Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution.

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

MicroRNA regulation of adrenal glucocorticoid and androgen biosynthesis.

Steroid hormones are derived from a common precursor molecule, cholesterol, and regulate a wide range of physiologic function including reproduction, salt balance, maintenance of secondary sexual characteristics, response to stress, neuronal function, and various metabolic processes. Among the steroids synthesized by the adrenal and gonadal tissues, adrenal mineralocorticoids, and glucocorticoids are essential for life. The process of steroidogenesis is regulated at multiple levels largely by transcriptional, posttranscriptional, translational, and posttranslational regulation of the steroidogenic enzymes (i.e., cytochrome P450s and hydroxysteroid dehydrogenases), cellular compartmentalization of the steroidogenic enzymes, and cholesterol processing and transport proteins. In recent years, small noncoding RNAs, termed microRNAs (miRNAs) have been recognized as major post-transcriptional regulators of gene expression with essential roles in numerous biological processes and disease pathologies. Although their role in the regulation of steroidogenesis is still emerging, several recent studies have contributed significantly to our understanding of the role miRNAs play in the regulation of the steroidogenic process. This chapter focuses on the recent developments in miRNA regulation of adrenal glucocorticoid and androgen production in humans and rodents.

Association of spinal-pelvic parameters with recurrence of lumbar disc herniation after endoscopic surgery: a retrospective case-control study.

PURPOSE: This study aimed to investigate the relationship between spinal-pelvic parameters and recurrence of lumbar disc herniation (rLDH) after percutaneous endoscopic lumbar discectomy (PELD) through a retrospective case-control study. METHODS: Patients who underwent PELD for single-segment LDH at our hospital were included in this study. The relationship between sagittal balance parameters of the spine and recurrence was analysed through correlation analysis, and ROC curves were plotted. The baseline characteristics, sagittal balance parameters of the spine and radiological parameters of the case and control groups were compared, and the relationship between sagittal balance parameters of the spine and recurrence of rLDH after PELD was determined through univariate and multivariate logistic regression analysis. RESULTS: Correlation analysis showed that PI and ∆PI-LL were negatively correlated with grouping (r = -0.090 and -0.120, respectively, P = 0.001 and 0.038). ROC curve analysis showed that the area under the curve (ROC-AUC) for predicting rLDH based on PI was 0.65 (CI95% = 0.598, 0.720), with a cut-off of 50.26°. The ROC-AUC for predicting rLDH based on ∆PI-LL was 0.56 (CI95% = 0.503, 0.634), with a cut-off of 28.21°. Multivariate logistic regression analysis showed that smoking status (OR = 2.667, P = 0.008), PI ≤ 50.26 (OR = 2.161, P = 0.009), ∆PI-LL ≤ 28.21 (OR = 3.185, P = 0.001) and presence of Modic changes (OR = 4.218, P = 0.001) were independent risk factors, while high DH (OR = 0.788, P = 0.001) was a protective factor. CONCLUSION: PI < 50.26 and ∆PI-LL < 28.21 were risk factors for recurrence of lumbar disc herniation after spinal endoscopic surgery and had some predictive value for post-operative recurrence.

Spatial analysis of stromal signatures identifies invasive front carcinoma-associated fibroblasts as suppressors of anti-tumor immune response in esophageal cancer.

BACKGROUND: Increasing evidence indicates that the tumor microenvironment (TME) is a crucial determinant of cancer progression. However, the clinical and pathobiological significance of stromal signatures in the TME, as a complex dynamic entity, is still unclear in esophageal squamous cell carcinoma (ESCC). METHODS: Herein, we used single-cell transcriptome sequencing data, imaging mass cytometry (IMC) and multiplex immunofluorescence staining to characterize the stromal signatures in ESCC and evaluate their prognostic values in this aggressive disease. An automated quantitative pathology imaging system determined the locations of the lamina propria, stroma, and invasive front. Subsequently, IMC spatial analyses further uncovered spatial interaction and distribution. Additionally, bioinformatics analysis was performed to explore the TME remodeling mechanism in ESCC. To define a new molecular prognostic model, we calculated the risk score of each patient based on their TME signatures and pTNM stages. RESULTS: We demonstrate that the presence of fibroblasts at the tumor invasive front was associated with the invasive depth and poor prognosis. Furthermore, the amount of α-smooth muscle actin (α-SMA)+ fibroblasts at the tumor invasive front positively correlated with the number of macrophages (MØs), but negatively correlated with that of tumor-infiltrating granzyme B+ immune cells, and CD4+ and CD8+ T cells. Spatial analyses uncovered a significant spatial interaction between α-SMA+ fibroblasts and CD163+ MØs in the TME, which resulted in spatially exclusive interactions to anti-tumor immune cells. We further validated the laminin and collagen signaling network contributions to TME remodeling. Moreover, compared with pTNM staging, a molecular prognostic model, based on expression of α-SMA+ fibroblasts at the invasive front, and CD163+ MØs, showed higher accuracy in predicting survival or recurrence in ESCC patients. Regression analysis confirmed this model is an independent predictor for survival, which also identifies a high-risk group of ESCC patients that can benefit from adjuvant therapy. CONCLUSIONS: Our newly defined biomarker signature may serve as a complement for current clinical risk stratification approaches and provide potential therapeutic targets for reversing the fibroblast-mediated immunosuppressive microenvironment.

Endoscopic transcortical expanded transforaminal transvenous transchoroidal approach to third ventricle lesion resection using an endoport.

OBJECTIVE: To investigate the clinical experience and application value of endoscopic resection of lesions in and around the third ventricle using a transcortical expanded transforaminal transvenous transchoroidal approach with an endoport. METHODS: Clinical data and follow-up results of seven patients who underwent the removal of lesions in the third ventricle and its adjacent area with an endoport-guided endoscopic system from January 2018 to December 2020 in the Department of Neurosurgery, Zhongshan Hospital Affiliated to Fudan University, were analyzed retrospectively. Two other patients from the Affiliated Pediatric Hospital of Fudan University and the Affiliated Hospital of Guizhou Medical University, respectively, were included in the analysis. RESULTS: A total of nine cases of third ventricle tumors were included in the study, including six women and three men, with an average age of 37.8 years (4-84 years old) and a follow-up time of 6-44 months. These nine tumor cases included two pilocytic astrocytomas, one diffuse midline glioma (H3 K27-altered), two craniopharyngiomas, two choroid plexus (CP) papillomas, one germinoma, and one pineal parenchymal tumor of intermediate differentiation. Total resection was completed in eight cases, with one near-total resection. There were no complications related to the surgical approach, such as epilepsy, aphasia, or hemiplegia. CONCLUSIONS: The endoscope transcortical expanded transforaminal transvenous transchoroidal approach using an endoport can safely and effectively remove third ventricle lesions. This approach can reach a wide area, from the anterior to the posterior third ventricle.

Mice deficient in ER protein seipin have reduced adrenal cholesteryl ester lipid droplet formation and utilization.

Cholesteryl ester (CE)-rich lipid droplets (LDs) accumulate in steroidogenic tissues under physiological conditions and constitute an important source of cholesterol as the precursor for the synthesis of all steroid hormones. The mechanisms specifically involved in CE-rich LD formation have not been directly studied and are assumed by most to occur in a fashion analogous to triacylglycerol-rich LDs. Seipin is an endoplasmic reticulum protein that forms oligomeric complexes at endoplasmic reticulum-LD contact sites, and seipin deficiency results in severe alterations in LD maturation and morphology as seen in Berardinelli-Seip congenital lipodystrophy type 2. While seipin is critical for triacylglycerol-rich LD formation, no studies have directly addressed whether seipin is important for CE-rich LD biogenesis. To address this issue, mice with deficient expression of seipin specifically in adrenal, testis, and ovary, steroidogenic tissues that accumulate CE-rich LDs under normal physiological conditions, were generated. We found that the steroidogenic-specific seipin-deficient mice displayed a marked reduction in LD and CE accumulation in the adrenals, demonstrating the pivotal role of seipin in CE-rich LD accumulation/formation. Moreover, the reduction in CE-rich LDs was associated with significant defects in adrenal and gonadal steroid hormone production that could not be completely reversed by addition of exogenous lipoprotein cholesterol. We conclude that seipin has a heretofore unappreciated role in intracellular cholesterol trafficking.

Neutrophil-to-lymphocyte ratio as a potential biomarker in predicting influenza susceptibility.

Background: Human population exposed to influenza viruses exhibited wide variation in susceptibility. The ratio of neutrophils to lymphocytes (NLR) has been examined to be a marker of systemic inflammation. We sought to investigate the relationship between influenza susceptibility and the NLR taken before influenza virus infection. Methods: We investigated blood samples from five independent influenza challenge cohorts prior to influenza inoculation at the cellular level by using digital cytometry. We used multi-cohort gene expression analysis to compare the NLR between the symptomatic infected (SI) and asymptomatic uninfected (AU) subjects. We then used a network analysis approach to identify host factors associated with NLR and influenza susceptibility. Results: The baseline NLR was significantly higher in the SI group in both discovery and validation cohorts. The NLR achieved an AUC of 0.724 on the H3N2 data, and 0.736 on the H1N1 data in predicting influenza susceptibility. We identified four key modules that were not only significantly correlated with the baseline NLR, but also differentially expressed between the SI and AU groups. Genes within these four modules were enriched in pathways involved in B cell-mediated immune responses, cellular metabolism, cell cycle, and signal transduction, respectively. Conclusions: This study identified the NLR as a potential biomarker for predicting disease susceptibility to symptomatic influenza. An elevated NLR was detected in susceptible hosts, who may have defects in B cell-mediated immunity or impaired function in cellular metabolism, cell cycle or signal transduction. Our work can serve as a comparative model to provide insights into the COVID-19 susceptibility.

Analysis of the application of artificial intelligence technology in the protection of corporate governance rights and interests.

Corporate governance delivers feasible and controlled company operations using a group of common shareholders and appropriate policies. The roles and responsibilities of the shareholders suggest and improve corporate development through monotonous and independent rights. The implication of artificial intelligence provides knowledgeable insights for decision-making and control management. This article introduces a Mutual Consent-based Governance Regulation Model (MCGRM) for dissimilarity mitigation in corporate rule implications. The proposed model exploits transfer learning for balanced rule implication and decision-making. The learning states are defined based on mutual agreement, individual interest, and operational features. Based on the governance policies, the above rules are employed without hindering the pioneer regulations implemented in different periods. Therefore, artificial intelligence technology is utilized for prompt and swift governance decisions in delivering special rights for consumers and shareholders. The performance of this model is validated and verified using data sources related to governance policies from a real-time industry. The impact of varying policy features with dissimilarity is analyzed for varying occurrences. The analysis is given based on the considered data sources for which the classification and its impact over reports, sharing, voting, complaint, and market are analyzed. The availability before and after the proposed improves the above metrics by 10.48, 10.65, 9.78, 13.39, and 9.26%.

The effect of coupled inhibitory-facilitatory repetitive transcranial magnetic stimulation on shaping early reorganization of the motor network after stroke.

Neural plasticity is a major factor driving cortical reorganization after stroke. This study aimed to evaluate functional connectivity (FC) changes in the cortical motor network after coupled inhibitory-facilitatory repetitive transcranial magnetic stimulation (rTMS) treatment and to assess the correlation between FC changes and functional recovery, further characterizing the neural mechanisms underlying the beneficial effects of rTMS. We randomly divided 63 patients with acute stroke into four groups: (1) Group A received coupled inhibitory-facilitatory rTMS [1 Hz over the contralesional primary motor cortex (M1) and 10 Hz over ipsilesional M1]; (2) Group B received a contralesional sham stimulation and ipsilesional 10 Hz stimulation; (3) Group C received a contralesional 1 Hz rTMS and ipsilesional sham stimulation; and (4) Group D received bilateral sham stimulation only. Standardized rehabilitation therapy was performed immediately after rTMS, and each group was treated with their respective treatment modalities for 4 weeks. Twenty-four hours before and after the intervention, participants underwent resting-state functional magnetic resonance imaging. Additional functional assessments were conducted at baseline, after treatment, and at the 3 month follow-up. The rTMS treatment significantly changed the FCs of intra- and inter-hemispheric cortical motor networks in the rTMS groups (A and B) compared with the sham group (Group D). This effect was more pronounced in Group A, which displayed a changed FC between the contralesional postcentral gyrus and contralesional superior parietal gyrus, between the contralesional precentral gyrus and contralesional postcentral gyrus, and between the ipsilesional postcentral gyrus and contralesional superior parietal gyrus, when compared with Groups B and C. Importantly, FC changes were significantly correlated with improvement of motor function. In the early stages of ischemic stroke, coupled rTMS was more conducive to motor recovery by modulating the FCs of intra-hemispheric and inter-hemispheric motor networks. Our results suggested that FC changes were related to motor function recovery for early-stage cerebral stroke patients treated with coupled rTMS. These findings could help to understand the mechanism of coupled rTMS and further the use of this therapy as an adjunct rehabilitation technique in motor recovery.

Hormone sensitive lipase ablation promotes bone regeneration.

There is an inverse relationship between the differentiation of mesenchymal stem cells (MSCs) along either an adipocyte or osteoblast lineage, with lineage differentiation known to be mediated by transcription factors PPARγ and Runx2, respectively. Endogenous ligands for PPARγ are generated during the hydrolysis of triacylglycerols to fatty acids through the actions of lipases such as hormone sensitive lipase (HSL). To examine whether reduced production of endogenous PPARγ ligands would influence bone regeneration, we examined the effects of HSL knockout on fracture repair in mice using a tibial mono-cortical defect as a model. We found an improved rate of fracture repair in HSL-ko mice documented by serial µCT and bone histomorphometry compared to wild-type (WT) mice. Similarly, accelerated rates of bone regeneration were observed with a calvarial model where implantation of bone grafts from HSL-ko mice accelerated bone regeneration at the injury site. Further analysis revealed improved MSC differentiation down osteoblast and chondrocyte lineage with inhibition of HSL. MSC recruitment to the injury site was greater in HSL-ko mice than WT. Finally, we used single cell RNAseq to understand the osteoimmunological differences between WT and HSL-ko mice and found changes in the pre-osteoclast population. Our study shows HSL-ko mice as an interesting model to study improvements to bone injury repair. Furthermore, our study highlights the potential importance of pre-osteoclasts and osteoclasts in bone repair.

A Bayesian factorization method to recover single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. In this article, we introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute the final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than ten other publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene- or cell-related information that users provide to increase performance.

Asymmetric Graph-Guided Multitask Survival Analysis With Self-Paced Learning.

Recently, multitask learning has been successfully applied to survival analysis problems. A critical challenge in real-world survival analysis tasks is that not all instances and tasks are equally learnable. A survival analysis model can be improved when considering the complexities of instances and tasks during the model training. To this end, we propose an asymmetric graph-guided multitask learning approach with self-paced learning for survival analysis applications. The proposed model is able to improve the learning performance by identifying the complex structure among tasks and considering the complexities of training instances and tasks during the model training. Especially, by incorporating the self-paced learning strategy and asymmetric graph-guided regularization, the proposed model is able to learn the model in a progressive way from "easy" to "hard" loss function items. In addition, together with the self-paced learning function, the asymmetric graph-guided regularization allows the related knowledge transfer from one task to another in an asymmetric way. Consequently, the knowledge acquired from those earlier learned tasks can help to solve complex tasks effectively. The experimental results on both synthetic and real-world TCGA data suggest that the proposed method is indeed useful for improving survival analysis and achieves higher prediction accuracies than the previous state-of-the-art methods.

Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data.

Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC).

SNAP25 mutation disrupts metabolic homeostasis, steroid hormone production and central neurobehavior.

OBJECTIVE: SNAP-25 is one of the key proteins involved in formation of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complexes that are at the core of hormonal secretion and synaptic transmission. Altered expression or function of SNAP-25 can contribute to the development of neuropsychiatric and metabolic disease. A dominant negative (DN) I67T missense mutation in the b-isoform of SNAP-25 (DN-SNAP25mut) mice leads to abnormal interactions within the SNARE complex and impaired exocytotic vesicle recycling, yet the significance of this mutation to any association between the central nervous system and metabolic homeostasis is unknown. METHODS: Here we explored aspects of metabolism, steroid hormone production and neurobehavior of DN-SNAP25mut mice. RESULTS: DN-SNAP25mut mice displayed enhanced insulin function through increased Akt phosphorylation, alongside increased adrenal and gonadal hormone production. In addition, increased anxiety behavior and beigeing of white adipose tissue with increased energy expenditure were observed in mutants. CONCLUSIONS: Our results show that SNAP25 plays an important role in bridging central neurological systems with peripheral metabolic homeostasis, and provide potential insights between metabolic disease and neuropsychiatric disorders in humans.

The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry.

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. Tumor heterogeneity continues to confound researchers' understanding of tumor growth and the development of an effective therapy. Digital cytometry allows interpretation of heterogeneous bulk tissue transcriptomes at the cellular level. We built a novel signature matrix to dissect epithelium and stroma signals using a scRNA-seq data set (GSE134520) for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts (GSE62254, GSE15459, GSE84437, and TCGA-STAD) from the GEO and TCGA databases. Robust computational methods were applied to identify strong prognostic factors for GC. We identified an EMEC population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. We found that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival (HR = 0.92, 95% CI = 0.89-0.94, p=2.05×10-9). The entire GC cohort was stratified into three risk groups (high-, moderate-, and low-risk), which yielded incremental survival times (p<0.0001). For stage III disease, patients in the moderate- and low-risk groups experienced better survival benefits from radiation therapy ((HR = 0.16, 95% CI = 0.06-0.4, p<0.0001), whereas those in the high-risk group did not (HR = 0.49, 95% CI = 0.14-1.72, p=0.25). We concluded that the STEM score is a promising prognostic factor for gastric cancer.

Effects of mirror therapy on motor aphasia after acute cerebral infarction: A randomized controlled trial.

BACKGROUND: Mirror therapy (MT) has proven to be beneficial for treating patients suffering from motor aphasia after stroke. However, the impacts of MT on neuroplasticity remain unexplored. OBJECTIVE: In this paper we conducted a randomized controlled trial to evaluate the treatment using the MT on motor aphasia following acute cerebral infarction. METHODS: We randomly assigned 30 patients into test and control groups, with test group patients treated with MT, whereas control group patients were treated with sham MT. At 24 hours prior to and after the intervention, we obtained functional magnetic resonance imaging (fMRI) data from study subjects. At baseline, after treatment and 12-week follow-up, we additionally evaluated patients with the Modified Rankin Scale (mRS), the National Institutes of Health Stroke Scale (NIHSS), and the aphasia quotient (AQ) in the western aphasia test. RESULTS: After 2 weeks of treatment, the test group demonstrated significant improvements in AQ values, naming, repetition, spontaneous speech, and mRS scores compared to the control group (P < 0.05). Furthermore, in the follow-up time point (12 weeks), we found that the test group exhibited significantly better NIHSS scores and AQ evaluation indicators than the control group (P < 0.05). Specifically, the fMRI study shows that functional connectivity significantly improved in test group patients mainly among frontal, temporal, and parietal lobes of the left hemisphere with each other than controls group. Meanwhile, we found significantly enhanced functional connectivity with the hippocampus (P < 0.01). CONCLUSIONS: Our results indicate that the MT can expedite the recovery of language function during the early phases of stroke recovery. These findings may elucidate the underlying mechanism of MT and the application of this therapy as an adjunct rehabilitation technique in language recovery.

Effects of Spironolactone on Hypoxia-Inducible Factor-1α in the Patients Receiving Coronary Artery Bypass Grafting.

ABSTRACT: We explored the protective effect of spironolactone on cardiac function in the patients undergoing coronary artery bypass grafting (CABG) by determining serum hypoxia-inducible factor-1α (HIF-1α) before and after CABG. We used the propensity score matching method retrospectively to select 174 patients undergoing CABG in our hospital from March 2018 to December 2019. Of the 174 patients, 87 patients taking spironolactone for more than 3 months before CABG were used as a test group and other 87 patients who were not taking spironolactone as a control group. In all patients, serum HIF-1α and troponin I levels were determined before as well as 24 hours and 7 days after CABG, serum N-terminal probrain natriuretic peptide (NT-proBNP) level was determined before as well as 12, 24, and 36 hours after CABG, and electrocardiographic monitoring was performed within 36 hours after CABG. The results indicated that there were no significant differences in the HIF-1α level between the test group and the control group before and 7 days after CABG, but the HIF-1α level was significantly lower in the test group than that in the control group 24 hours after CABG (P < 0.01). The 2 groups were not significantly different in the troponin I level at any time point. There was no significant difference in the serum NT-proBNP level between the test group and the control group before CABG, but NT-proBNP (BNP) levels were all significantly lower in the test group than those in the control group at postoperative 12, 24, and 36 hour time points (all P <0.05). The incidence of postoperative atrial fibrillation was also significantly lower in the test group than that in the control group (P = 0.035). Spironolactone protects cardiac function probably by improving myocardial hypoxia and inhibiting myocardial remodeling.