SAGCN: Using graph convolutional network with subgraph-aware for circRNA-drug sensitivity identification.

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first construct a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

The role of sacubitril/valsartan in abnormal renal function patients combined with heart failure: a meta-analysis and systematic analysis.

AIMS: This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies. METHODS: The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs. RESULTS: A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3-5 patients with heart failure (OR: 0.65, 95%CI: 0.54-0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68-0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73-0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60-0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79-2.17) and hypotension (OR:1.57, 95%CI:0.94-2.62) were increased in sacubitril/valsartan group among CKD stages 3-5 patients with heart failure. CONCLUSIONS: Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients.

Association of Vascular Calcification with Serum lncRNA H19 and Runx2 mRNA Expression in Patients with Uremia.

Background: The objective of this study was to investigate the relationship between vascular calcification, serum lncRNA H19, and Runt-Related Transcription Factor 2 mRNA expression in patients with uremia. Methods: This study is a retrospective study which recruited 146 patients with uremia on dialysis from December 2021 to November 2022. Participants were divided into the VC and non-VC groups based on their chest X-ray calcification ratings. General and clinical data were collected from all patients. Serum H19, Runx2 mRNA, mineral bone disease effectors, and other blood markers were tested. Univariate analysis was performed to compare the changes in each clinical index between these two groups of patients. A multi-factor logistic regression analysis of risk factors for VC was performed. Receiver operating characteristics analyzed the H19 and Runx2 for their diagnostic values for VC. Pearson's test was used to analyze the correlation between the H19 and Runx2 expression and the factors influencing VC. Results: Patients in the VC group had significantly higher creatinine, serum phosphorus, calcium, BMP-2, FGF-23, OPG, and iPTH levels than those in the non-VC group (P < .05), while their albumin levels were significantly lower than those in the non-VC group (P < .05). The expression of H19 and Runx2 mRNA was significantly upregulated in the serum of VC patients (P < .05). H19 was significantly positively correlated with creatinine, serum phosphorus, calcium, BMP-2, OPG, and iPTH (P < .05). Runx2 mRNA was significantly positively correlated with creatinine, FGF-23, and iPTH (P < .05 ), while there was no significant correlation with other factors(P > .05). Albumin, BMP-2, iPTH, H19, and Runx2 were independent correlative-factors of uremic VC. In addition, the combined H19 and Runx2 test (AUC=0.850; 95% CI: 0.781-0.903) had good diagnostic values for the development of VC. Conclusion: Serum H19 and Runx2 levels are significantly associated with VC-related factors and are independent risk factors for uremic VC, and their levels contribute to the diagnosis of uremic VC.

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Relationship between effective blood flow rate and clinical outcomes in maintenance hemodialysis patients: a single-center study.

The association between blood flow rate (BFR) and clinical outcomes in patients undergoing maintenance hemodialysis (MHD) is inconclusive. This retrospective study included 175 patients undergoing MHD treatment between July 2015 and March 2022, divided into two groups based on time-averaged effective blood flow rate (eBFR) median value. We investigated arteriovenous fistula (AVF) outcomes and the association of eBFR with all-cause mortality and new major adverse cardiovascular events (MACE). Mean ± SD and median time-averaged eBFR values were 276 ± 24 and 275 mL/min, respectively. After adjusting for relevant factors including age, sex, vintage, diabetes, CVD, receiving hemodiafiltration (HDF) treatment and spKt/V, Cox models indicated a low time-averaged eBFR (≤ 275 ml/min) was associated with increased risks of all-cause mortality (hazard ratio [HR] 14.18; 95% confidence interval [CI], 3.14-64.1) and new MACE (HR 3.76; 95% CI, 1.91-7.40) in MHD patients. Continuous Cox models demonstrated each 20 ml/min increase in eBFR linked to a 63% decrease in the risk of all-cause mortality (HR: 0.37, 95% CI: 0.23-0.59) and a 38% decrease in the occurrence of new MACE (HR: 0.62, 95% CI: 0.46-0.84). There was no significant difference in AVF outcomes between the two groups. Our study noted higher eBFR (>275 mL/min) is associated with lower risks of both all-cause mortality and new MACE compared with low eBFR (≤275 mL/min). Increased eBFR is not associated with a higher risk of AVF failure.

Hypomethylation of the LncRNA H19 promoter accelerates osteogenic differentiation of vascular smooth muscle cells by activating the Erk1/2 pathways.

OBJECTIVE: Vascular calcification is a common chronic kidney disease complication. This study aimed to investigate the function of long non-coding RNA (LncRNA) H19 in vascular calcification to explore new therapeutic strategies. METHODS: We induced osteogenic differentiation and calcification of vascular smooth muscle cells (VSMCs) using ß-glycerophosphate. Then, we detected the LncRNA H19 promoter methylation status and Erk1/2 pathways using methylation-specific polymerase chain reaction and western blotting, respectively. RESULTS: Compared with the control group, high phosphorus levels induced VSMC calcification, accompanied by increases in LncRNA H19 and the osteogenic marker Runx2 and reduction of the contractile phenotype marker SM22a. LncRNA H19 knockdown inhibited osteogenic differentiation and calcification of VSMCs. However, the suppressed role of VSMC calcification caused by shRNA H19 was partially reversed by simultaneous activation of the Erk1/2 pathways. Mechanically, we found that the methylation rate of CpG islands in the LncRNA H19 promoter region was significantly lower in the high-phosphorus group, and the hypomethylation state elevated LncRNA H19 levels, which in turn regulated phosphorylated Erk1/2 expression. CONCLUSIONS: LncRNA H19 promoted osteogenic differentiation and calcification of VSMCs by regulating the Erk1/2 pathways. Additionally, hypomethylation of LncRNA H19 promoter CpG islands upregulated LncRNA H19 levels and subsequently activated Erk1/2 phosphorylation.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients.

Background: Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC. Methods: The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort. Results: In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model. Conclusion: We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

A new insight into Cd exposure-induced hemocyte reduction in Lymantria dispar larvae: Involvement of the ROS-ATF6-ER stress-apoptosis pathway.

Hemocytes are important targets for heavy metal-induced immunotoxicity in insects. This study aimed to investigate the mechanism by which cadmium (Cd) exposure affects the hemocyte count in Lymantria dispar larvae. The results showed that the number of larval hemocytes was significantly decreased under Cd exposure, accompanied by a significant increase in the apoptosis rate and the expression of Caspase-3. The endoplasmic reticulum (ER) of hemocytes in the Cd-treated group showed irregular swelling. Expression levels of ER stress indicator genes (CHOP, Bip1, Bip2, Bip3, and Bip4) were significantly higher in the Cd-treated group. Among the three pathways that potentially mediate ER stress, only the key genes in the ATF6 pathway (ATF6, S1P-1, S1P-2, and WFS1) exhibited differential responses to Cd exposure. Cd exposure significantly increased the levels of reactive oxygen species (ROS) and the expression of oxidative stress-related genes (CNCC, P38, and ATF2) in hemocytes. Studies using inhibitors confirmed that apoptosis mediated the decrease in hemocyte count, ER stress mediated apoptosis, ATF6 pathway mediated ER stress, and ROS or oxidative stress mediated ER stress through the activation of the ATF6 pathway. Taken together, the ROS-ATF6-ER stress-apoptosis pathway is responsible for the reduction in the hemocyte count of Cd-treated L. dispar larvae.

Effects of early recovery of renal function on adverse renal outcomes and mortality in patients with acute kidney injury: a systematic review and meta-analysis.

AIM: This study intended to scrutinize the effect of RFR time on adverse renal outcomes and mortality and try to define the cutoff of early RFR. METHODS: We conducted a literature search from database inception to February 2023. Outcome measures incorporated the progression of CKD, delivery of RRT, incidence of composite renal outcomes, and mortality. And pooled results were depicted as odds ratio (OR) and 95% confidence interval (CI). RESULTS: A total of 11 studies were finally selected (507,989 patients, mean follow-up, 3.89 years). The results exhibited that the crude mortality was lower in patients with early RFR (OR = 0.39, 95% CI: 0.16-0.95, P = 0.037). In addition, patients with early RFR had a lower incidence of progression to CKD (OR = 0.38, 95% CI: 0.17-0.85, P < 0.018), RRT (OR = 0.37, 95% CI: 0.20-0.71, P = 0.03), and composite renal outcomes (OR = 0.18, 95% CI: 0.15-0.20, P < 0.001). CKD progression-related events were significantly higher in patients whose renal function recovered after 7 days (OR = 0.69, 95% CI: 0.47-1.09, P = 0.112) than in those whose renal function recovered within 7 days (OR = 0.23, 95% CI: 0.06-0.92, P = 0.038), and the risk of RRT was lower in patients who recovered within 7 days (OR = 0.32, 95% CI: 0.15-0.66, P = 0.002) than in those who recovered after 7 days (OR = 0.72, 95% CI: 0.17-3.09, P = 0.654) or longer. CONCLUSION: Patients with early RFR had a lower risk of CKD progression, RRT, and composite renal outcomes, as well as lower crude mortality than those without early recovery, despite no marked difference in 30-day, 90-day, and 1-year mortality. We speculated that 7 days may be used as a cutoff for early RFR.

Interaction of Sp1 and Setd8 promotes vascular smooth muscle cells apoptosis by activating Mark4 in vascular calcification.

Vascular calcification (VC) is directly related to high mortality in chronic kidney disease (CKD), and cellular apoptosis of vascular smooth muscle cells (VSMCs) is a crucial process in the initiation of VC. Microtubule affinity-regulating kinase 4 (Mark4), known as a serine/threonine protein kinase, can induce cell apoptosis and autophagy by modulating Akt phosphorylation. However, the potential functions and molecular mechanisms of Mark4 in VSMCs apoptosis and calcification need to be further explored. Initially, our data indicated that the mRNA expression of Mark4 was prominently elevated in high phosphorus-stimulated human VSMCs compared with the other members in Marks. Consistently, Mark4 expression was found to be significantly increased in the calcified arteries of both CKD patients and rats. In vitro, silencing Mark4 suppressed apoptosis-specific marker expression by promoting Akt phosphorylation, finally attenuating VSMCs calcification induced by high phosphate. Mechanically, the transcription factor Sp1 was enriched in the Mark4 promoter region and modulated Mark4 transcription. Moreover, SET domain-containing protein 8 (Setd8) was proved to interact with Sp1 and jointly participated in the transcriptional regulation of Mark4. Finally, rescue experiments revealed that Setd8 contributed to VSMCs apoptosis and calcification by modulating Mark4 expression. In conclusion, these findings reveal that Mark4 is transcriptionally activated by Sp1, which is interacted with Setd8, to promote VSMCs calcification through Akt-mediated antiapoptotic effects, suggesting that Mark4 represents a potent and promising therapeutic target for VC in CKD.

Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.

MicroRNA (miRNA) serves as a pivotal regulator of numerous cellular processes, and the identification of miRNA-disease associations (MDAs) is crucial for comprehending complex diseases. Recently, graph neural networks (GNN) have made significant advancements in MDA prediction. However, these methods tend to learn one type of node representation from a single heterogeneous network, ignoring the importance of multiple network topologies and node attributes. Here, we propose SMDAP (Sequence hierarchical modeling-based Mirna-Disease Association Prediction framework), a novel GNN-based framework that incorporates multiple network topologies and various node attributes including miRNA seed and full-length sequences to predict potential MDAs. Specifically, SMDAP consists of two types of MDA representation: following a heterogeneous pattern, we construct a transfer learning-like synchronous mutual learning network to learn the first MDA representation in conjunction with the miRNA seed sequence. Meanwhile, following a homogeneous pattern, we design a subgraph-inspired asynchronous multi-scale embedding network to obtain the second MDA representation based on the miRNA full-length sequence. Subsequently, an adaptive fusion approach is designed to combine the two branches such that we can score the MDAs by the downstream classifier and infer novel MDAs. Comprehensive experiments demonstrate that SMDAP integrates the advantages of multiple network topologies and node attributes into two branch representations. Moreover, the area under the receiver operating characteristic curve is 0.9622 on DB1, which is a 5.06% increase from the baselines. The area under the precision-recall curve is 0.9777, which is a 7.33% increase from the baselines. In addition, case studies on three human cancers validated the predictive performance of SMDAP. Overall, SMDAP represents a powerful tool for MDA prediction.

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

BACKGROUND: Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. RESULTS: In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. CONCLUSION: Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Comparative efficacy of sodium thiosulfate, bisphosphonates, and cinacalcet for the treatment of vascular calcification in patients with haemodialysis: a systematic review and network meta-analysis.

BACKGROUND: Up to now, there is no unequivocal intervention to mitigate vascular calcification (VC) in patients with hemodialysis. This network meta-analysis aimed to systematically evaluate the clinical efficacy of sodium thiosulfate, bisphosphonates, and cinacalcet in treating vascular calcification. METHODS: A comprehensive study search was performed using PubMed, Web of Science, the Cochrane Library, EMBASE and China National Knowledge Internet (CNKI) to collect randomized controlled trials (RCTs) of sodium thiosulfate, bisphosphonates, and cinacalcet for vascular calcification among hemodialysis patients. Then, network meta-analysis was conducted using Stata 17.0 software. RESULTS: In total, eleven RCTs including 1083 patients were qualified for this meta-analysis. We found that cinacalcet (SMD - 0.59; 95% CI [-0.95, -0.24]) had significant benefit on vascular calcification compared with conventional therapy, while sodium thiosulfate or bisphosphonates did not show such efficiency. Furthermore, as for ranking the efficacy assessment, cinacalcet possessed the highest surface under the cumulative ranking curve (SUCRA) value (88.5%) of lessening vascular calcification and was superior to sodium thiosulfate (50.4%) and bisphosphonates (55.4%). Thus, above results suggested that cinacalcet might be the most promising drug for vascular calcification treatment in hemodialysis patients. Mechanistically, our findings illustrated that cinacalcet reduced serum calcium (SMD - 1.20; 95% CI [-2.08, - 0.33]) and showed the tendency in maintaining the balance of intact Parathyroid Hormone (iPTH) level. CONCLUSIONS: This network meta-analysis indicated that cinacalcet appear to be more effective than sodium thiosulfate and bisphosphonates in mitigating vascular calcification through decreasing serum calcium and iPTH. And cinacalcet might be a reasonable option for hemodialysis patients with VC in clinical practice. SYSTEMATIC REVIEW REGISTRATION: [ http://www.crd.york.ac.uk/PROSPERO ], identifier [CRD42022379965].

Large-scale genomic rearrangements boost SCRaMbLE in Saccharomyces cerevisiae.

Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) is a promising tool to study genomic rearrangements. However, the potential of SCRaMbLE to study genomic rearrangements is currently hindered, because a strain containing all 16 synthetic chromosomes is not yet available. Here, we construct SparLox83R, a yeast strain containing 83 loxPsym sites distributed across all 16 chromosomes. SCRaMbLE of SparLox83R produces versatile genome-wide genomic rearrangements, including inter-chromosomal events. Moreover, when combined with synthetic chromosomes, SCRaMbLE of hetero-diploids with SparLox83R leads to increased diversity of genomic rearrangements and relatively faster evolution of traits compared to hetero-diploids only with wild-type chromosomes. Analysis of the SCRaMbLEd strain with increased tolerance to nocodazole demonstrates that genomic rearrangements can perturb the transcriptome and 3D genome structure and consequently impact phenotypes. In summary, a genome with sparsely distributed loxPsym sites can serve as a powerful tool for studying the consequence of genomic rearrangements and accelerating strain engineering in Saccharomyces cerevisiae.

How does ultrasound-assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties.

Proteins are essential to human health with enormous food applications. Despite their advantages, plant and animal proteins often exhibit limited molecular flexibility and poor solubility due to hydrogen bonds, hydrophobic interactions, and ionic interactions within their molecular structures. Thus, there is an urgent need to modify the rigid structure of proteins to enhance their stability and functional properties. Ultrasound-assisted ionic liquid (UA-IL) treatment for developing compound modification and producing proteins with excellent functional properties has received interest. However, no review specifically addresses the interactions between UA-ILs and proteins. Hence, this review focused on recent research advancements concerning the effects and potential reaction mechanisms of UA-ILs on the physicochemical properties (including particle size; primary, secondary, and tertiary structure; and surface morphology) as well as the functionality (such as solubility, emulsifying properties, and foaming ability) of proteins. Moreover, the safety evaluation of modified proteins was also discussed from various perspectives, such as acute and chronic toxicity, genotoxicity, cytotoxicity, and environmental and microbial toxicity. This review demonstrated that UA-IL treatment-induced protein structural changes significantly impact the functional characteristics of proteins. This treatment approach efficiently promotes protein structure stretching and spatial rearrangement through cavitation, thermal effects, and ionic interactions. As a result, the functional properties of modified proteins exhibited an obvious enhancement, thereby bringing more opportunities to utilize modified protein products in the food industry. Potential future directions for protein modification using UA-ILs were also proposed.

The efficacy and safety of sacubitril/valsartan in chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: Sacubitril/valsartan, a new pharmacological class of angiotensin receptor neprilysin inhibitor, is beneficial to heart failure through blocking the degradation of natriuretic peptides and inhibiting renin-angiotensin-aldosterone system (RAAS) activation which also relate to the pathophysiologic mechanisms of chronic kidney disease (CKD). However, its effects on CKD remain unclear. To assess the efficacy and safety of sacubitril/valsartan for patients with CKD, we performed this meta-analysis. METHODS: The Embase, PubMed and the Cochrane Library were searched for randomized controlled trials (RCTs) that compared sacubitril/valsartan with ACEI/ARBs in patients with CKD whose estimated glomerular filtration rate (eGFR) was below 60 mL/min/1.73 m2. We adopted the Cochrane Collaboration tool for assessing the risk of bias. The effect size was estimated using the odds ratio (OR) with 95% confidence interval (CI). RESULTS: Six trials with a total of 6217 patients with CKD were included. In terms of cardiovascular events, sacubitril/valsartan attenuated the risk of cardiovascular death or heart failure hospitalization (OR: 0.68, 95% CI 0.61-0.76, P < 0.00001, I2 = 43%). With respect to renal function, sacubitril/valsartan prevented the incidence of serum creatinine (Scr) elevation among patients with CKD (OR: 0.79, 95% CI 0.67-0.95, P = 0.01, I2 = 0%). Subgroup analysis about eGFR demonstrated that with long follow-up, sacubitril/valsartan significantly decreased the number of patients with more than 50% reduction in eGFR compared with ACEI/ARBs (OR: 0.52, 95% CI 0.32-0.84, P = 0.008, I2 = 9%). In patients with CKD, the incidence of end-stage renal disease (ESRD) was reduced with sacubitril/valsartan treatment, despite no statistically significant difference between the two groups (OR: 0.59, 95% CI 0.29-1.20, P = 0.14, I2 = 0%). As for the safety, we found that sacubitril/valsartan was associated with the occurrence of hypotension (OR: 1.71, 95% CI 1.15-2.56, P = 0.008, I2 = 51%). However, there was no trend towards increasing the risk of hyperkalemia in patients who received sacubitril/valsartan (OR: 1.09, 95% CI 0.75-1.60, P = 0.64, I2 = 64%). CONCLUSION: This meta-analysis indicated that sacubitril/valsartan improved renal function and conferred effective cardiovascular benefits in patients with CKD, without serious safety issues being observed. Thus, sacubitril/valsartan may be a promising option for patients with CKD. Certainly, further large-scale randomized controlled trials are needed to confirm these conclusions. SYSTEMATIC REVIEW REGISTRATION: [ https://inplasy.com/inplasy-2022-4-0045/ ], identifier [INPLASY202240045].

The association of hemoglobin ethylene oxide levels with albuminuria in US adults: analysis of NHANES 2013-2016.

Ethylene oxide (EO) is a common chemical contaminant in the environment and associated with the occurrence of multiple clinical diseases. This study aimed to explore the association of hemoglobin ethylene oxide (HbEO) levels with the risk of albuminuria in a representative sample of US adults. In total, 3523 participants from National Health and Nutrition Examination Survey (NHANES) 2013-2016 were enrolled and classified into four groups based on HbEO. Restricted cubic spline plots and multiple logistic regression were performed to investigate the connection between HbEO levels and albuminuria, and mediation analysis was applied to elucidate the potential mechanism for the effect of HbEO concentrations on albuminuria. In the results, compared with the extreme quartile of HbEO levels, the weighted prevalence of albuminuria was significantly increased in participants with highest quartile (Q4 vs Q1, 11.2% vs 8.1%). Restricted cubic spline plots revealed that the risk of albuminuria raised non-linearly and positively with elevated HbEO level. After adjusting for confounders, the logistic regression suggested that the risk of albuminuria was enhanced by 12% for each one-unit increase in log-2-transformed HbEO (OR = 1.12, 95% CI, 1.03-1.22, P = 0.007). Moreover, the multivariate ORs (95% CIs) on albuminuria was increased across the increasing HbEO quartiles (Q4 vs Q1, OR = 1.54, 95% CI, 1.09-2.17; P for trend = 0.029). Furthermore, the impact of high HbEO level on albuminuria was partially related to inflammation markers, including white blood cells (17.2%), neutrophils (22.1%), and lymphocytes (19.5%). To sum up, our study identified that high HbEO levels increased the risk of albuminuria in representative population of US adults, and several inflammatory mediators might be potentially involved in EO-associated albuminuria.

EpiMCI: Predicting Multi-Way Chromatin Interactions from Epigenomic Signals.

The recently emerging high-throughput Pore-C (HiPore-C) can identify whole-genome high-order chromatin multi-way interactions with an ultra-high output, contributing to deciphering three-dimensional (3D) genome organization. However, it also brings new challenges to relevant data analysis. To alleviate this problem, we proposed the EpiMCI, a model for multi-way chromatin interaction prediction based on a hypergraph neural network with epigenomic signals as the input. The EpiMCI integrated separate hyperedge representations with coupling hyperedge information and obtained AUCs of 0.981 and 0.984 in the GM12878 and K562 datasets, respectively, which outperformed the current available method. Moreover, the EpiMCI can be applied to denoise the HiPore-C data and improve the data quality efficiently. Furthermore, the vertex embeddings extracted from the EpiMCI reflected the global chromatin architecture accurately. The principal component analysis suggested that it was well aligned with the activities of genomic regions at the chromatin compartment level. Taken together, the EpiMCI can accurately predict multi-way chromatin interactions and can be applied to studies relying on chromatin architecture.

RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion.

Identifying noncoding RNAs (ncRNAs)-drug resistance association computationally would have a marked effect on understanding ncRNA molecular function and drug target mechanisms and alleviating the screening cost of corresponding biological wet experiments. Although graph neural network-based methods have been developed and facilitated the detection of ncRNAs related to drug resistance, it remains a challenge to explore a highly trusty ncRNA-drug resistance association prediction framework, due to inevitable noise edges originating from the batch effect and experimental errors. Herein, we proposed a framework, referred to as RDRGSE (RDR association prediction by using graph skeleton extraction and attentional feature fusion), for detecting ncRNA-drug resistance association. Specifically, starting with the construction of the original ncRNA-drug resistance association as a bipartite graph, RDRGSE took advantage of a bi-view skeleton extraction strategy to obtain two types of skeleton views, followed by a graph neural network-based estimator for iteratively optimizing skeleton views aimed at learning high-quality ncRNA-drug resistance edge embedding and optimal graph skeleton structure, jointly. Then, RDRGSE adopted adaptive attentional feature fusion to obtain final edge embedding and identified potential RDRAs under an end-to-end pattern. Comprehensive experiments were conducted, and experimental results indicated the significant advantage of a skeleton structure for ncRNA-drug resistance association discovery. Compared with state-of-the-art approaches, RDRGSE improved the prediction performance by 6.7% in terms of AUC and 6.1% in terms of AUPR. Also, ablation-like analysis and independent case studies corroborated RDRGSE generalization ability and robustness. Overall, RDRGSE provides a powerful computational method for ncRNA-drug resistance association prediction, which can also serve as a screening tool for drug resistance biomarkers.