Development and validation of a novel strong prognostic index for colon cancer through a robust combination of laboratory features for systemic inflammation: a prognostic immune nutritional index.

BACKGROUND: Systemic inflammation is associated with survival outcomes in colon cancer. However, it is not well-known which systemic inflammatory marker is a powerful prognostic marker in patients with colon cancer. METHODS: A total of 4535 colon cancer patients were included in this study. We developed a novel prognostic index using a robust combination of seven systemic inflammation-associated blood features of the discovery set. The predictability and generality of the novel prognostic index were evaluated in the discovery, validation and replication sets. RESULTS: Among all combinations, the combination of albumin and monocyte count was the best candidate expression. The final formula of the proposed novel index is named the Prognostic Immune and Nutritional Index (PINI). The concordance index of PINI for overall and progression-free survival was the highest in the discovery, validation and replication sets compared to existing prognostic inflammatory markers. PINI was found to be a significant independent prognostic factor for both overall and progression-free survival. CONCLUSIONS: PINI is a novel prognostic index that has improved discriminatory power in colon cancer patients and appears to be superior to existing prognostic inflammatory markers. PINI can be utilised for decision-making regarding personalised treatment as the complement of the TNM staging system.

HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.

MOTIVATION: Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. RESULTS: In this article, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. AVAILABILITYAND IMPLEMENTATION: https://github.com/JaesikKim/HiG2Vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A deep learning model for screening type 2 diabetes from retinal photographs.

BACKGROUND AND AIMS: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. METHODS AND RESULTS: The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%. CONCLUSION: Our results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.

Evaluation of MARK BTM for Quantitative Measurement of Three Tumor Markers: Prostate Specific Antigen, Alpha Fetoprotein, and Carcinoembryonic Antigen.

BACKGROUND: Tumor marker assays have played a crucial role for screening cancers and monitoring cancer patients, as they reflect the status and prognosis of patients. Alpha fetoprotein (AFP), prostate specific antigen (PSA), and carcinoembryonic antigen (CEA) are the most commonly used tumor marker proteins. The MARK BTM immunoassay system is a novel platform based on magnetic nanoparticles and electrochemical immunoassay. METHODS: The analytical performance of MARK BTM immunoassay system for determination of AFP, PSA, and CEA are evaluated. Comparisons of methods are also conducted by comparing the assay results of MARK BTM immunoassay system to that of cobas e 801 system. RESULTS: The MARK BTM immunoassay system provides within-run, between-run, and between-day precisions for the three tumor markers, ranging from 1.13 - 7.46%. Data measured by the MARK BTM immunoassay system show high correlation with that of the cobas e 801 system, with a linear slope ranging from 0.966 to 1.042 and a correlation coefficient of r > 0.996 for the three markers. CONCLUSIONS: These results show that the MARK BTM immunoassay system can be used for the quantitative measurements of AFP, PSA, and CEA in clinical practice.

Improved extraction of resveratrol and antioxidants from grape peel using heat and enzymatic treatments.

BACKGROUND: Resveratrol, an extensively recognized phytochemical that belongs to the stilbene family, is abundant in grape peel which is discarded as a by-product during grape juice processing. RESULTS: In this study, we established that pre-heating grape peel above 75 °C significantly improved the extractability of resveratrol and its glucoside piceid. In particular, thermal heating of grape peel at 95 °C for 10 min, followed by treatment with a mixture of exo-1,3-ß-glucanase and pectinases at 50 °C for 60 min, dramatically increased the conversion of piceid into resveratrol and the overall extractability of this phytochemical by 50%. Furthermore, thermal pre-treatment promoted a substantial increase in the total phenol, flavonoid, and anthocyanin concentrations in the grape peel extract. Ultimately, resveratrol-enriched grape peel extract significantly augmented the antioxidant response in vitro, possibly by attenuating the accumulation of intracellular reactive oxygen species via the Nrf2 signaling pathway. CONCLUSION: The method developed in this study for preparing grape peel extract introduces a potential low-cost green processing for the industrial fortification of food products with resveratrol and other health-beneficial antioxidants. © 2019 Society of Chemical Industry.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Enterotypical Prevotella and three novel bacterial biomarkers in preoperative stool predict the clinical outcome of colorectal cancer.

BACKGROUND: A significant proportion of colorectal cancer (CRC) patients suffer from early recurrence and progression after surgical treatment. Although the gut microbiota is considered as a key player in the initiation and progression of CRC, most prospective studies have been focused on a particular pathobionts such as Fusobacterium nucleatum. Here, we aimed to identify novel prognostic bacteria for CRC by examining the preoperative gut microbiota through 16S ribosomal RNA gene sequencing. RESULTS: We collected stool samples from 333 patients with primary CRC within 2 weeks before surgery and followed up the patients for a median of 27.6 months for progression and 43.6 months for survival. The sequence and prognosis data were assessed using the log-rank test and multivariate Cox proportional hazard analysis. The gut microbiota was associated with the clinical outcomes of CRC patients (Pprogress = 0.011, Pdecease = 0.007). In particular, the high abundance of Prevotella, a representative genus of human enterotypes, indicated lower risks of CRC progression (P = 0.026) and decease (P = 0.0056), while the occurrence of Alistipes assigned to Bacteroides sp., Pyramidobacter piscolens, Dialister invisus, and Fusobacterium nucleatum indicated a high risk of progression. A microbiota-derived hazard score considering the five prognostic bacteria accurately predicted CRC progression in 1000 random subsamples; it outperformed widely accepted clinical biomarkers such as carcinoembryonic antigen and lymphatic invasion, after adjustment for the clinicopathological stage (adjusted HR 2.07 [95% CI, 1.61-2.64], P = 7.8e-9, C-index = 0.78). PICRUSt2 suggested that microbial pathways pertaining to thiamine salvage and L-histidine degradation underlie the different prognoses. CONCLUSIONS: The enterotypical genus Prevotella was demonstrated to be useful in improving CRC prognosis, and combined with the four pathobionts, our hazard score based on the gut microbiota should provide an important asset in predicting medical outcomes for CRC patients. Video Abstract.

Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.

A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning.

BACKGROUND: Preoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using blood features derived from machine learning. METHODS: Patients who underwent total mesorectal excision after preoperative CRT were included in this study. The performance of machine learning models using blood features before CRT (pre-CRT) and from 1 to 2 weeks after CRT (early-CRT) was evaluated. Based on the best model, important features were selected. The scoring system was developed from the selected model and features. The performance of the new scoring system was compared with those of systemic inflammatory indicators: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and the prognostic nutritional index. RESULTS: The models using early-CRT blood features had better performances than those using pre-CRT blood features. Based on the ridge regression model, which showed the best performance among the machine learning models (AUROC 0.6322 and AUPRC 0.5965), a novel scoring system for the response of preoperative CRT, named Response Prediction Score (RPS), was developed. The RPS system showed higher predictive power (AUROC 0.6747) than single blood features and systemic inflammatory indicators and stratified the tumor regression grade and overall downstaging clearly. CONCLUSION: We discovered that we can more accurately predict CRT response by using early-treatment blood data. With larger data, we can develop a more accurate and reliable indicator that can be used in real daily practices. In the future, we urge the collection of early-treatment blood data and pre-treatment blood data.

Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.

BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS: We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS: The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS: In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo.

Grape Peel Extract and Resveratrol Inhibit Wrinkle Formation in Mice Model Through Activation of Nrf2/HO-1 Signaling Pathway.

Considering the anti-photoaging effect of antioxidant compounds, we investigated the protective capacity of grape peel extract (GPE) and resveratrol on ultraviolet (UV)-induced skin wrinkle formation. Total phenolic, total anthocyanin, and total flavonoid content in GPE prepared from peel of Campbell Early variety were 23.96 ± 0.09, 3.27 ± 0.40, and 1.24 ± 0.09 mg/g dry weight, respectively. Additionally, trans-resveratrol and piceid content of the resulting GPE were 117.14 ± 19.97 and 85.23 ± 8.89 µg/g dry weight, respectively. Oral administration of either 2 g GPE or 2 mg resveratrol per kg body weight in mice attenuated UVB-induced epidermal thickening (the thickness was reduced by about 63% and 55% with GPE and resveratrol consumption prior to exposure to UVB, respectively, compared to only UVB-treated condition) and had marginally protective effect on wrinkle formation of skin exposed to UVB. As introduction of either GPE or resveratrol induced Nrf2-dependent antioxidant enzymes including heme oxygenase-1 (HO-1) in liver and skin as well as inhibited metalloproteinases, it is highly probable that the extract or resveratrol mitigated UVB-induced photoaging through activation of Nrf2/HO-1 signaling pathway. PRACTICAL APPLICATION: This study proved that resveratrol and the extract of grape peel, a common by-product of grape juice processing, provide effective protection from UV-induced skin wrinkle formation. Therefore, grape peel extract, which contains an appreciable amount of bioactive compound resveratrol, can be utilized as functional food ingredient for the manufacture of inner beauty products.

MESIA: Magnetic force-assisted electrochemical sandwich immunoassays for quantification of prostate-specific antigen in human serum.

We propose a new immunoassay technique, called magnetic-force assisted electrochemical sandwich immunoassay (MESIA), where serum biomarkers can be determined by magnetic actuation and electrochemical detection of gold-coated iron oxide nanoparticles as probes for immunocomplex formation. In MESIA, neither washing buffer nor fluidic parts are necessary, because the formation of immunocomplexes and the removal of unbound probes are controlled by magnetic forces. Electrochemical pretreatment and measurement of the gold-coated magnetic probes allows highly sensitive, precise, and robust system for quantification of target analytes. Using MESIA, the concentration of prostate-specific antigen (PSA) in 10 µl of human serum is determined within 5 min. The limit of detection is 0.085 ng/mL, and the average coefficient of variance is 8.85% for five different PSA concentrations ranging from 0 to 25 ng/mL. This method shows good precision and reproducibility (<10%) and high correlation with cobas e 801 (r = 0.997) for clinical patient samples. We believe this technique to be useful in the development of a point-of-care testing platform for diagnosis and prognosis of various diseases, such as cancer, based on quantification of biomarkers in a drop of blood.

In vitro and in vivo anti-inflammatory activities of mixed fruit and vegetable juice.

In this study, we attempted to develop fruit-based functional juice with anti-inflammatory activity for consumers living under heavy air pollution. At first, four different formulations for functional juice prepared by combining 10 kinds of fruits and other ingredients in various ratios were tested for NAD(P)H: quinoneoxidoreductase 1 (NQO1)-inducing activity in mouse hepatoma Hepa1c1c7 cells. The formulation showing the highest NQO1-inducing activity [named detoxification juice (DJ)] was investigated for its anti-inflammatory activity in a co-culture system of human lung carcinoma A549 cell line and a differentiated human monocytic cell line THP-1. DJ significantly reduced the LPS-induced production of proinflammatory cytokines, such as interleukin-6 (IL-6), IL-1ß, and tumor necrosis factor α (TNF-α). Furthermore, DJ administration suppressed the benzo[a]pyreneinduced elevation of plasma cytokine levels of C57BL/6 mice. However, the protective effect of DJ against inflammation in lung and trachea caused by air pollution needs to be assessed through clinical studies.

Ordered mesoporous porphyrinic carbons with very high electrocatalytic activity for the oxygen reduction reaction.

The high cost of the platinum-based cathode catalysts for the oxygen reduction reaction (ORR) has impeded the widespread application of polymer electrolyte fuel cells. We report on a new family of non-precious metal catalysts based on ordered mesoporous porphyrinic carbons (M-OMPC; M = Fe, Co, or FeCo) with high surface areas and tunable pore structures, which were prepared by nanocasting mesoporous silica templates with metalloporphyrin precursors. The FeCo-OMPC catalyst exhibited an excellent ORR activity in an acidic medium, higher than other non-precious metal catalysts. It showed higher kinetic current at 0.9 V than Pt/C catalysts, as well as superior long-term durability and MeOH-tolerance. Density functional theory calculations in combination with extended X-ray absorption fine structure analysis revealed a weakening of the interaction between oxygen atom and FeCo-OMPC compared to Pt/C. This effect and high surface area of FeCo-OMPC appear responsible for its significantly high ORR activity.