variance (ANOVA) statistical tests. Results A total of 14,509 patients were included. Comparing groups, FTBR had higher incidence of superficial surgical site infection (SSI) [4.49% vs 1.6% vs 1.56%, respectively; p < .00001], deep SSI (1.57% vs 0.48% vs 0.94%, respectively; p < .00001), and wound disruption (2.16% vs 0.78% vs 0.94%, respectively; p < .00001) than IBR and EBR. There was not a significant difference in preoperative serum albumin measurements between the three reconstruction modalities; mean measurements for the three reconstruction modalities were all within normal limits. Conclusion Preoperative nutritional status was found to be grossly appropriate in a large population of breast reconstruction patients and routine preoperative nutrition screening and optimization is not necessary. Free tissue-based reconstruction incurred the greatest risk of surgical complication independent of preoperative nutritional status.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Routine Preoperative Nutritional Optimization Not Required in Patients Undergoing Breast Reconstruction

et al. 2022

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“…Bayesian networks (BNs) [188] are a long-standing form of statistical network modeling used to reverse-engineer probabilistic causality among variables; with the development of highthroughput sequencing technology, BNs have been widely used to infer causal gene regulatory networks in different diseases [189][190][191][192][193][194]. Recent studies have applied BNs to infer molecular mechanisms and key drivers in Alzheimer's disease [22,128].…”

Section: 7) Bayesian Network Predictive Network and Network Validationmentioning

confidence: 99%

Predictive Network Analysis IdentifiesJMJD6and Other Novel Key Drivers in Alzheimer’s Disease

Merchant

Zhu

Henrion

et al. 2022

Preprint

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Despite decades of genetic studies on late onset Alzheimer disease (LOAD), the molecular mechanisms of Alzheimer disease (AD) remain unclear. Furthermore, different cell types in the central nervous system (CNS) play distinct roles in the onset and progression of AD pathology. To better comprehend the complex etiology of AD, we used an integrative approach to build robust predictive (causal) network models which were cross-validated over multiple large human multi-omics datasets in AD. We employed a published method to delineate bulk-tissue gene expression into single cell-type gene expression and integrated clinical and pathologic traits of AD, single nucleotide variation, and deconvoluted gene expression for the construction of predictive network models for each cell type in AD. With these predictive causal models, we are able to identify and prioritize robust key drivers of the AD-associated network state. In this study, we focused on neuron-specific network models and prioritized 19 predicted key drivers modulating AD pathology. These targets were validated via shRNA knockdown in human induced pluripotent stem cell (iPSC) derived neurons (iNs), in which 10 out of the 19 neuron-related targets (JMJD6, NSF, NUDT2, YWHAZ, RBM4, DCAF12, NDRG4, STXBP1, ATP1B1, and FIBP) significantly modulated levels of amyloid-beta and/or phosphorylated tau peptides in the postmitotic iNs. Most notably, knockdown of JMJD6 significantly altered the neurotoxic ratios of Aβ to 40 and p231-tau to total tau, indicating its potential therapeutic relevance to both amyloid and tau pathology in AD. Molecular validation by RNA sequencing (RNAseq) in iNs further confirmed the network structure, showing significant enrichment in differentially expressed genes after knockdown of the validated targets. Interestingly, our network model predicts that these 10 key drivers are upstream regulators of REST and VGF, two recently identified key regulators of AD pathogenesis.

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“…After deconvolution, we applied a cutting-edge systems biology approach 23,59,60 to build causal network models of the neuronal component of AD by integrating the deconvoluted neuronspecific RNAseq data with the whole-genome genotype data from the MAYO and ROSMAP datasets. Bayesian networks 61 are a long-standing form of statistical network modeling used to reverse-engineer probabilistic causality among variables; with the development of high-throughput sequencing technology, Bayesian networks have been widely used to infer causal gene regulatory networks in different diseases [62][63][64][65][66][67] . Recent studies have applied Bayesian networks to infer molecular mechanisms and key drivers in Alzheimer's disease 24,68 .…”

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confidence: 99%

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease

et al. 2023

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Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer’s pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer’s-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer’s disease.

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Optimal Nutrition Formulas for Patients Undergoing Surgery for Colorectal Cancer: A Bayesian Network Analysis

Cited by 6 publications

References 26 publications

Routine Preoperative Nutritional Optimization Not Required in Patients Undergoing Breast Reconstruction

Routine Preoperative Nutritional Optimization Not Required in Patients Undergoing Breast Reconstruction

Predictive Network Analysis IdentifiesJMJD6and Other Novel Key Drivers in Alzheimer’s Disease

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease

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