HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications.

MOTIVATION: Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary. RESULTS: In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine. AVAILABILITY AND IMPLEMENTATION: The platform is openly available at http://sysbio.org.cn/HFBD/.

Washed microbiota transplantation improves sleep quality in patients with sleep disorder by the gut-brain axis.

Background: The clinical impact of washed microbiota transplantation (WMT) from healthy donors in sleep disorder (SD) patients is unclear. This study aimed to investigate the effect of WMT in SD patients. Methods: The clinical data were collected from patients with different indications receiving 1-3 courses of WMT, divided into two groups by 7 points of PSQI scale. The score of PQSI and SF-36 scale was used to assess the improvement in sleep quality and life quality among patients with sleep disorders following WMT. Finally, 16S rRNA gene amplicon sequencing was performed on fecal samples of patients with sleep disorders before and after WMT. Results: WMT significantly improved sleep quality in patients with sleep disorder in the short and medium term. WMT significantly improved sleep latency, sleep time and total score in the short term. WMT significantly improved sleep quality and total score in the medium term. In terms of sleep quality and sleep latency, the improvement value also increased with the increase of treatment course, and the improvement effect of multiple treatment course was better than that of single and double treatment course. In the total score, the improvement effect of double and multiple treatment was better than that of single treatment. WMT also improved quality of life in the sleep disorder group. WMT significantly improved general health, vitality, social function and mental health in the short term. WMT significantly improved role-physical, general health, vitality, and mental health in the medium term. WMT regulated the disturbed gut microbiota in patients with sleep disorders. In the normal sleep group, WMT had no effect on the decline of sleep quality in the short, medium and long term, and had an improving effect on the quality of life. Conclusion: WMT could significantly improve sleep quality and life quality in patients with sleep disorders with no adverse events. The improvement in sleep quality resulting from WMT could lead to an overall enhancement in life quality. WMT could be a potentially effective treatment for patients with sleep disorders by regulating the gut microbiota.

Washed Microbiota Transplantation Is Associated With Improved Lipid Profiles: Long-Term Efficacy and Safety in an Observational Cohort From South China.

INTRODUCTION: Dyslipidemia is one of the main risk factors of chronic metabolic diseases. Our previous studies have shown that washed microbiota transplantation (WMT) has a significant improvement effect on patients with hyperlipidemia and hypolipemia in the Chinese population. The purpose of this study was to further explore the long-term efficacy and safety of WMT in patients with hyperlipidemia. METHODS: Clinical data of patients who received WMT for multicourse were collected. Changes of blood lipid indexes before and after WMT, including triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), lipoprotein A, and Apolipoprotein B. RESULTS: A total of 124 patients were enrolled, including 56 cases in the hyperlipidemia group and 68 cases with normal lipids. The mean observation time was 787.80 ± 371.45 days, and the longest follow-up time was 1,534 days. TC and non-HDL-C in the hyperlipidemia group with 1-4 courses of WMT were significantly reduced ( P < 0.05); TG decreased significantly after the second course ( P < 0.05); low-density lipoprotein cholesterol also significantly decreased after the fourth course of treatment ( P < 0.05); TG, TC, and non-HDL-C significantly decreased in single course, double course, and multiple course, respectively ( P < 0.05). In terms of time period, over 1 year, the improvement in multicourse treatment was more significant than the single and double-course ones. In terms of comprehensive efficacy, WMT restored 32.14% of patients in the hyperlipidemia group to the normal lipid group ( P < 0.001), of which 30.00% recovered to the normal lipid group within 1 year ( P = 0.004) and 65.38% were reassigned to the normal lipid group over 1 year ( P = 0.003). In addition, over the 1-year treatment period, WMT significantly degraded the high-risk and medium-risk groups of atherosclerotic cardiovascular disease risk stratification in hyperlipidemia cases. There were no serious adverse events. DISCUSSION: WMT had a long-term improvement effect on patients with hyperlipidemia. The effect of multiple courses over 1 year was more significant than that of single/double courses and also had a significant destratification effect on the risk of atherosclerotic cardiovascular disease with high safety. Therefore, WMT provides a safe and long-term effective clinical treatment for patients with dyslipidemia.

HNF1ɑ promotes colorectal cancer progression via HKDC1-mediated activation of AKT/AMPK signaling pathway.

The hepatocyte nuclear factor-1 (HNF1ɑ) is a transcription factor that contributes to several kinds of cancer progression. However, very little is known regarding the mechanisms underlying the activity of HNF1ɑ. We aimed to explore the role of HNF1ɑ in the progress of colorectal cancer (CRC) and elucidate its molecular mechanism. HNF1ɑ expression was upregulated in CRC samples and high expression of HNF1ɑ was associated with poor prognosis of CRC patients. HNF1α knockdown and overexpression inhibited and promoted proliferation, migration and invasion of CRC cells both in vitro and in vivo respectively. Mechanistically, HNF1ɑ increased the transcriptional activity of hexokinase domain component 1（HKDC1）promoter, thus activated AKT/AMPK signaling. Meanwhile, HKDC1 upregulation was important for the proliferation, migration and invasion of CRC cells and knockdown of HKDC1 significantly reversed the proliferation, migration and invasion induced by HNF1α overexpression. Taken together, HNF1ɑ contributes to CRC progression and metastasis through binding to HKDC1 and activating AKT/AMPK signaling. Targeting HNF1ɑ could be a potential therapeutic strategy for CRC patients.

Robot-assisted laparoscopic combined with endoscopic partial gastrectomy (RALE-PG) for the treatment of gastric gastrointestinal stromal tumors in challenging anatomical locations: single-center experience.

Background: Gastric gastrointestinal stromal tumors in challenging anatomical locations are difficult to remove. Methods: This study retrospectively analyzed the clinical data of 12 patients with gastric GISTs in challenging anatomical locations who underwent robot-assisted laparoscopic combined with endoscopic partial gastrectomy (RALE-PG) and manual suturing of the gastric wall. Results: This study included 12 patients with a mean age of 56.8 ± 9.8 years and a mean BMI of 23.9 ± 1.9 kg/m2. Tumors were located in the GEJ (n = 3), lesser curvature (n = 3), posterior gastric wall (n = 3) and antrum (n = 3). The cardia and pylorus were successfully preserved in all patients regardless of the tumor location. The mean tumor size was 4.5 ± 1.4 cm. The mitotic-count/50 mm2 was less than 5 in all patients (100%). There was no intraoperative tumor rupture (0%) and no conversion to open surgery (0%). The median operation time was 122 (97-240) min, and the median blood loss volume was 10 (5-30) ml. The median postoperative VAS score was 2 (2-4). The median time to first flatus was 2 (2-3) days. The median time to first fluid intake was 2 (2-3) days. The median time to first ambulation after the operation was 3 (2-4) days. No cases of anastomotic stenosis or leakage were found. The median time to drain removal for 6 patients was 5 (4-7) days. The median time to nasogastric tube removal for all patients was 2 (1-5) days. The median postoperative hospital stay was 5 (4-8) days. One patient (female/41 year) developed moderate anemia (Clavien-Dindo grade II complication). There was no unplanned readmission within 30 days after the operation. The median distance from the tumor to the resection margin was 1 (1-2) cm. R0 resection was achieved in all patients. The median follow-up period was 19 (10-25) months, and all patients survived with no recurrence or metastasis. Conclusions: RALE-PG is a safe, feasible and advantageous technique for treating GISTs in challenging anatomical locations. It can be used to accurately remove the tumor while preserving gastric function to the greatest extent, but long-term oncologic outcomes need to be evaluated in a study with a larger sample size and longer follow-up period.

Prognostic model construction and validation of esophageal cancer cellular senescence-related genes and correlation with immune infiltration.

Introduction: Cellular senescence is a cellular response to stress, including the activation of oncogenes, and is characterized by irreversible proliferation arrest. Restricted studies have provided a relationship between cellular senescence and immunotherapy for esophageal cancer. Methods: In the present study, we obtained clinical sample of colon cancer from the TCGA database and cellular senescence-related genes from MSigDB and Genecard datasets. Cellular senescence-related prognostic genes were identified by WGCNA, COX, and lasso regression analysis, and a cellular senescence-related risk score (CSRS) was calculated. We constructed a prognostic model based on CSRS. Validation was performed with an independent cohort that GSE53625. Three scoring systems for immuno-infiltration analysis were performed, namely ssGSEA analysis, ESTIMATE scores and TIDE scores. Result: Five cellular senescence-related genes, including H3C1, IGFBP1, MT1E, SOX5 and CDHR4 and used to calculate risk score. Multivariate regression analysis using cox regression model showed that cellular senescence-related risk scores (HR=2.440, 95% CI=1.154-5.159, p=0.019) and pathological stage (HR=2.423, 95% CI=1.119-5.249, p=0.025) were associated with overall survival (OS). The nomogram model predicts better clinical benefit than the American Joint Committee on Cancer (AJCC) staging for prognosis of patients with esophageal cancer with a five-year AUC of 0.946. Patients with high CSRS had a poor prognosis (HR=2.93, 95%CI=1.74-4.94, p<0.001). We observed differences in the distribution of CSRS in different pathological staging and therefore performed a subgroup survival analysis finding that assessment of prognosis by CSRS independent of pathological staging. Comprehensive immune infiltration analysis and functional enrichment analysis suggested that patients with high CSRS may develop immunotherapy resistance through mechanisms of deacetylation and methylation. Discussion: In summary, our study suggested that CSRS is a prognostic risk factor for esophageal cancer. Patients with high CSRS may have worse immunotherapy outcomes.

NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases.

INTRODUCTION: Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or function. The complex etiologies of the NDDs remain unclear, thus the prevention and early diagnosis of NDDs are critical to reducing the mortality and morbidity of these diseases. OBJECTIVES: To provide a systematic understanding of the heterogeneity of the risk factors associated with different NDDs (pan-neurodegenerative diseases or pan-NDDs), the knowledgebase is established to facilitate the personalized and knowledge-guided diagnosis, prevention and prediction of NDDs. METHODS: Before data collection, the medical, lifescienceand informatics experts as well as the potential users of the database were consulted and discussed for the scope of data and the classification of risk factors. The PubMed database was used as the resource of the data and knowledge extraction. Risk factors of NDDs were manually collected from literature published between 1975 and 2020. RESULTS: The comprehensive risk factors database for NDDs (NDDRF) was established including 998 single or combined risk factors, 2293 records and 1071 articles relevant to the 14 most common NDDs. The single risk factors are classified into 3 categories, i.e. epidemiological factors (469), genetic factors (324) and biochemical factors (153). Among all the factors, 179 factors are positive and protective, while 880 factors have negative influence for NDDs. The knowledgebase is available at http://sysbio.org.cn/NDDRF/. CONCLUSION: NDDRF provides the structured information and knowledge resource on risk factors of NDDs. It could benefit the future systematic and personalized investigation of pan-NDDs genesis and progression. Meanwhile it may be used for the future explainable artificial intelligence modeling for smart diagnosis and prevention of NDDs.

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals.

Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMD-based entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.

Phenotype-genotype network construction and characterization: a case study of cardiovascular diseases and associated non-coding RNAs.

The phenotype-genotype relationship is a key for personalized and precision medicine for complex diseases. To unravel the complexity of the clinical phenotype-genotype network, we used cardiovascular diseases (CVDs) and associated non-coding RNAs (ncRNAs) (i.e. miRNAs, long ncRNAs, etc.) as the case for the study of CVDs at a systems or network level. We first integrated a database of CVDs and ncRNAs (CVDncR, http://sysbio.org.cn/cvdncr/) to construct CVD-ncRNA networks and annotate their clinical associations. To characterize the networks, we then separated the miRNAs into two groups, i.e. universal miRNAs associated with at least two types of CVDs and specific miRNAs related only to one type of CVD. Our analyses indicated two interesting patterns in these CVD-ncRNA networks. First, scale-free features were present within both CVD-miRNA and CVD-lncRNA networks; second, universal miRNAs were more likely to be CVDs biomarkers. These results were confirmed by computational functional analyses. The findings offer theoretical guidance for decoding CVD-ncRNA associations and will facilitate the screening of CVD ncRNA biomarkers. Database URL: http://sysbio.org.cn/cvdncr/.

Integrative microRNA and mRNA expression profiling in acute aristolochic acid nephropathy in mice.

In acute aristolochic acid nephropathy (AAN), aristolochic acid (AA) induces renal injury and tubulointerstitial fibrosis. However, the roles of microRNAs (miRNAs/miRs) and mRNAs involved in AAN are not clearly understood. The aim of the present study was to examine AA­induced genome­wide differentially expressed (DE) miRNAs and DE mRNAs using deep sequencing in mouse kidneys, and to analyze their regulatory networks. In the present self­controlled study, mice were treated with 5 mg/kg/day AA for 5 days, following unilateral nephrectomy. AA­induced renal injury and tubulointerstitial fibrosis were detected using hematoxylin and eosin staining and Masson's trichrome staining in the mouse kidneys. A total of 82 DE miRNAs and 4,605 DE mRNAs were identified between the AA­treated group and the self­control group. Of these DE miRNAs and mRNAs, some were validated using reverse transcription­quantitative PCR. Expression levels of the profibrotic miR­21, miR­433 and miR­132 families were significantly increased, whereas expression levels of the anti­fibrotic miR­122­5p and let­7a­1­3p were significantly decreased. Functions and signaling pathways associated with the DE miRNAs and mRNAs were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 767 DE pairs (in opposing directions) of miRNAs and their mRNA targets were identified. Among these, regulatory networks of miRNAs and mRNAs were analyzed using KEGG to identify enriched signaling pathways and extracellular matrix­associated pathways. In conclusion, the present study identified genome­wide DE miRNAs and mRNAs in the kidneys of AA­treated mice, as well as their regulatory pairs and signaling networks. The present results may improve the understanding of the role of DE miRNAs and their mRNA targets in the pathophysiology of acute AAN.

Renyi Distribution Entropy Analysis of Short-Term Heart Rate Variability Signals and Its Application in Coronary Artery Disease Detection.

Coronary artery disease (CAD) is a life-threatening condition that, unless treated at an early stage, can lead to congestive heart failure, ischemic heart disease, and myocardial infarction. Early detection of diagnostic features underlying electrocardiography signals is crucial for the identification and treatment of CAD. In the present work, we proposed novel entropy called Renyi Distribution Entropy (RdisEn) for the analysis of short-term heart rate variability (HRV) signals and the detection of CAD. Our simulation experiment with synthetic, physiological, and pathological signals demonstrated that RdisEn could distinguish effectively among different subject groups. Compared to the values of sample entropy or approximation entropy, the RdisEn value was less affected by the parameter choice, and it remained stable even in short-term HRV. We have developed a combined CAD detection scheme with RdisEn and wavelet packet decomposition (WPD): (1) Normal and CAD HRV beats obtained were divided into two equal parts. (2) Feature acquisition: RdisEn and WPD-based statistical features were calculated from one part of HRV beats, and student's t-test was performed to select clinically significant features. (3) Classification: selected features were computed from the remaining part of HRV beats and fed into K-nearest neighbor and support vector machine, to separate CAD from normal subjects. The proposed scheme automatically detected CAD with 97.5% accuracy, 100% sensitivity and 95% specificity and performed better than most of the existing schemes.

MIRKB: a myocardial infarction risk knowledge base.

Myocardial infarction (MI) is a common cardiovascular disease and a leading cause of death worldwide. The etiology of MI is complicated and not completely understood. Many risk factors are reported important for the development of MI, including lifestyle factors, environmental factors, psychosocial factors, genetic factors, etc. Identifying individuals with an increased risk of MI is urgent and a major challenge for improving prevention. The MI risk knowledge base (MIRKB) is developed for facilitating MI research and prevention. The goal of MIRKB is to collect risk factors and models related to MI to increase the efficiency of systems biological level understanding of the disease. MIRKB contains 8436 entries collected from 4366 articles in PubMed before 5 July 2019 with 7902 entries for 1847 single factors, 195 entries for 157 combined factors and 339 entries for 174 risk models. The single factors are classified into the following five categories based on their characteristics: molecular factor (2356 entries, 649 factors), imaging (821 entries, 252 factors), physiological factor (1566 entries, 219 factors), clinical factor (2523 entries, 561 factors), environmental factor (46 entries, 26 factors), lifestyle factor (306 entries, 65 factors) and psychosocial factor (284 entries, 75 factors). MIRKB will be helpful to the future systems level unraveling of the complex mechanism of MI genesis and progression.