Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach

BACKGROUND

Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5.

METHODS

Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed.

RESULTS

A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model.

CONCLUSION

This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

Purpose

Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.

Patients and Methods

We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.

Results

The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.

Conclusion

Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.

Downregulation of SIRT1 and GADD45G genes and left atrial fibrosis induced by right ventricular dependent pacing in a complete atrioventricular block pig model

The molecular and genetic mechanisms underlying left atrial (LA) enlargement and atrial fibrosis following right ventricular (RV) dependent pacing remain unclear. Our objective was to investigate genetic expressions in the LA of pigs subjected to RV pacing for atrioventricular block (AVB), as well as to identify the differential gene expressions affected by biventricular (BiV) pacing. We established an AVB pig model and divided the subjects into three groups: a sham control group, an RV pacing group, and a BiV pacing group. Differential expression genes (DEGs) analyses conducted through next-generation sequencing (NGS) and enrichment analyses were employed to identify genes with altered expression in the LA myocardium. The RV pacing group showed a significant increase in extracellular fibrosis in the LA myocardium compared to the control group. NGS analysis revealed suppressed expression of the sirtuin signaling pathway in the RV pacing group. Among the DEGs within this pathway, GADD45G was found to be downregulated in the RV pacing group and upregulated in the BiV pacing group. Remarkably, the BiV pacing group exhibited elevated levels of GADD45G protein. In our study, we observed significant downregulation of SIRT1 and GADD45G genes, which are associated with the sirtuin signaling pathway, in the LA myocardium of the RV pacing group when compared to the control group. Moreover, these genes, which were downregulated in the RV pacing group, displayed a noteworthy upregulation in the BiV pacing group when compared to the RV pacing group.

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning

BACKGROUND

Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations.

METHODS

In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer.

RESULTS

After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878.

CONCLUSIONS

We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning

Metabolic syndrome (MetS) includes several conditions that can increase an individual's predisposition to high-risk cardiovascular events, morbidity, and mortality. Non-alcoholic fatty liver disease (NAFLD) is a predominant cause of cirrhosis, which is a global indicator of liver transplantation and is considered the hepatic manifestation of MetS. FibroScan^® provides an accurate and non-invasive method for assessing liver steatosis and fibrosis in patients with NAFLD, via a controlled attenuation parameter (CAP) and liver stiffness measurement (LSM or E) scores and has been widely used in current clinical practice. Several machine learning (ML) models with a recursive feature elimination (RFE) algorithm were applied to evaluate the importance of the CAP score. Analysis by ANOVA revealed that five symptoms at different CAP and E score levels were significant. All eight ML models had accuracy scores > 0.9, while treebags and random forest had the best kappa values (0.6439 and 0.6533, respectively). The CAP score was the most important variable in the seven ML models. Machine learning models with RFE demonstrated that using the CAP score to identify patients with MetS may be feasible. Thus, a combination of CAP scores and other significant biomarkers could be used for early detection in predicting MetS.

The downregulation of miR-509-3p expression by collagen type XI alpha 1-regulated hypermethylation facilitates cancer progression and chemoresistance via the DNA methyltransferase 1/Small ubiquitin-like modifier-3 axis in ovarian cancer cells

BACKGROUND

MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC.

METHODS

The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study.

RESULTS

Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity.

CONCLUSION

The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients

Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data. Decision tree, support vector machine, logistic regression, and random forest algorithms were chosen for the analysis. A total of 2,138 participants (1,028 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) with AF and 8,552 random controls (after the matching process) without AF (4,112 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) were included in the model. The 1-year new-onset AF risk prediction model based on the random forest algorithm using medication and diagnostic information, along with specific laboratory data, attained an area under the receiver operating characteristic of 0.74, whereas the specificity was 98.7%. Machine learning-based model focusing on the older aged patients could offer acceptable discrimination in differentiating the risk of incident AF in the next year. In conclusion, a targeted screening approach using multidimensional informatics in the electronic medical records could result in a clinical choice with efficacy for prediction of the incident AF risk in older aged patients.

The Downregulation of miR-509-3p Expression by Collagen Type XI Alpha 1-Regulated Hypermethylation Facilitates Cancer Progression and Chemoresistance via the DNA Methyltransferase 1/Small Ubiquitin-like Modifier-3 Axis in Ovarian Cancer Cells

Background MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC. Methods The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study. Results Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) phosphorylation and stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity. Conclusion The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Microbiota dysbiosis in odontogenic rhinosinusitis and its association with anaerobic bacteria

Odontogenic rhinosinusitis is a subtype of rhinosinusitis associated with dental infection or dental procedures and has special bacteriologic features. Previous research on the bacteriologic features of odontogenic rhinosinusitis has mainly used culture-dependent methods. The variation of microbiota between odontogenic and nonodontogenic rhinosinusitis as well as the interplay between the involved bacteria have not been explored. Therefore, we enrolled eight odontogenic rhinosinusitis cases and twenty nonodontogenic rhinosinusitis cases to analyze bacterial microbiota through 16S rRNA sequencing. Significant differences were revealed by the Shannon diversity index (Wilcoxon test p = 0.0003) and PERMANOVA test based on weighted UniFrac distance (Wilcoxon test p = 0.001) between odontogenic and nonodontogenic samples. Anaerobic bacteria such as Porphyromonas, Fusobacterium, and Prevotella were significantly dominant in the odontogenic rhinosinusitis group. Remarkably, a correlation between different bacteria was also revealed by Pearson's correlation. Staphylococcus was highly positively associated with Corynebacterium, whereas Fusobacterium was highly negatively correlated with Prophyromonas. According to our results, the microbiota in odontogenic rhinosinusitis, predominantly anaerobic bacteria, was significantly different from that in nonodontogenic rhinosinusitis, and the interplay between specific bacteria may a major cause of this subtype of rhinosinusitis.

Relationship between metformin use and lactic acidosis in advanced chronic kidney disease: The REMIND-TMU study

BACKGROUND

Evidence of metformin-associated lactic acidosis (MALA) in advanced chronic kidney disease (CKD) has been limited due to high mortality rate but rare incidence rate. The mechanism of increased MALA in advanced CKD is mainly based on the hypothesis of decreased drug elimination, which might also be confounded by increased comorbidities as CKD progresses. The goal of the study is to analyze the incidence and associated factors of lactic acidosis between metformin user and non-user with advanced CKD.

METHODS

This study used a three million population-based, propensity score-matched cohort from 2008 to 2016. The primary outcome was laboratory-defined lactic acidosis. Relationships between the probability of lactic acidosis and various estimated glomerular filtration rate (eGFR) values in advanced CKD patients were also presented in regression analysis.

RESULTS

Adults with type 2 diabetes whose eGFR was <30 mL/min/1.73 m² were enrolled in this study. After the process of propensity score matching, 7707 patients were divided into metformin and non-metformin groups. In linear regression model, metformin significantly increased the risk of lactic acidosis (p=0.0204) as the eGFR declined in advanced CKD over a mean follow up of over 600 days even after confounder adjustment with age, sex and comorbidities.

CONCLUSIONS

Metformin was associated with a significant increased risk of laboratory-defined lactic acidosis (p=0.0204) even after adjusting confounder such as age, sex and underlying comorbidities. This "REMIND" study reminds us that metformin-associated lactic acidosis is mainly caused by decreased drug renal elimination other than underlying comorbidities in advanced CKD patients.

Hypermethylation of TMEM240 predicts poor hormone therapy response and disease progression in breast cancer

Background

Approximately 25% of patients with early-stage breast cancer experience cancer progression throughout the disease course. Alterations in TMEM240 in breast cancer were identified and investigated to monitor treatment response and disease progression.

Methods

Circulating methylated TMEM240 in the plasma of breast cancer patients was used to monitor treatment response and disease progression. The Cancer Genome Atlas (TCGA) data in Western countries and Illumina methylation arrays in Taiwanese breast cancer patients were used to identify novel hypermethylated CpG sites and genes related to poor hormone therapy response. Quantitative methylation-specific PCR (QMSP), real-time reverse transcription PCR, and immunohistochemical analyses were performed to measure DNA methylation and mRNA and protein expression levels in 394 samples from Taiwanese and Korean breast cancer patients. TMEM240 gene manipulation, viability, migration assays, RNA-seq, and MetaCore were performed to determine its biological functions and relationship to hormone drug treatment response in breast cancer cells.

Results

Aberrant methylated TMEM240 was identified in breast cancer patients with poor hormone therapy response using genome-wide methylation analysis in the Taiwan and TCGA breast cancer cohorts. A cell model showed that TMEM240, which is localized to the cell membrane and cytoplasm, represses breast cancer cell proliferation and migration and regulates the expression levels of enzymes involved in estrone and estradiol metabolism. TMEM240 protein expression was observed in normal breast tissues but was not detected in 88.2% (67/76) of breast tumors and in 90.0% (9/10) of metastatic tumors from breast cancer patients. QMSP revealed that in 54.5% (55/101) of Taiwanese breast cancer patients, the methylation level of TMEM240 was at least twofold higher in tumor tissues than in matched normal breast tissues. Patients with hypermethylation of TMEM240 had poor 10-year overall survival (p = 0.003) and poor treatment response, especially hormone therapy response (p < 0.001). Circulating methylated TMEM240 dramatically and gradually decreased and then diminished in patients without disease progression, whereas it returned and its levels in plasma rose again in patients with disease progression. Prediction of disease progression based on circulating methylated TMEM240 was found to have 87.5% sensitivity, 93.1% specificity, and 90.2% accuracy.

Conclusions

Hypermethylation of TMEM240 is a potential biomarker for treatment response and disease progression monitoring in breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00474-9.

Contact Laxative Use and the Risk of Arteriovenous Fistula Maturation Failure in Patients Undergoing Hemodialysis: A Multi-Center Cohort Study

Laxatives are commonly prescribed for constipation management; however, they are recognized as an independent factor associated with cardiovascular diseases. Arteriovenous fistula (AVF) is the closest to the ideal model of hemodialysis (HD) vascular access and part of the cardiovascular system. Our study aims to explore the association of contact laxative use with AVF maturation outcomes in patients undergoing HD. We conducted a multi-center cohort study of 480 contact laxative users and 472 non-users who had undergone initial AVF creation. All patients were followed until the outcomes of AVF maturation were confirmed. Multivariable logistic regression models were performed to evaluate the risk of AVF maturation failure imposed by laxatives. Here, we found that patients who used contact laxatives were significantly associated with an increased risk of AVF maturation failure compared to non-users (adjusted odds ratio, 1.64; p = 0.003). Notably, the risk of AVF maturation failure increased when increasing their average daily doses and cumulative treatment days. In conclusion, our study found a significant dose- and duration-dependent relationship between contact laxative use and an increased risk of AVF maturation failure. Thus, laxatives should be prescribed with caution in this population. Further studies are needed to validate these observations and investigate the potential mechanisms.

Galectin-1 orchestrates an inflammatory tumor-stroma crosstalk in hepatoma by enhancing TNFR1 protein stability and signaling in carcinoma-associated fibroblasts

Most cases of hepatocellular carcinoma (HCC) arise with the fibrotic microenvironment where hepatic stellate cells (HSCs) and carcinoma-associated fibroblasts (CAFs) are critical components in HCC progression. Therefore, CAF normalization could be a feasible therapy for HCC. Galectin-1 (Gal-1), a β-galactoside-binding lectin, is critical for HSC activation and liver fibrosis. However, few studies has evaluated the pathological role of Gal-1 in HCC stroma and its role in hepatic CAF is unclear. Here we showed that Gal-1 mainly expressed in HCC stroma, but not cancer cells. High expression of Gal-1 is correlated with CAF markers and poor prognoses of HCC patients. In co-culture systems, targeting Gal-1 in CAFs or HSCs, using small hairpin (sh)RNAs or an therapeutic inhibitor (LLS30), downregulated plasminogen activator inhibitor-2 (PAI-2) production which suppressed cancer stem-like cell properties and invasion ability of HCC in a paracrine manner. The Gal-1-targeting effect was mediated by increased a disintegrin and metalloprotease 17 (ADAM17)-dependent TNF-receptor 1 (TNFR1) shedding/cleavage which inhibited the TNF-α → JNK → c-Jun/ATF2 signaling axis of pro-inflammatory gene transcription. Silencing Gal-1 in CAFs inhibited CAF-augmented HCC progression and reprogrammed the CAF-mediated inflammatory responses in a co-injection xenograft model. Taken together, the findings uncover a crucial role of Gal-1 in CAFs that orchestrates an inflammatory CSC niche supporting HCC progression and demonstrate that targeting Gal-1 could be a potential therapy for fibrosis-related HCC.

Comparative survival analysis of platinum-based adjuvant chemotherapy for early-stage squamous cell carcinoma and adenocarcinoma of the lung

Background and Purpose

Although cytotoxic platinum‐based adjuvant chemotherapy (pACT) has been recommended for patients with completely resected early‐stage (ES) non–small‐cell lung cancer (ES‐NSCLC), therapeutic regimens for NSCLC have evolved in the past two decades. The study was aimed to examine the effectiveness of postoperative pACT for resected ES‐NSCLC patients with squamous cell carcinoma (SCC) or adenocarcinoma (ADC) according to real‐world data.

Methods and Patients

Inverse probability treatment weighting (IPTW) was used to adjust baseline characteristics between the group receiving pACT and those not receiving any treatment (observation, OBS) within 3 months after curative surgery. Cox regression models were used to compare overall survival (OS) and treatment failure‐free survival (TFS) between the groups.

Results

Of 31,208 patients with ES‐NSCLC, 4700 undergoing complete tumor resection were eligible, with a mean follow‐up period of 4.5 years. The pACT (n = 2347) and OBS (n = 2353) groups were well‐balanced after IPTW. The median OS differed between the pACT and OBS groups (77.2 vs. 75.5 months, adjusted hazard ratio [aHR] = 0.87, 95% confidence interval [CI] = 0.79–0.95, p = 0.003), and the 5‐year survival rates were 58.2% and 55.3%, respectively (p < 0.001). In the SCC group, pACT was superior to OBS in OS (75.0 vs. 57.4 months, aHR = 0.74, 95% CI = 0.62–0.88, p = 0.001) and TFS (32.7 vs. 21.8 months, aHR = 0.74, 95% CI = 0.63–0.86, p < 0.001). Both OS and TFS did not differ between two groups in those with ADC.

Conclusion

Real‐world data indicated that pACT confers a survival benefit for resected ES‐NSCLC patients with SCC but not ADC, which needs to be verified by a large sample of randomized controlled studies.

Effects of osteoporosis medications on bone fracture in patients with chronic kidney disease

PURPOSE OF THE STUDY

The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear.

STUDY DESIGN

Patients with stage 3-5 CKD during 2011-2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed.

RESULTS AND CONCLUSIONS

67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p=0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p=0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p=0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p=0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

Effects of osteoporosis medications on bone fracture in patients with chronic kidney disease

PURPOSE OF THE STUDY

The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear.

STUDY DESIGN

Patients with stage 3-5 CKD during 2011-2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed.

RESULTS AND CONCLUSIONS

67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p = 0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p = 0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p = 0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p = 0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein-protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.

Sarcomeres Morphology and Z-Line Arrangement Disarray Induced by Ventricular Premature Contractions through the Rac2/Cofilin Pathway

The most common ventricular premature contractions (VPCs) originate from the right ventricular outflow tract (RVOT), but the molecular mechanisms of altered cytoskeletons of VPC-induced cardiomyopathy remain unexplored. We created a RVOT bigeminy VPC pig model (n = 6 in each group). Echocardiography was performed. The histopathological alternations in the LV myocardium were analyzed, and next generation sequencing (NGS) and functional enrichment analyses were employed to identify the differentially expressed genes (DEGs) responsible for the histopathological alternations. Finally, a cell silencing model was used to confirm the key regulatory gene and pathway. VPC pigs had increased LV diameters in the 6-month follow-up period. A histological study showed more actin cytoskeleton disorganization and actin accumulation over intercalated disc, Z-line arrangement disarray, increased β-catenin expression, and cardiomyocyte enlargement in the LV myocardium of the VPC pigs compared to the control pigs. The NGS study showed actin cytoskeleton signaling, RhoGDI signaling, and signaling by Rho Family GTPases and ILK Signaling presented z-scores with same activation states. The expressions of Rac family small GTPase 2 (Rac2), the p-cofilin/cofilin ratio, and the F-actin/G-actin ratio were downregulated in the VPC group compared to the control group. Moreover, the intensity and number of actin filaments per cardiomyocyte were significantly decreased by Rac2 siRNA in the cell silencing model. Therefore, the Rac2/cofilin pathway was found to play a crucial role in the sarcomere morphology and Z-line arrangement disarray induced by RVOT bigeminy VPCs.

Association between intracytoplasmic sperm injection and neurodevelopmental outcomes among offspring

PURPOSE

To compare the risk of neurodevelopmental disorders in children conceived via intracytoplasmic sperm injection (ICSI) and those conceived naturally.

MATERIALS AND METHODS

A population-based cohort study using data retrieved from the Taipei Medical University Research Database (TMURD) from January, 2004 to August, 2016. The data included maternal pregnancy history, perinatal history and developmental follow up of their babies up to 5 years of age. The study included 23885 children, of whom 23148 were naturally conceived and 737 were conceived via ICSI. Neurodevelopmental disorders defined by 21 ICD-9-CM codes.

RESULTS

Of the 23885 children enrolled for analysis, 2778 children were included for further subgrouping analysis after propensity matching to reduce bias from maternal factors. The single-birth group included 1752 naturally conceived (NC) children and 438 ICSI children. The multiple-birth group included 294 NC and 294 ICSI children. The risk of neurodevelopmental disorders was not increased for ICSI children in both groups (single birth: adjusted hazard ratio aHR = 0.70, 95% CI = 0.39-1.27, p = 0.243; multiple-birth group aHR = 0.77, 95% CI = 0.43-1.35, p = 0.853). In the single-birth group, multivariate analyses showed that male sex (aHR = 1.81, 95% CI = 1.29-2.54, p < 0.001), and intensive care unit (ICU) admission (aHR = 3.10, 95% CI = 1.64-5.86, p < 0.001) were risk factors for neurodevelopmental disorders. In the multiple-birth group, multivariate analyses demonstrated that ICU admission (aHR = 3.58, 95% CI = 1.82-7.04, p < 0.001), was risk factor for neurodevelopmental disorders.

CONCLUSION

Our study indicated that the use of ICSI does not associated with higher risk of neurodevelopmental disorders in the offspring. But male sex, and ICU admission do have increased risk of neurodevelopmental disorders. However, long term follow up of this cohort on health outcomes in adolescence and adulthood will strengthen the conclusions that ICSI is safe regarding offspring long term outcome.

Investigation of dual antiplatelet therapy after coronary stenting in patients with chronic kidney disease

Background

Dual antiplatelet therapy (DAPT) is currently the standard treatment for the prevention of ischemic events after stent implantation. However, the optimal DAPT duration remains elusive for patients with chronic kidney disease (CKD). Therefore, we aimed to compare the effectiveness and safety between long-term and short-term DAPT after coronary stenting in patients with CKD.

Methods

This retrospective cohort study analyze data from the Taipei Medical University (TMU) Institutional and Clinical Database, which include anonymized electronic health data of 3 million patients that visited TMU Hospital, Wan Fang Hospital, and Shuang Ho Hospital. We enrolled patients with CKD after coronary stenting between 2008 and 2019. The patients were divided into the long-term (>6 months) and short-term DAPT group (≤ 6 months). The primary end point was major adverse cardiovascular events (MACE) from 6 months after the index date. The secondary outcomes were all-cause mortality and Thrombolysis in Myocardial Infarction (TIMI) bleeding.

Results

A total of 1899 patients were enrolled; of them, 1112 and 787 were assigned to the long-term and short-term DAPT groups, respectively. Long-term DAPT was associated with similar risk of MACE (HR: 1.05, 95% CI: 0.65–1.70, P = 0.83) compare with short-term DAPT. Different CKD risk did not modify the risk of MACE. There was also no significant difference in all-cause mortality (HR: 1.10, 95% CI: 0.75–1.61, P = 0.63) and TIMI bleeding (HR 1.19, 95% CI: 0.86–1.63, P = 0.30) between groups.

Conclusions

Among patients with CKD and coronary stenting, we found that long-term and short-term DAPT tied on the risk of MACE, all-cause mortality and TIMI bleeding.

Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

[This corrects the article DOI: 10.2196/27806.].

Tissue microbiota in nasopharyngeal adenoid and its association with pneumococcal carriage

The microbial colonization in the nasopharynx is a prerequisite for the onset of infectious diseases. For successful infection, pathogens should overcome host defenses as well as compete effectively with the resident microbiota. Hence, elucidating the richness and diversity of the microbiome at the site of pathogen colonization is pivotal. Here, we investigated the adenoidal tissue microbiota collected through adenoidectomy to evaluate the impact of Streptococcus pneumoniae. Prospectively, children with sleep-disordered breathing (SDB) and otitis media with effusion (OME) were enrolled. During adenoidectomy, the nasopharyngeal swab and adenoid tissues were collected to determine the pneumococcal carriage and tissue microbiota, using multiplex PCR and 16S ribosomal RNA (16S rRNA) pyrosequencing. A total of 66 pediatric patients comprising 38 children with SDB and 28 children with OME were enrolled. There was no difference between the bacterial cultures from the surface of the nasopharyngeal adenoid in the SDB and OME groups. Thirty-four samples (17 SDB and 17 OME) underwent 16S rRNA pyrosequencing and fulfilled the criteria for further analysis. The Shannon diversity index for the samples from the SDB patients was found to be higher than that observed for the samples from OME patients, although the difference was not significant (p = 0.095). The Shannon diversity index for the samples negative for the pneumococcal carriage was significantly higher than that for the samples positive for pneumococcal carriage (p = 0.038). Alloprevotella, Staphylococcus, Moraxella, and Neisseriaceae were significantly dominant in the samples positive for the pneumococcal carriage. Dialister was significantly less present in the adenoid tissue positive for the pneumococcal carriage. Streptococcus pneumoniae, one of the most common pathogens of the airway, significantly influences the composition and diversity of the microbiota in the nasopharyngeal adenoid. Thus, bacterial community analysis based on 16S rRNA pyrosequencing allows for better understanding of the relationship between the adenoidal microbial communities.

Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion

Simple Summary

Around 30% of men treated with adjuvant therapy experience recurrences of prostate cancer (PC). Current monitoring of the relapse of PC requires regular postoperative prostate-specific antigen (PSA) value follow-up. Our study aims to identify potential multiomics biomarkers using modern computational analytic methods, deep learning (DL), similarity network fusion (SNF), and the Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) dataset. Six significantly intersected omics biomarkers from the two models, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23) were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panels and clinical information achieve p-value = 2.97 × 10⁻¹⁵ and C-index = 0.713, and the prediction performance of five-year recurrence achieves AUC = 0.789. The results show that the multiomics panel provided valuable biomarkers for the early detection of high-risk recurrent patients, and integrating multiomics data gave us the power to detect the complex mechanisms of cancer among the interactions of different genetic and epigenetic factors.

Abstract

This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10⁻⁹, which is better than the former study (p-value = 5 × 10⁻⁷). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10⁻¹⁵, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

Background

More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures.

Objective

We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries.

Methods

The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set.

Results

A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea.

Conclusions

The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Maternal Vegetable and Fruit Consumption during Pregnancy and Its Effects on Infant Gut Microbiome

Maternal nutrition intake during pregnancy may affect the mother-to-child transmission of bacteria, resulting in gut microflora changes in the offspring, with long-term health consequences in later life. Longitudinal human studies are lacking, as only a small amount of studies showing the effect of nutrition intake during pregnancy on the gut microbiome of infants have been performed, and these studies have been mainly conducted on animals. This pilot study explores the effects of high or low fruit and vegetable gestational intake on the infant microbiome. We enrolled pregnant women with a complete 3-day dietary record and received postpartum follow-up. The 16S rRNA gene sequence was used to characterize the infant gut microbiome at 2 months (n = 39). Principal coordinate analysis ordination revealed that the infant gut microbiome clustered differently for high and low maternal fruit and vegetable consumption (p < 0.001). The linear discriminant analysis effect size and feature selection identified 6 and 17 taxa from both the high and low fruit and vegetable consumption groups. Among the 23 abundant taxa, we observed that six maternal intake nutrients were associated with nine taxa (e.g., Erysipelatoclostridium, Isobaculum, Lachnospiraceae, Betaproteobacteria, Burkholderiaceae, Sutterella, Clostridia, Clostridiales, and Lachnoclostridium). The amount of gestational fruit and vegetable consumption is associated with distinct changes in the infant gut microbiome at 2 months of age. Therefore, strategies involving increased fruit and vegetable consumption during pregnancy should be employed for modifying the gut microbiome early in life.

Platelet autophagic machinery involved in thrombosis through a novel linkage of AMPK-MTOR to sphingolipid metabolism

Basal macroautophagy/autophagy has recently been found in anucleate platelets. Platelet autophagy is involved in platelet activation and thrombus formation. However, the mechanism underlying autophagy in anucleate platelets require further clarification. Our data revealed that LC3-II formation and SQSTM1/p62 degradation were noted in H₂O₂-activated human platelets, which could be blocked by 3-methyladenine and bafilomycin A₁, indicating that platelet activation may cause platelet autophagy. AMPK phosphorylation and MTOR dephosphorylation were also detected, and block of AMPK activity by the AMPK inhibitor dorsomorphin reversed SQSTM1 degradation and LC3-II formation. Moreover, autophagosome formation was observed through transmission electron microscopy and deconvolution microscopy. These findings suggest that platelet autophagy was induced partly through the AMPK-MTOR pathway. In addition, increased LC3-II expression occurred only in H₂O₂-treated Atg5^f/f platelets, but not in H₂O₂-treated atg5^-/- platelets, suggesting that platelet autophagy occurs during platelet activation. atg5^-/- platelets also exhibited a lower aggregation in response to agonists, and platelet-specific atg5^-/- mice exhibited delayed thrombus formation in mesenteric microvessles and decreased mortality rate due to pulmonary thrombosis. Notably, metabolic analysis revealed that sphingolipid metabolism is involved in platelet activation, as evidenced by observed several altered metabolites, which could be reversed by dorsomorphin. Therefore, platelet autophagy and platelet activation are positively correlated, partly through the interconnected network of sphingolipid metabolism. In conclusion, this study for the first time demonstrated that AMPK-MTOR signaling could regulate platelet autophagy. A novel linkage between AMPK-MTOR and sphingolipid metabolism in anucleate platelet autophagy was also identified: platelet autophagy and platelet activation are positively correlated.Abbreviations: 3-MA: 3-methyladenine; A.C.D.: citric acid/sod. citrate/glucose; ADP: adenosine diphosphate; AKT: AKT serine/threonine kinase; AMPK: AMP-activated protein kinase; ANOVA: analysis of variance; ATG: autophagy-related; B4GALT/LacCS: beta-1,4-galactosyltransferase; Baf-A1: bafilomycin A₁; BECN1: beclin 1; BHT: butylate hydrooxytoluene; BSA: bovine serum albumin; DAG: diacylglycerol; ECL: enhanced chemiluminescence; EDTA: ethylenediamine tetraacetic acid; ELISA: enzyme-linked immunosorbent assay; GALC/GCDase: galactosylceramidase; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GBA/GluSDase: glucosylceramidase beta; GPI: glycosylphosphatidylinositol; H₂O₂: hydrogen peroxide; HMDB: human metabolome database; HRP: horseradish peroxidase; IF: immunofluorescence; IgG: immunoglobulin G; KEGG: Kyoto Encyclopedia of Genes and Genomes; LAMP1: lysosomal associated membrane protein 1; LC-MS/MS: liquid chromatography-tandem mass spectrometry; mAb: monoclonal antibody; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MPV: mean platelet volume; MTOR: mechanistic target of rapamycin kinase; ox-LDL: oxidized low-density lipoprotein; pAb: polyclonal antibody; PC: phosphatidylcholine; PCR: polymerase chain reaction; PI3K: phosphoinositide 3-kinase; PLS-DA: partial least-squares discriminant analysis; PRP: platelet-rich plasma; Q-TOF: quadrupole-time of flight; RBC: red blood cell; ROS: reactive oxygen species; RPS6KB/p70S6K: ribosomal protein S6 kinase B; SDS: sodium dodecyl sulfate; S.E.M.: standard error of the mean; SEM: scanning electron microscopy; SGMS: sphingomyelin synthase; SM: sphingomyelin; SMPD/SMase: sphingomyelin phosphodiesterase; SQSTM1/p62: sequestosome 1; TEM: transmission electron microscopy; UGT8/CGT: UDP glycosyltransferase 8; UGCG/GCS: UDP-glucose ceramide glucosyltransferase; ULK1: unc-51 like autophagy activating kinase 1; UPLC: ultra-performance liquid chromatography; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3P: phosphatidylinositol-3-phosphate; WBC: white blood cell; WT: wild type.

Collagen type VI regulates the CDK4/6-p-Rb signaling pathway and promotes ovarian cancer invasiveness, stemness, and metastasis

The expression of collagen VI in primary ovarian tumors may correlate with tumor grade and response to chemotherapy. We have sought to elucidate the role of collagen VI in promoting ovarian cancer tumor growth and metastasis. Here we examined the effects of collagen VI on ovarian carcinoma stromal progenitor cells (OCSPCs). Epithelial-like OCSPCs (epi-OCSPCs) and mesenchymal-like OCSPCs (msc-OCSPCs) were analyzed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Differentially expressed genes were integrated with survival-related genes using The Cancer Genome Atlas (TCGA) data and confirmed in our samples. The roles of candidate genes and signaling pathways were further explored. We found that SKOV3/msc-OCSPCs possessed greater migration, invasion, and spheroid formation than SKOV3/epi-OCSPCs (P < 0.001). Expression of collagen alpha-3 (VI; COL6A3), which encodes collagen VI, was 90-fold higher in msc-OCSPCs than in epi-OCSPCs. Analysis of TCGA data and our samples indicated that high expression of COL6A3 was correlated with advanced-stage carcinoma (P < 0.01) and shorter overall survival (P < 0.01). In vitro, adding collagen VI, msc-OCSPCs, or knockdown collagen VI in msc-OCSPCs to epithelial ovarian carcinoma (EOC) cells augmented or decreased invasion and spheroid formation. Tumor dissemination to the peritoneal cavity and lung in mice following intraperitoneal coinjection with msc-OCSPCs and SKOV3-Luc cells and intravenous injection with COL6A3 and ES2 cells derived spheroids was significantly greater compare to coinjection with SKOV3-Luc cells alone or in combination with msc-OCSPCs/shCOL6A3 cells and msc-OCSPCs and ES2 derived spheroids. Knockdown of COL6A3 abolished the expression of DNMT1, CDK4, CDK6, and p-Rb in msc-OCSPCs and EOC spheroids. In contrast, overexpression of COL6A3 enhanced the expression of CDK4, CDK6, and p-Rb in SKOV3 cells. EOC spheroid formation, invasion, tumor growth, and metastasis were inhibited when COL6A3 downstream signaling pathway was blocked using CDK4/6 inhibitor LEE011. Our results suggested that collagen VI regulates the CDK4/6-p-Rb signaling pathway and promotes EOC invasiveness, stemness, and metastasis.

Early Chronic Kidney Disease Care Programme delays kidney function deterioration in patients with stage I-IIIa chronic kidney disease: an observational cohort study in Taiwan

OBJECTIVES

To investigate the effect of the Early Chronic Kidney Disease (CKD) Care Programme on CKD progression in patients with CKD stage I-IIIa.

DESIGN

Observational cohort study.

SETTING

Taipei Medical University Research Database from three affiliated hospitals.

PARTICIPANTS

Adult non-pregnant patients with CKD stage I-IIIa from Taipei Medical University Research Database between 1 January 2012 and 31 August 2017 were recruited. These patients were divided into Early CKD Care Programme participants (case) and non-participants (control). The models were matched by age, sex, estimated glomerular filtration rate and CKD stage with 1:2 propensity score to reduce bias between two groups.

OUTCOME MEASURES

The risks of CKD stage I-IIIa progression to IIIb between Early CKD Care Programme participants and non-participants.

RESULTS

Compared with the control group, the case group demonstrated more comorbidities and higher proportions of hypertension, diabetes mellitus, gout, dyslipidaemia, heart disease and cerebrovascular disease, but had lower risk of progression to CKD stage IIIb before and (HR 0.72; 95% CI 0.61 to 0.85) and after (adjusted HR (aHR) 0.67; 95% CI 0.55 to 0.81) adjustments. Moreover, Kaplan-Meier analysis revealed the cumulative incidence of CKD stage IIIb was significantly lower in the case group than in the control group. Finally, the programme was an independent protective factor against progression to stage IIIb, especially in patients with CKD stage IIIa before (HR 0.72; 95% CI 0.61 to 0.85) and after (aHR 0.67; 95% CI 0.55 to 0.81) adjustments.

CONCLUSIONS

The Early CKD Care Programme is an independent protective factor against progression of early CKD.

Classification of Changes in the Fecal Microbiota Associated with Colonic Adenomatous Polyps Using a Long-Read Sequencing Platform

The microbiota is the community of microorganisms that colonizes the oral cavity, respiratory tract, and gut of multicellular organisms. The microbiota exerts manifold physiological and pathological impacts on the organism it inhabits. A growing body of attention is being paid to host–microbiota interplay, which is highly relevant to the development of carcinogenesis. Adenomatous polyps are considered a common hallmark of colorectal cancer, the second leading cause of carcinogenesis-mediated death worldwide. In this study, we examined the relevance between targeted operational taxonomic units and colonic polyps using short- and long-read sequencing platforms. The gut microbiota was assessed in 132 clinical subjects, including 53 healthy participants, 36 patients with occult blood in the gut, and 43 cases with adenomatous polyps. An elevation in the relative abundance of Klebsiella pneumonia, Fusobacterium varium, and Fusobacterium mortiferum was identified in patients with adenomatous polyps compared with the other groups using long-read sequencing workflow. In contrast, the relatively high abundances of Blautia luti, Bacteroides plebeius, and Prevotella copri were characterized in the healthy groups. The diversities in gut microbiota communities were similar in all recruited samples. These results indicated that alterations in gut microbiota were characteristic of participants with adenomatous polyps, which might be relevant to the further development of CRC. These findings provide a potential contribution to the early prediction and interception of CRC occurrence.

Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study

Simple Summary

Characterization of immunophenotypes in GBM is important for therapeutic stratification and helps predict treatment response and prognosis. However, identifying immunophenotypes of patients with GBM requires multiple laboratory experiments and is time consuming. We developed a non-invasive method to evaluate enrichment levels of CTL, aDC, Treg, and MDSC immune cells to classify immunophenotypes of GBM tumor microenvironment with radiomic features of MR imaging. Five immunophenotypes (G1–G5) of GBM can be classified with specific gene set enrichment analysis. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. Moreover, the developed radiomics models can successfully identified these two groups by immune cell subsets enriched levels prediction. Therefore, it is possible to characterize immunophenotypes of GBM and predict patient prognosis with radiomics methods.

Abstract

Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the “immune-cold” phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.

Characterization of Fecal Microbiota with Clinical Specimen Using Long-Read and Short-Read Sequencing Platform

Accurate and rapid identification of microbiotic communities using 16S ribosomal (r)RNA sequencing is a critical task for expanding medical and clinical applications. Next-generation sequencing (NGS) is widely considered a practical approach for direct application to communities without the need for in vitro culturing. In this report, a comparative evaluation of short-read (Illumina) and long-read (Oxford Nanopore Technologies (ONT)) platforms toward 16S rRNA sequencing with the same batch of total genomic DNA extracted from fecal samples is presented. Different 16S gene regions were amplified, bar-coded, and sequenced using the Illumina MiSeq and ONT MinION sequencers and corresponding kits. Mapping of the sequenced amplicon using MinION to the entire 16S rRNA gene was analyzed with the cloud-based EPI2ME algorithm. V3–V4 reads generated using MiSeq were aligned by applying the CLC genomics workbench. More than 90% of sequenced reads generated using distinct sequencers were accurately classified at the genus or species level. The misclassification of sequenced reads at the species level between the two approaches was less substantial as expected. Taken together, the comparative results demonstrate that MinION sequencing platform coupled with the corresponding algorithm could function as a practicable strategy in classifying bacterial community to the species level.

Preclinical Evaluation of the Novel Small-Molecule MSI-N1014 for Treating Drug-Resistant Colon Cancer via the LGR5/β-catenin/miR-142-3p Network and Reducing Cancer-Associated Fibroblast Transformation

Colorectal cancer represents one of the most prevalent malignancies globally, with an estimated 140,000 new cases in the United States alone in 2019. Despite advancements in interventions, drug resistance occurs in virtually all patients diagnosed with late stages of colon cancer. Amplified epidermal growth factor receptor (EGFR) signaling is one of the most prevalent oncogenic drivers in patients and induces increased Janus kinase (JAK)/signal transduction and activator of transcription (STAT) and β-catenin functions, all of which facilitate disease progression. Equally important, cancer-associated fibroblasts (CAFs) transformed by cancer cells within the tumor microenvironment (TME) further facilitate malignancy by secreting interleukin (IL)-6 and augmenting STAT3 signaling in colon cancer cells and promoting the generation of cancer stem-like cells (CSCs). Based on these premises, single-targeted therapeutics have proven ineffective for treating malignant colon cancer, and alternative multiple-targeting agents should be explored. Herein, we synthesized a tetracyclic heterocyclic azathioxanthone, MSI-N1014, and demonstrated its therapeutic potential both in vitro and in vivo. First, we used a co-culture system to demonstrate that colon cancer cells co-cultured with CAFs resulted in heightened 5-fluorouracil (5-FU) resistance and tumor sphere-forming ability and increased side populations, accompanied by elevated expression of cluster of differentiation 44 (CD44), β-catenin, leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5), and ATP-binding cassette super-family G member 2 (ABCG2). MSI-N1014 suppressed cell viability, colony formation, and migration in both DLD1 and HCT116 cells. MSI-N1014 treatment led to decreased expressions of oncogenic markers, including mammalian target of rapamycin (mTOR), EGFR, and IL-6 and stemness markers such as CD44, β-catenin, and LGR5. More importantly, MSI-N1014 treatment suppressed the transformation of CAFs, and was associated with decreased secretion of IL-6 and vascular endothelial growth factor (VEGF) by CAFs. Furthermore, MSI-N1014 treatment resulted in significantly reduced oncogenic properties, namely the migratory ability, tumor-sphere generation, and resistance against 5-FU. Notably, an increased level of the tumor suppressor, miR-142-3p, whose targets include LGR5, IL-6, and ABCG2, was detected in association with MSI-N1014 treatment. Finally, we demonstrated the therapeutic potential of MSI-N1014 in vivo, where combined treatment with MSI-N1014 and 5-FU led to the lowest tumor growth, followed by MSI-N1014 only, 5-FU, and the vehicle control. Tumor samples from the MSI-N1014 group showed markedly reduced expressions of LGR5, β-catenin, IL-6, and mTOR, but increased expression of the tumor suppressor, miR-142-3p, according to qRT-PCR analysis. Collectively, we present preclinical support for the application of MSI-N1014 in treating 5-FU-resistant colon cancer cells. Further investigation is warranted to translate these findings into clinical settings.

P0903EFFECTS OF OSTEOPOROSIS MEDICATIONS ON BONE FRACTURE IN PATIENTS WITH CHRONIC KIDNEY DISEASE

Background and Aims

To evaluate the association of osteoporosis medications and outcomes of patients at risk of CKD.

Method

We included patients with stage 3-5 CKD from three Taipei Medical University-affiliated hospitals between January 1, 2011 and August 31, 2016. Patients were divided into two groups based on whether they received osteoporosis medications (bisphosphonates, raloxifene, teriparatide, or denosumab) and then matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events, and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors.

Results

A total of 39128 patients with CKD were included. After propensity score matching, 763 patients were in the study and control groups, respectively. The mean age of patients was 71.9 ± 12.5 years, and 34.4% of patients were men. After a median follow-up of 2.9 years, the incidence rates of bone fracture, CV events, and all-cause mortality were 10.4%, 7.5%, and 7.5%, respectively. Multivariate analysis results indicated that osteoporosis treatment was note associated with reduced risks of bone fracture (HR 0.85; 95% CI 0.43–1.68; P = 0.65), CV events (HR 0.82; 95% CI 0.56–1.20; P = 0.30), and all-cause mortality (HR 0.74; 95% CI 0.51–1.08; P = 0.12). Subgroup analysis demonstrated only patients with CKD stage 3 who received osteoporosis medications associated with better survival.

Conclusion

Osteoporosis medications did not reduce bone fractures, CV events or mortality in patients with CKD. Improved overall survival was observed in patients with CKD stage 3.

Untargeted metabolomics predicts the functional outcome of ischemic stroke

BACKGROUND/PURPOSE

Metabolites in blood have been found associated with the occurrence of vascular diseases, but its role in the functional recovery of stroke is unclear. The aim of this study is to investigate whether the untargeted metabolomics at the acute stage of ischemic stroke is able to predict functional recovery.

METHODS

One hundred and fifty patients with acute ischemic stroke were recruited and followed up for 3 months. Fasting blood samples within 7 days of stroke were obtained, liquid chromatography and mass spectrometry were applied to identify outcome-associated metabolites. The patients' clinical characteristics and identified metabolites were included for constructing the outcome prediction model using machine learning approaches.

RESULTS

By using multivariate analysis, 220 differentially expressed metabolites (DEMs) were discovered between patients with favorable outcomes (modified Rankin Scale, mRS ≤ 2 at 3 months, n = 77) and unfavorable outcomes (mRS ≥ 3 at 3 months, n = 73). After feature selection, 63 DEMs were chosen for constructing the outcome prediction model. The predictive accuracy was below 0.65 when including patients' clinical characteristics, and could reach 0.80 when including patients' clinical characteristics and 63 selected DEMs. The functional enrichment analysis identified platelet activating factor (PAF) as the strongest outcome-associated metabolite, which involved in proinflammatory mediators release, arachidonic acid metabolism, eosinophil degranulation, and production of reactive oxygen species.

CONCLUSION

Metabolomics is a potential method to explore the blood biomarkers of acute ischemic stroke. The patients with unfavorable outcomes had a lower PAF level compared to those with favorable outcomes.

Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.

Computational analysis for identification of the extracellular matrix molecules involved in endometrial cancer progression

Recurrence and poorly differentiated (grade 3 and above) and atypical cell type endometrial cancer (EC) have poor prognosis outcome. The mechanisms and characteristics of recurrence and distal metastasis of EC remain unclear. The extracellular matrix (ECM) of the reproductive tract in women undergoes extensive structural remodelling changes every month. Altered ECMs surrounding cells were believed to play crucial roles in a cancer progression. To decipher the associations between ECM and EC development, we generated a PAN-ECM Data list of 1516 genes including ECM molecules (ECMs), synthetic and degradation enzymes for ECMs, ECM receptors, and soluble molecules that regulate ECM and used RNA-Seq data from The Cancer Genome Atlas (TCGA) for the studies. The alterations of PAN-ECM genes by comparing the RNA-Seq expressions profiles of EC samples which have been grouped as tumorigenesis and metastasis group based on their pathological grading were identified. Differential analyses including functional enrichment, co-expression network, and molecular network analysis were carried out to identify the specific PAN-ECM genes that may involve in the progression of EC. Eight hundred and thirty-one and 241 PAN-ECM genes were significantly involved in tumorigenesis (p-value <1.571e-15) and metastasis (p-value <2.2e-16), respectively, whereas 140 genes were in the intersection of tumorigenesis and metastasis. Interestingly, 92 of the 140 intersecting PAN-ECM genes showed contrasting fold changes between the tumorigenesis and metastasis datasets. Enrichment analysis for the contrast PAN-ECM genes indicated pathways such as GP6 signaling, ILK signaling, and interleukin (IL)-8 signaling pathways were activated in metastasis but inhibited in tumorigenesis. The significantly activated ECM and ECM associated genes in GP6 signaling, ILK signaling, and interleukin (IL)-8 signaling pathways may play crucial roles in metastasis of EC. Our study provides a better understanding of the etiology and the progression of EC.

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer

Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC.

Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established.

Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively.

Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.

Liver X Receptor/Retinoid X Receptor Pathway Plays a Regulatory Role in Pacing-Induced Cardiomyopathy

Background

The molecular mechanisms through which high‐demand pacing induce myocardial dysfunction remain unclear.

Methods and Results

We created atrioventricular block in pigs using dependent right ventricular septal pacing for 6 months. Echocardiography was performed to evaluate dyssynchrony between pacing (n=6) and sham control (n=6) groups. Microarray and enrichment analyses were used to identify differentially expressed genes (DEGs) in the left ventricular (LV) myocardium between pacing and sham control groups. Histopathological and protein changes were also analyzed and an A cell pacing model was also performed. Pacing significantly increased mechanical dyssynchrony. Enrichment analysis using Ingenuity Pathway Analysis and the activation z‐score analysis method demonstrated that there were 5 DEGs (ABCA1, APOD, CLU, LY96, and SERPINF1) in the LV septum (z‐score=−0.447) and 5 DEGs (APOD, CLU, LY96, MSR1, and SERPINF1) in the LV free wall (z‐score=−1.000) inhibited the liver X receptor/retinoid X receptor (LXR/RXR) pathway, and 4 DEGs (ACTA2, MYL1, PPP2R3A, and SNAI2) activated the integrin‐linked kinase (ILK) pathway in the LV septum (z‐score=1.000). The pacing group had a larger cell size, higher degree of myolysis and fibrosis, and increased expression of intracellular lipid, inflammatory cytokines, and apoptotic markers than the sham control group. The causal relationships between pacing and DEGs related to LXR/RXR and ILK pathways, apoptosis, fibrosis, and lipid expression after pacing were confirmed in the cell pacing model. Luciferase reporter assay in the cell pacing model also supported inhibition of the LXR pathway by pacing.

Conclusions

Right ventricular septal‐dependent pacing was associated with persistent LV dyssynchrony–induced cardiomyopathy through inhibition of the LXR/RXR pathway.

Microbiota Dysbiosis in Fungal Rhinosinusitis

Fungal rhinosinusitis is a unique phenotype of chronic rhinosinusitis with unique clinical and histological characteristics. The role of bacterial microbiota in various phenotypes chronic rhinosinusitis is not thoroughly understood. Therefore, we conducted 16s rRNA amplification sequencing to determine differences in bacterial communities between phenotypes (fungal vs. non- fungal) and anatomical sites (middle meatus vs. nasopharynx). Endoscope-guided swabs were used to collect samples from the middle meatus and nasopharynx of seven consecutive patients with fungal and 18 consecutive patients with non-fungal rhinosinusitis. DNA was extracted and investigated through 16S rRNA amplification. Among samples from the middle meatus, Shannon diversity was significantly lower in those from the fungal rhinosinusitis group (p = 0.029). However, no significant differences in diversity were noted between nasopharynx samples (p = 0.85). Fungal rhinosinusitis samples exhibited a distinct distribution of taxon relative abundance, which involved not only the absence of rhinosinusitis-associated commensal Corynebacterium and Fusobacterium in the middle meatus but also a significant increase in Haemophilus prevalence and abundance. This is the first study to compare bacterial communities in fungal and non-fungal rhinosinusitis samples. Our findings demonstrated that bacterial community dysbiosis was more apparent in fungal rhinosinusitis samples and was limited to the middle meatus.

Effect of weight loss on the estimated glomerular filtration rates of obese patients at risk of chronic kidney disease: the RIGOR-TMU study

BACKGROUND

Weight-reduction therapies, including bariatric surgery (BS), are standard treatments for severely obese patients with type 2 diabetes; however, the outcomes of these therapies are inconclusive for obese patients with chronic kidney disease (CKD). This study aimed to investigate the effects of BS or non-surgical interventions on the estimated glomerular filtration rate (eGFR) and to determine whether BS can be recommended for renal function preservation based on body mass index (BMI) and eGFR changes in obese patients with CKD.

METHODS

This study used data from the weight Reduction Intervention on GFR in Obese Patients with Renal Impairment-Taipei Medical University (TMU) study, which was a large, long-term, propensity score-matched cohort study based on clinical data from patients who registered at weight-reduction centres at TMU and its affiliated hospitals from 2008 to 2016. The patients were stratified according to whether they had undergone BS and into the mild, moderate, and high CKD risk groups using the Kidney Disease: Improving Global Outcomes guidelines. The primary outcome was the eGFR calculated using the Taiwan Chronic Kidney Disease-Epidemiology Collaboration equation. Cox regression models were used to determine hazard ratios (HRs) for eGFR decreases ≥25%.

RESULTS

A total of 4332 obese patients were enrolled in this study. After propensity score matching, 1620 patients, including 60.2% women, with a mean age of 36.5 (9.9) years were divided into BS or non-surgery groups (n = 810 per group). The overall mean eGFRs increased by 4.4 (14) mL/min·1.73 m² and decreased by 6.4 (16.0) mL/min·1.73 m² in the BS and non-surgery groups, respectively. The decrease in BMI in the BS and non-surgery groups were 2.5 and 1.3 kg/m² , respectively. In the moderate/high CKD risk BS group, a significant correlation was evident between an increased eGFR and a reduced BMI (Spearman's correlation -0.229, P < 0.001). The Cox regression analysis showed that the BS group had a significantly lower risk of an eGFR decline ≥25% at 12 months [adjusted HR (aHR) 0.47, P = 0.03). After BS, obese patients with hypertension or albuminuria had significantly lower risks of eGFR declines ≥25% (aHR 0.37, P = 0.02 and aHR 0.13, P = 0.0018, respectively).

CONCLUSIONS

Bariatric surgery was associated with eGFR preservation in all obese patients and, particularly, in those with moderate-to-high CKD risks. A longer term outcome study is warranted to determine the benefits of BS for CKD patients.

Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes

In mammals, microRNAs (miRNAs) play key roles in controlling posttranscriptional regulation through binding to the mRNAs of target genes. Recently, it was discovered that viral miRNAs may be involved in human cancers and diseases. It is likely that viral miRNAs help viruses enter the latent phase of their life cycle and become undetected by the host's immune system, while increasing the host's risk for cancer development. Cervical cancer is typically related to the infection of human papillomavirus (HPV) through sexual transmission. To further understand the molecular mechanisms underlying the associations of HPV infection with genital diseases, we developed a systematic method for viral miRNA identification and viral miRNA-mediated regulatory network construction based on genome-wide sequence analysis. The complete genomes of certain high-risk HPV subtypes were used to predict putative viral pre-miRNAs by bioinformatics approaches. In addition, small RNA libraries in human cervical lesions from existing publications were collected to validate the predicted HPV pre-miRNAs. For the construction of virally encoded miRNA-mediated regulatory network of HPV infection, cervical squamous epithelial carcinoma gene expression data were extracted from the RNA sequencing platform in The Cancer Genome Atlas; the differentially expressed genes were used to identify the putative targets of viral miRNAs. Predicted cellular target genes of HPV-encoded miRNAs provide an overview of these viral miRNA's putative functions. Finally, a large-scale genome analysis was carried out to examine the phylogenetic relationship and structural evolution among genital HPV types that have the potential to cause genital cancer. In this study, we discovered putative HPV-encoded miRNAs, which were validated against the small RNA libraries in human cervical lesions. Furthermore, as indicated by their biological functions, host genes targeted by HPV-encoded miRNAs may play significant roles in virus infection and carcinogenesis. These viral miRNAs pose as promising candidates for the development of antiviral drugs. More importantly, the identified subtype-specific miRNAs have the potential to be used as biomarkers for HPV subtype determination.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

BACKGROUND

Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

METHODS

In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

RESULTS

We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

CONCLUSION

In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

Adverse Outcomes after Major Surgeries in Patients with Diabetes: A Multicenter Matched Study

The impact of diabetes on perioperative outcomes remains incompletely understood. Our purpose is to evaluate post-operative complications and mortality in patients with diabetes. Using the institutional and clinical databases of three university hospitals from 2009–2015, we conducted a matched study of 16,539 diabetes patients, aged >20 years, who underwent major surgery. Using a propensity score matching procedure, 16,539 surgical patients without diabetes who underwent surgery were also selected. Logistic regressions were used to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for post-operative complications and in-hospital mortality associated with diabetes. Patients with diabetes had a higher risk of postoperative septicemia (OR 1.33, 95% CI 1.01–1.74), necrotizing fasciitis (OR 3.98, 95% CI 1.12–14.2), cellulitis (OR 2.10, 95% CI 1.46–3.03), acute pyelonephritis (OR 1.86, 95% CI 1.01–3.41), infectious arthritis (OR 3.89, 95% CI 1.19–12.7), and in-hospital mortality (OR 1.51, 95% CI 1.07–2.13) compared to people without diabetes. Previous admission for diabetes (OR 2.33, 95% CI 1.85–2.93), HbA1c >8% (OR 1.96, 95% CI 1.64–2.33) and fasting glucose >180 mg/dL (OR 1.90, 95% CI 1.68–2.16) were predictors for post-operative adverse events. Diabetes patients who underwent surgery had higher risks of infectious complications and in-hospital mortality compared with patients without diabetes who underwent similar major surgeries.

Vaginal microbiome variances in sample groups categorized by clinical criteria of bacterial vaginosis

Background

One of the most common and recurrent vaginal infections is bacterial vaginosis (BV). The diagnosis is based on changes to the “normal” vaginal microbiome; however, the normal microbiome appears to differ according to reproductive status and ethnicity, and even among individuals within these groups. The Amsel criteria and Nugent score test are widely used for diagnosing BV; however, these tests are based on different criteria, and so may indicate distinct changes in the vaginal microbial community. Nevertheless, few studies have compared the results of these test against metagenomics analysis.

Methods

Vaginal flora samples from 77 participants were classified according to the Amsel criteria and Nugent score test. The microbiota composition was analyzed using 16S ribosome RNA gene amplicon sequencing. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and function.

Results

Only 3 % of the participants diagnosed BV negative using the Amsel criteria (A−) were BV-positive according to the Nugent score test (N+), while over half of the BV-positive patients using the Amsel criteria (A+) were BV-negative according to the Nugent score test (N−). Thirteen genera showed significant differences in distribution among BV status defined by BV tests (e.g., A − N−, A + N− and A + N+). Variations in the four most abundant taxa, Lactobacillus, Gardnerella, Prevotella, and Escherichia, were responsible for most of this dissimilarity. Furthermore, vaginal microbial diversity differed significantly among the three groups classified by the Nugent score test (N−, N+, and intermediate flora), but not between the Amsel criteria groups. Numerous predictive microbial functions, such as bacterial chemotaxis and bacterial invasion of epithelial cells, differed significantly among multiple BV test, but not between the A− and A+ groups.

Conclusions

Metagenomics analysis can greatly expand our current understanding of vaginal microbial diversity in health and disease. Metagenomics profiling may also provide more reliable diagnostic criteria for BV testing.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5284-7) contains supplementary material, which is available to authorized users.

Idi1 and Hmgcs2 Are Affected by Stretch in HL-1 Atrial Myocytes

Background: Lipid expression is increased in the atrial myocytes of mitral regurgitation (MR) patients. This study aimed to investigate key regulatory genes and mechanisms of atrial lipotoxic myopathy in MR. Methods: The HL-1 atrial myocytes were subjected to uniaxial cyclic stretching for eight hours. Fatty acid metabolism, lipoprotein signaling, and cholesterol metabolism were analyzed by PCR assay (168 genes). Results: The stretched myocytes had significantly larger cell size and higher lipid expression than non-stretched myocytes (all p < 0.001). Fatty acid metabolism, lipoprotein signaling, and cholesterol metabolism in the myocytes were analyzed by PCR assay (168 genes). In comparison with their counterparts in non-stretched myocytes, seven genes in stretched monocytes (Idi1, Olr1, Nr1h4, Fabp2, Prkag3, Slc27a5, Fabp6) revealed differential upregulation with an altered fold change >1.5. Nine genes in stretched monocytes (Apoa4, Hmgcs2, Apol8, Srebf1, Acsm4, Fabp1, Acox2, Acsl6, Gk) revealed differential downregulation with an altered fold change <0.67. Canonical pathway analysis, using Ingenuity Pathway Analysis software, revealed that the only genes in the “superpathway of cholesterol biosynthesis” were Idi1 (upregulated) and Hmgcs2 (downregulated). The fraction of stretched myocytes expressing Nile red was significantly decreased by RNA interference of Idi1 (p < 0.05) and was significantly decreased by plasmid transfection of Hmgcs2 (p = 0.004). Conclusions: The Idi1 and Hmgcs2 genes have regulatory roles in atrial lipotoxic myopathy associated with atrial enlargement.