Effect of postbiotic Lactiplantibacillus plantarum LRCC5314 supplemented in powdered milk on type 2 diabetes in mice

Type 2 diabetes (T2D) is a chronic multifactorial disease characterized by a combination of insulin resistance and impaired glucose regulation. The alleviative effects of probiotics on T2D have been widely studied. However, studies on the effects of postbiotics, known as inactivated probiotics, on dairy products are limited. This study aimed to evaluate the effectiveness of postbiotic Lactiplantibacillus plantarum LRCC5314 in milk powder (MP-LRCC5314) in a stress-T2D mouse model. Compared with probiotic MP-LRCC5314, postbiotic MP-LRCC5314 significantly influenced stress-T2D-related factors. The administration of heat-killed MP-LRCC5314 reduced corticosterone levels, increased short-chain fatty acid production by modulating gut microbiota, and regulated immune response, glucose metabolism, stress-T2D-related biomarkers in the brain, gut, and adipose tissues, as well as glucose and insulin sensitivity. Additionally, heat-killed MP-LRCC5314 treatment led to a decrease in pro-inflammatory cytokine levels and an increase in anti-inflammatory cytokine levels. Overall, these findings suggest that adding postbiotic MP-LRCC5314 to milk powder could serve as a potential supplement for stress-T2D mitigation.

Ultra-Thin GaAs Single-Junction Solar Cells for Self-Powered Skin-Compatible Electrocardiogram Sensors

GaAs thin-film solar cells have high efficiency, reliability, and operational stability, making them a promising solution for self-powered skin-conformal biosensors. However, inherent device thickness limits suitability for such applications, making them uncomfortable and unreliable in flexural environments. Therefore, reducing the flexural rigidity becomes crucial for integration with skin-compatible electronic devices. Herein, this study demonstrated a novel one-step surface modification bonding methodology, allowing a streamlined transfer process of ultra-thin (2.3 µm thick) GaAs solar cells on flexible polymer substrates. This reproducible technique enables strong bonding between dissimilar materials (GaAs-polydimethylsiloxane, PDMS) without high external pressures and temperatures. The fabricated solar cell showed exceptional performance with an open-circuit voltage of 1.018 V, short-circuit current density of 20.641 mA cm-2, fill factor of 79.83%, and power conversion efficiency of 16.77%. To prove the concept, the solar cell is integrated with a skin-compatible organic electrochemical transistor (OECT). Competitive electrical outputs of GaAs solar cells enabled high current levels of OECT under subtle light intensities lower than 50 mW cm-2, which demonstrates a self-powered electrocardiogram sensor with low noise (signal-to-noise ratio of 32.68 dB). Overall, this study presents a promising solution for the development of free-form and comfortable device structures that can continuously power wearable devices and biosensors.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

MOTIVATION

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

RESULTS

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

AVAILABILITY AND IMPLEMENTATION

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Prognostic significance of senescence-related tumor microenvironment genes in head and neck squamous cell carcinoma

The impact of the senescence related microenvironment on cancer prognosis and therapeutic response remains poorly understood. In this study, we investigated the prognostic significance of senescence related tumor microenvironment genes (PSTGs) and their potential implications for immunotherapy response. Using the Cancer Genome Atlas- head and neck squamous cell carcinoma (HNSC) data, we identified two subtypes based on the expression of PSTGs, acquired from tumor-associated senescence genes, tumor microenvironment (TME)-related genes, and immune-related genes, using consensus clustering. Using the LASSO, we constructed a risk model consisting of senescence related TME core genes (STCGs). The two subtypes exhibited significant differences in prognosis, genetic alterations, methylation patterns, and enriched pathways, and immune infiltration. Our risk model stratified patients into high-risk and low-risk groups and validated in independent cohorts. The high-risk group showed poorer prognosis and immune inactivation, suggesting reduced responsiveness to immunotherapy. Additionally, we observed a significant enrichment of STCGs in stromal cells using single-cell RNA transcriptome data. Our findings highlight the importance of the senescence related TME in HNSC prognosis and response to immunotherapy. This study contributes to a deeper understanding of the complex interplay between senescence and the TME, with potential implications for precision medicine and personalized treatment approaches in HNSC.

Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease

BACKGROUND

Hypertensive disorders during pregnancy are associated with the risk of long-term cardiovascular disease after pregnancy, but it has not yet been determined whether genetic predisposition for hypertensive disorders during pregnancy can predict the risk for long-term cardiovascular disease.

OBJECTIVE

This study aimed to evaluate the risk for long-term atherosclerotic cardiovascular disease according to polygenic risk scores for hypertensive disorders during pregnancy.

STUDY DESIGN

Among UK Biobank participants, we included European-descent women (n=164,575) with at least 1 live birth. Participants were divided according to genetic risk categorized by polygenic risk scores for hypertensive disorders during pregnancy (low risk, score ≤25th percentile; medium risk, score 25th∼75th percentile; high risk, score >75th percentile), and were evaluated for incident atherosclerotic cardiovascular disease, defined as the new occurrence of one of the following: coronary artery disease, myocardial infarction, ischemic stroke, or peripheral artery disease.

RESULTS

Among the study population, 2427 (1.5%) had a history of hypertensive disorders during pregnancy, and 8942 (5.6%) developed incident atherosclerotic cardiovascular disease after enrollment. Women with high genetic risk for hypertensive disorders during pregnancy had a higher prevalence of hypertension at enrollment. After enrollment, women with high genetic risk for hypertensive disorders during pregnancy had an increased risk for incident atherosclerotic cardiovascular disease, including coronary artery disease, myocardial infarction, and peripheral artery disease, compared with those with low genetic risk, even after adjustment for history of hypertensive disorders during pregnancy.

CONCLUSION

High genetic risk for hypertensive disorders during pregnancy was associated with increased risk for atherosclerotic cardiovascular disease. This study provides evidence on the informative value of polygenic risk scores for hypertensive disorders during pregnancy in prediction of long-term cardiovascular outcomes later in life.

Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks

BACKGROUND

Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease.

METHODS

We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses.

RESULTS

The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype.

CONCLUSION

We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

MOTIVATION

Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs.

RESULTS

We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity.

AVAILABILITY AND IMPLEMENTATION

The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

P-530 tRNA fragments in follicle fluid can be explored in Diminished Ovarian Reserve patients as a marker of IVF outcomes

Study question

Can tRNA fragments in follicle fluid be explored for markers that predict blastocyst formation in patients with diminished ovarian reserve?

Summary answer

tRNA-Ser-CGA-4 is up-regulated in the follicle fluid of patients with diminished ovarian reserve (DOR)

What is known already

tRNA fragments are a novel class of small non-coding RNAs that have been shown to play regulatory roles in reproductive biology.

Study design, size, duration

Non-Randomized case control study in patients presenting for IVF treatment at a University affiliated fertility clinic from September 2020- January 2021. Thirty three patients were included: 15 patients with DOR and 18 controls.

Participants/materials, setting, methods

Patients were stimulated by appropriate protocols according to their age, diagnosis and AMH.

At retrieval, non-bloody follicle fluid (FF) was retrieved for after removing the cumulus-oophorus complex. FF was centrifuged at 1600 g x 10 mins and filtered with 80 nm filter for analysis.

Main results and the role of chance

There were no differences between the two groups with respect to age, BMI and duration of infertility. The mean age was 35.1 years

As anticipated, the mean AMH, antral follicle count and number of eggs retrieved were statistically lower in the DOR groups compared with controls (<0.05)

The fertilization rate was 61.2% in DOR vs 79.5% in controls

The blastocyst rate was 39.5% in DOR patients and 50.6% in controls

Total RNA yield was lower in DOR patients, as 50% of the DOR samples had undetectable levels (p < 0.001).

Samples were predominantly processed in duplicate. Input into the pipeline was paired end data which showed an overall tRNA fragment count that was higher in the DOR population. Log2FC showed an up-regulation of the tRNA fragments tRNA-Ser-CGA-4, tRNA-Cys-GCA-15, tRNA-Cys-GCA-10 and tRNA-Cys-GCA-19 in the DOR population.

Limitations, reasons for caution

Undetectable levels in follicle fluid of some of the DOR patients introduced selection bias

Larger sample size and broader applicability across various ethnic groups.

Wider implications of the findings

Follicle fluid can be explored to give insight into the signaling pathways of ovarian biology

Non-invasive method of assessing oocyte competence

Useful in explaining the pathophysiology of DOR

Can these findings assist is in the identification of therapeutic targets to delay the progression of age related/non-age related DOR?

Trial registration number

not applicable

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.

NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results

BACKGROUND

Disease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology-common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications.

RESULTS

To improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals' genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individual's multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank

BACKGROUND

Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications.

METHODS

We built a disease-disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge.

RESULTS

A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN.

CONCLUSION

Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.

Determining the Degree of Dopaminergic Denervation Based on the Loss of Nigral Hyperintensity on SMWI in Parkinsonism

BACKGROUND AND PURPOSE

Nigrostriatal dopaminergic function in patients with Parkinson disease can be assessed using ¹²³I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropan dopamine transporter (¹²³I-FP-CIT) SPECT, and a good correlation has been demonstrated between nigral status on SWI and dopaminergic denervation on ¹²³I-FP-CIT SPECT. Here, we aim to correlate quantified dopamine transporter attenuation on ¹²³I-FP-CIT SPECT with nigrosome-1 status using susceptibility map-weighted imaging (SMWI).

MATERIALS AND METHODS

Between May 2017 and January 2018, consecutive patients with idiopathic Parkinson disease (n = 109) and control participants (n = 29) who underwent ¹²³I-FP-CIT SPECT with concurrent 3T SWI were included. SMWI was generated from SWI. Two neuroradiologists evaluated nigral hyperintensity from nigrosome-1 on each side of the substantia nigra. Using consensus reading, we compared the ¹²³I-FP-CIT-specific binding ratio according to nigral hyperintensity status and the ¹²³I-FP-CIT specific binding ratio threshold to confirm the loss of nigral hyperintensity was determined using receiver operating characteristic curve analysis.

RESULTS

The concordance rate between SMWI and ¹²³I-FP-CIT SPECT was 65.9%. The ¹²³I-FP-CIT-specific binding ratios in the striatum, caudate nucleus, and putamen were significantly lower when nigral hyperintensity in the ipsilateral substantia nigra was absent than when present (all, P < .001). The ¹²³I-FP-CIT-specific binding ratio threshold values for the determination of nigral hyperintensity loss were 2.56 in the striatum (area under the curve, 0.890), 3.07 in the caudate nucleus (0.830), and 2.36 in the putamen (0.887).

CONCLUSIONS

Nigral hyperintensity on SMWI showed high positive predictive value and low negative predictive value with dopaminergic degeneration on ¹²³I-FP-CIT SPECT. In patients with Parkinson disease, the loss of nigral hyperintensity is prominent in patients with lower striatal specific binding ratios.

Molecular insights into the development of hepatic metastases in colorectal cancer: a metastasis prediction study

OBJECTIVE

Colorectal cancer is presently the third most commonly diagnosed cancer in the United States. In this study, we identified molecular differences between hepatic and non-hepatic metastases in colorectal cancer and evaluated their prognostic significance.

MATERIALS AND METHODS

We downloaded primary data from the NCBI Gene Expression Omnibus (GSE6988, GSE62321, GSE50760, and GSE28722). To identify the molecular differences, we used the Significance Analysis of Microarray method. We selected nine prognostic genes (SYTL2, PTPLAD1, CDS1, RNF138, PIGR, WDR78, MYO7B, TSPAN3, and ATP5F1) with hepatic metastasis prediction score in colorectal cancer (hereafter referred to as LASSO Score). We confirmed the prognostic significance of the LASSO Score by using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristic curve at 1-5 years.

RESULTS

Survival analysis revealed that a high LASSO Score is associated with a poor prognosis in colorectal cancer patients with hepatic metastases (p = 0). Analysis of C-indices and AUC values from the receiver operating characteristic curve further supported this prediction by the LASSO Score. Multivariate analysis confirmed the prognostic significance of the LASSO Score (p = 1.13e-06).

CONCLUSIONS

This study reveals the biological mechanisms underlying hepatic metastases in colorectal cancer and will help in developing targeted therapies for colorectal cancer.

Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics

BACKGROUND AND PURPOSE

Human papillomavirus is a prognostic marker for oropharyngeal squamous cell carcinoma. We aimed to determine the value of CT-based radiomics for predicting the human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

MATERIALS AND METHODS

Eighty-six patients with oropharyngeal squamous cell carcinoma were retrospectively collected and grouped into training (n = 61) and test (n = 25) sets. For human papillomavirus status and overall survival prediction, radiomics features were selected via a random forest-based algorithm and Cox regression analysis, respectively. Relevant features were used to build multivariate Cox regression models and calculate the radiomics score. Human papillomavirus status and overall survival prediction were assessed via the area under the curve and concordance index, respectively. The models were validated in the test and The Cancer Imaging Archive cohorts (n = 78).

RESULTS

For prediction of human papillomavirus status, radiomics features yielded areas under the curve of 0.865, 0.747, and 0.834 in the training, test, and validation sets, respectively. In the univariate Cox regression, the human papillomavirus status (positive: hazard ratio, 0.257; 95% CI, 0.09-0.7; P = .008), T-stage (≥III: hazard ratio, 3.66; 95% CI, 1.34-9.99; P = .011), and radiomics score (high-risk: hazard ratio, 3.72; 95% CI, 1.21-11.46; P = .022) were associated with overall survival. The addition of the radiomics score to the clinical Cox model increased the concordance index from 0.702 to 0.733 (P = .01). Validation yielded concordance indices of 0.866 and 0.720.

CONCLUSIONS

CT-based radiomics may be useful in predicting human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

Benchmarking an 11-qubit quantum computer

The field of quantum computing has grown from concept to demonstration devices over the past 20 years. Universal quantum computing offers efficiency in approaching problems of scientific and commercial interest, such as factoring large numbers, searching databases, simulating intractable models from quantum physics, and optimizing complex cost functions. Here, we present an 11-qubit fully-connected, programmable quantum computer in a trapped ion system composed of 13 ¹⁷¹Yb⁺ ions. We demonstrate average single-qubit gate fidelities of 99.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, average two-qubit-gate fidelities of 97.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, and SPAM errors of 0.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%. To illustrate the capabilities of this universal platform and provide a basis for comparison with similarly-sized devices, we compile the Bernstein-Vazirani and Hidden Shift algorithms into our native gates and execute them on the hardware with average success rates of 78\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% and 35\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, respectively. These algorithms serve as excellent benchmarks for any type of quantum hardware, and show that our system outperforms all other currently available hardware.

The growing complexity of quantum computing devices makes presents challenges for benchmarking their performance as previous, exhaustive approaches become infeasible. Here the authors characterise the quality of their 11-qubit device by successfully computing two quantum algorithms.

The translational network for metabolic disease - from protein interaction to disease co-occurrence

Background

The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network.

Results

We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network.

Conclusions

The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

Drug repurposing with network reinforcement

Background

Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data.

Results

The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations.

Conclusions

The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2858-6) contains supplementary material, which is available to authorized users.

Cascade recurring deep networks for audible range prediction

Background

Hearing Aids amplify sounds at certain frequencies to help patients, who have hearing loss, to improve the quality of life. Variables affecting hearing improvement include the characteristics of the patients’ hearing loss, the characteristics of the hearing aids, and the characteristics of the frequencies. Although the two former characteristics have been studied, there are only limited studies predicting hearing gain, after wearing Hearing Aids, with utilizing all three characteristics. Therefore, we propose a new machine learning algorithm that can present the degree of hearing improvement expected from the wearing of hearing aids.

Methods

The proposed algorithm consists of cascade structure, recurrent structure and deep network structure. For cascade structure, it reflects correlations between frequency bands. For recurrent structure, output variables in one particular network of frequency bands are reused as input variables for other networks. Furthermore, it is of deep network structure with many hidden layers. We denote such networks as cascade recurring deep network where training consists of two phases; cascade phase and tuning phase.

Results

When applied to medical records of 2,182 patients treated for hearing loss, the proposed algorithm reduced the error rate by 58% from the other neural networks.

Conclusions

The proposed algorithm is a novel algorithm that can be utilized for signal or sequential data. Clinically, the proposed algorithm can serve as a medical assistance tool that fulfill the patients’ satisfaction.

An inference method from multi-layered structure of biomedical data

Background

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels.

Methods

To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer.

Results

The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results.

Conclusion

This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.

Geriatric oral and maxillofacial dysfunctions in the context of geriatric syndrome

OBJECTIVES

To propose the application of the concept of geriatric syndrome for common geriatric oral and maxillofacial dysfunctions and to suggest the necessity of developing effective evaluation methods for oral and maxillofacial frailty.

DESIGN

The concepts of frailty and geriatric syndrome based on multi-morbidity and polypharmacy were applied to five common geriatric oral medicinal dysfunctional problems: salivary gland hypofunction (dry mouth), chronic oral mucosal pain disorders (burning mouth symptoms), taste disorders (taste disturbances), swallowing disorders (dysphagia), and oral and maxillofacial movement disorders (oromandibular dyskinesia and dystonia).

RESULTS

Each of the dysfunctions is caused by various kinds of diseases and/or conditions and medications, thus the concept of geriatric syndrome could be applied. These dysfunctions, suggested as components of oral and maxillofacial geriatric syndrome, are associated and interacted with each other in a complexity of vicious cycle. The resulting functional impairments caused by this syndrome can cause oral and maxillofacial frailty.

CONCLUSIONS

Geriatric oral and maxillofacial dysfunctions could be better appreciated in the context of geriatric syndrome. The development of effective methods for evaluating the severity of these dysfunctions and the resulting frailty is essential.

High-resolution electrohydrodynamic inkjet printing of stretchable metal oxide semiconductor transistors with high performance

As demands for high pixel densities and wearable forms of displays increase, high-resolution printing technologies to achieve high performance transistors beyond current amorphous silicon levels and to allow low-temperature solution processability for plastic substrates have been explored as key processes in emerging flexible electronics. This study describes electrohydrodynamic inkjet (e-jet) technology for direct printing of oxide semiconductor thin film transistors (TFTs) with high resolution (minimum line width: 2 μm) and superb performance, including high mobility (∼230 cm² V^-1 s^-1). Logic operations of the amplifier circuits composed of these e-jet-printed metal oxide semiconductor (MOS) TFTs demonstrate their high performance. Printed In₂O TFTs with e-jet printing-assisted high-resolution S/D electrodes were prepared, and the direct printing of passivation layers on these channels enhanced their gate-bias stabilities significantly. Moreover, low process temperatures (<250 °C) enable the use of thin plastic substrates; highly flexible and stretchable TFT arrays have been demonstrated, suggesting promise for next-generation printed electronics.

CLASH: Complementary Linkage with Anchoring and Scoring for Heterogeneous biomolecular and clinical data

Background

The study on disease-disease association has been increasingly viewed and analyzed as a network, in which the connections between diseases are configured using the source information on interactome maps of biomolecules such as genes, proteins, metabolites, etc. Although abundance in source information leads to tighter connections between diseases in the network, for a certain group of diseases, such as metabolic diseases, the connections do not occur much due to insufficient source information; a large proportion of their associated genes are still unknown. One way to circumvent the difficulties in the lack of source information is to integrate available external information by using one of up-to-date integration or fusion methods. However, if one wants a disease network placing huge emphasis on the original source of data but still utilizing external sources only to complement it, integration may not be pertinent. Interpretation on the integrated network would be ambiguous: meanings conferred on edges would be vague due to fused information.

Methods

In this study, we propose a network based algorithm that complements the original network by utilizing external information while preserving the network’s originality. The proposed algorithm links the disconnected node to the disease network by using complementary information from external data source through four steps: anchoring, connecting, scoring, and stopping.

Results

When applied to the network of metabolic diseases that is sourced from protein-protein interaction data, the proposed algorithm recovered connections by 97%, and improved the AUC performance up to 0.71 (lifted from 0.55) by using the external information outsourced from text mining results on PubMed comorbidity literatures. Experimental results also show that the proposed algorithm is robust to noisy external information.

Conclusion

This research has novelty in which the proposed algorithm preserves the network’s originality, but at the same time, complements it by utilizing external information. Furthermore it can be utilized for original association recovery and novel association discovery for disease network.

A superhydrophilic nitinol shape memory alloy with enhanced anti-biofouling and anti-corrosion properties

This work reports on a nitinol (NiTi) surface modification scheme based on a chemical oxidation method, and characterizes its effects on wetting, biofouling and corrosion. The scheme developed is also compared with selected previous oxidation methods. The proposed method turns NiTi into superhydrophilic in ~5 min, and the static contact angle and contact angle hysteresis were measured to be ~7° and ~12°, respectively. In the PRP (platelet rich plasma) test, platelet adhesion was reduced by ~89% and ~77% respectively, compared with the original NiTi and the NiTi treated with the previous chemical oxidation scheme. The method developed provides a high (~1.1 V) breakdown voltage, which surpasses the ASTM standard for intervascular medical devices. It also provides higher superhydrophilicity, hemo-compatibility and anti-corrosion resistance than previous oxidation schemes, with a significantly reduced process time (~5 min), and will help the development of high performance NiTi devices.

Disseminated mucormycosis with myocardial involvement in a renal transplant recipient

We report the case of a renal transplant recipient with pulmonary and splenic mucormycosis whose demise was accelerated by a myocardial abscess. Once pulmonary and splenic mucormycosis was diagnosed, liposomal amphotericin B was started and immunosuppressant treatments were discontinued. The pulmonary cavities regressed during treatment, but new myocardial and peri-allograft abscesses developed. The myocardial abscess diffusely infiltrated the left ventricular wall and was associated with akinesia, which led to sudden cardiac arrest. This case demonstrates a rare manifestation of mucormycosis and highlights the fatality and invasiveness of this infection.

Quasi 3D ECE imaging system for study of MHD instabilities in KSTAR

A second electron cyclotron emission imaging (ECEI) system has been installed on the KSTAR tokamak, toroidally separated by 1/16th of the torus from the first ECEI system. For the first time, the dynamical evolutions of MHD instabilities from the plasma core to the edge have been visualized in quasi-3D for a wide range of the KSTAR operation (B0 = 1.7∼3.5 T). This flexible diagnostic capability has been realized by substantial improvements in large-aperture quasi-optical microwave components including the development of broad-band polarization rotators for imaging of the fundamental ordinary ECE as well as the usual 2nd harmonic extraordinary ECE.

A coupling approach of a predictor and a descriptor for breast cancer prognosis

Background

In cancer prognosis research, diverse machine learning models have applied to the problems of cancer susceptibility (risk assessment), cancer recurrence (redevelopment of cancer after resolution), and cancer survivability, regarding an accuracy (or an AUC--the area under the ROC curve) as a primary measurement for the performance evaluation of the models. However, in order to help medical specialists to establish a treatment plan by using the predicted output of a model, it is more pragmatic to elucidate which variables (markers) have most significantly influenced to the resulting outcome of cancer or which patients show similar patterns.

Methods

In this study, a coupling approach of two sub-modules--a predictor and a descriptor--is proposed. The predictor module generates the predicted output for the cancer outcome. Semi-supervised learning co-training algorithm is employed as a predictor. On the other hand, the descriptor module post-processes the results of the predictor module, mainly focusing on which variables are more highly or less significantly ranked when describing the results of the prediction, and how patients are segmented into several groups according to the trait of common patterns among them. Decision trees are used as a descriptor.

Results

The proposed approach, 'predictor-descriptor,' was tested on the breast cancer survivability problem based on the surveillance, epidemiology, and end results database for breast cancer (SEER). The results present the performance comparison among the established machine leaning algorithms, the ranks of the prognosis elements for breast cancer, and patient segmentation. In the performance comparison among the predictor candidates, Semi-supervised learning co-training algorithm showed best performance, producing an average AUC of 0.81. Later, the descriptor module found the top-tier prognosis markers which significantly affect to the classification results on survived/dead patients: 'lymph node involvement', 'stage', 'site-specific surgery', 'number of positive node examined', and 'tumor size', etc. Also, a typical example of patient-segmentation was provided: the patients classified as dead were grouped into two segments depending on difference in prognostic profiles, ones with serious results with respect to the pathologic exams and the others with the feebleness of age.