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BACKGROUND: The optimal alternative treatment strategy to coronary artery bypass graft surgery (CABG) for in-stent restenosis (ISR) in left main (LM) coronary artery disease remains uncertain. METHODS: We retrospectively screened all intervention reports from an intervention database and extracted those mentioning an LM stent. We then manually confirmed reports involving LM ISR and divided them into two groups, those in which the patient received a new drug-eluting stent (new-DES) strategy, and those in which the patient received a drug-coated balloon (DCB) only. A composite endpoint of major adverse cardiovascular events (MACEs) and each individual endpoint were compared. We also performed a brief analysis of similar designed studies. RESULTS: Between the new-DES (n = 40) and DCB-only (n = 22) groups, during median respective follow-up times of 581.5 and 642.5 days, no significant statistical differences were detected in MACEs (50.0% vs. 50.0%, p = 0.974), cardiovascular death (27.5% vs. 13.6%, p = 0.214), nonfatal myocardial infarction (30.0% vs. 31.8%, p = 0.835), or target lesion revascularization (35.0% vs. 45.5%, p = 0.542). We analyzed four similar studies and found comparable MACE findings (odds ratio: 0.85, 95% CI: 0.44-1.67). CONCLUSIONS: Our findings support both DCB angioplasty and repeat DES implantation for LMISR lesions in patients who were clinically judged to be unsuitable for CABG; the treatments achieved comparable clinical results in terms of MACEs in the medium term.

Additional file 1: Supplementary Figure 1. Low-dose SNP treatment increased nitric oxide levels in vivo and induced protein nitrosylation in vitro.(A) Nitric oxide level in the serum of control or 0.1 mg/kg SNP-treated mice was determined by measuring total serum nitrite and nitrate levels. (B). Total nitrosylated protein levels in THP-1 cells were detected using anti-biotin antibody after 30 min of SNP treatment, and GAPDH levels served as the loading control in western blotting. (C) Nitrosylated Hsp90 (SNO-Hsp90) was measured by western blotting after streptavidin agarose bead collection. The column scatter dot plot represents the mean values ± SEMs. The p-value was obtained by t test. Supplementary Figure 2. Low-dose SNAP regulates cytokine expression levels and the Th2 cell population in the spleen. (A) The tumor weight and tumor size on the day mice were sacrificed. Tumor tissues were isolated from LL2 tumor-bearing mice. The mice were sacrificed on the 20th day after LL2 tumor implantation. (B) A cytokine array was used to determine the cytokine levels in the culture medium of splenocytes, which were co-cultured with LL2 cell lysates at 37 °C for 24 hr. The splenocytes were collected from 0.004 mg/kg SNAP treated mice (upper) and PBS-containing 0.004 % DMSO treated mice (lower). (C) The coordinates of every cytokine in the array. (D) The expression levels of each cytokine in the control and low-dose SNAP treatment groups. (E) The quadrants represent the percentage of CD4+ and/or IL-4-positive cells. (F) The percentage of IL-4-positive cells in the CD4+ population (type 2 T helper cells; Th2). LL2 tumor-bearing mice were administered SNAP (0.004 mg/kg) at 11 to 13 and 16 to 18 days, and splenocytes were isolated and analyzed. The column scatter dot plot represents the mean values ± SEMs. *p < 0.05, versus the control group. ns, no significant difference. P-values of the Th2 percentages were obtained by t test. Supplementary Figure 3. Reduction of cell clusters from 11 to 6 by the levels of marker genes expressed in CD45+ cells. (A)Clustering of CD45+ and subset visualization. UMAP dimensionality reduction of total CD45+ cells (6563 cells) was executed based on visualization of relevant cell clusters. All clusters were determined using the “FindCluster” function in the Seurat package. (B) Heatmap displaying the top 5 differentially expressed genes (DEGs) and mRNA levels in 11 clusters, and the top five DEGs list is presented in Supplementary Data 1. (C) Feature plot depicting single-cell gene expression of marker genes. Cd14, Cd68, and Itgam are marker genes for tumor-associated macrophages (TAMs). Cd3e and Trac were used for T-cell identification. S100a8, S100a9 and Fcgr1 were used to identify monocytes (monocytes did not express Fcgr1). Dendritic cells were identified by Itgax (CD11c) and H2-Ab1 (MHC II molecule). B cells expressed the Igkc and Cd79a genes. Cald1 and Col3a1 were expressed on fibroblasts. (D) Violin plot showing the expression levels of each marker gene in each cluster. Supplementary Figure 4. T lymphocytes are distinguished into 6 clusters and identified by marker genes. (A) Subclustering of T lymphocytes and visualization of the subsets.UMAP dimensionality reduction of 813 T lymphocytes (control: 463; drug: 350) was executed based on visualization of relevant cell clusters. All clusters were determined using the “FindCluster” function in the Seurat package. (B) Heatmap displaying the top 5 DEGs and mRNA levels in six subsets of T lymphocytes, and the gene list is shown in Supplementary Data 2. (C) Feature plot showing the single-cell gene expression of marker genes. Cd3e represents T cells. CD8a (Ab) and Cd8a are marker genes of CD8+ T cells. Gzma, Gzmb, Prf1, and Nkg7 were used to identify the cytotoxic function of cells. Tcf7 is an essential marker of central memory T cells. CD4 (Ab) was used to identify CD4+ T cells, and Il2ra (CD25) and Ctla4 determined the regulatory function of CD4+ T cells. Mki67 is a proliferation marker. Supplementary Figure 5. TCF7 and CD69 expression levels in T-cell subsets and GSEA analysis of NK cells. (A) Violin plot showing Tcf7 and Cd69 expression levels in T-cell subsets. (B) MSigDB Hallmark gene set displayed significant overlap with the DEGs of natural killer cells. The DEGs of NK cells were identified by the “FindMarker” function in the Seurat package and the DEGs list was shown in supplementary data 4. p-values of the violin plot were obtained by ANOVA. **p < 0.01, ***p< 0.001, ****p< 0.0001 versus the TAM1 group. Supplementary Figure 6. Low-dose SNAP regulates the Wnt/beta-catenin pathway in CD8+ T cells. (A) T lymphocytes were identified by CD4 (Ab) and CD8 (Ab) Abseq antibodies. Q3 are designated as CD8+ T cells. (B) Bar chart showing the significant pathways in CD8+ T cells after low-dose SNAP treatment. Significant genes were selected for pathway analysis, and the gene list is shown in Supplementary Data 5 (p-value < 0.05, fold change > 1.5, percentages of expressed cells > 10, and the p-value was obtained by t test). (C) Ctnnb1 (beta-catenin) and (D) Tcf7 mRNA levels in intratumoral CD8+ T cells from the control and 0.004 mg/kg SNAP treatment groups as determined by RT‒qPCR. The column scatter dot plot represents the mean values ± SEMs. p-values of the column scatter dot plot were obtained by t test. *p< 0.05, ns, no significant difference. Supplementary Figure 7. Low-dose SNAP treatment did not significantly induce the expression of surface calreticulin in LL2 tumor-bearing mice. Flow cytometry analysis of surface calreticulin in CD326-positive tumor cells isolated from LL2 tumor-bearing mice between the control and 0.004 mg/kg SNAP treatment groups. The mice were sacrificed at Day 20 after LL2 cell implantation. The column scatter dot plot represents the mean values ± SEMs. The p-values were obtained by t tests. Supplementary Figure 8. The mRNA levels of the top 5 differentially expressed genes in the TAM subsets. Violin plot demonstrating the expression levels of the top five DEGs in each subset of TAMs. The Y-axis represents log-normalized expression levels. Supplementary Figure 9. TAM1 cells do not express the immunosuppressive cytokine IL-10. Ridge plots determining IL-10 expression in the TAM1 subset. The X-axis represents log-normalized expression levels. p-values of the violin plot were obtained using the Wilcoxontest. ns, no significant difference. Supplementary Figure 10. Single-cell RNA sequencing analysis of combination cisplatin and low-dose SNAP treatment. (A) Feature plot demonstrating the gene expression of marker genes. Cd14, Cd68, and Itgam are marker genes for tumor-associated macrophages (TAMs). Cd3e and Trac were used for T-cell identification. S100a8, S100a9 and Fcgr1 were used to identify monocytes (monocytes did not express Fcgr1). Dendritic cells were identified by Itgax (CD11c) and H2-Ab1 (MHC II molecular). B cells expressed the genes Igkc and Cd79a. Cald1 and Col3a1 were expressed on fibroblasts. (B) Left panel: Clustering of intratumoral CD45+ cells and visualization of each type of cell. Uniform manifold approximation and projection (UMAP) of single-cell RNA sequencing data from 6690 cells (Control: 1909; Low-dose SNAP: 1943, Cisplatin: 1474, and Drug Combination: 1364) revealed six clusters determined by 14 specific markers (Supplementary Fig. 2). Right panel: Subclustering of T lymphocytes and visualization of the subsets. UMAP dimensionality reduction of 403 T lymphocytes was executed based on visualization of relevant cell clusters. (C) Upper panel: Violin plots revealed the expression levels of the marker genes for the subset of T cells. The Y-axis represents log-normalized expression levels. Cd3e serve as a T-cell marker; Foxp3, Il2ra, and Cd4 serve as CD4 regulatory T-cell markers; Gzmb, Ifng, and Cd8a serve as CD8 cytotoxic T-cell markers; Mki67, Top2a, and Pclaf serve as proliferating T-cell markers; Klrk, Nkg7, and Gzma serve as NK cell markers. Lower panel: The percentages of each group of the subset of T cluster within total immune cells. (D) GSEA of CD8+ cytotoxic T cell DEGs between combination and cisplatin treatment. The red bar chart and blue bar chart show upregulated and downregulated pathways in the combination treatment group, respectively. Supplementary Figure 11. Treatment with 0.004 mg/kg but not 0.02 mg/kg SNAP decreased angiogenesis in the tumor microenvironment. (A) Frozen sections of 0.004 mg/kg or (B) 0.02 mg/kg SNAP-treated and control tumors from LL2 tumor-bearing mice stained with the angiogenesis marker CD31 (red). Five randomly chosen areas in each tumor sample were used to calculate the CD31+ area. The column scatter dot plot represents the mean values ± SEMs. *p < 0.05, ns, no significant difference. p-values of the column scatter dot plot were obtained by t test.

Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Researchers must understand the effects of various treatments on recurrent events and investigate the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real-world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process., Comment: In preparation for journal submission

BACKGROUND: Hydroxychloroquine is used as an antimalarial and immunomodulator, however it can induce QT prolongation that could potentially lead to fatal arrhythmia. We investigated changes in QT interval in long-term hydroxychloroquine users, and identified possible risk factors associated with significant QTc prolongation. METHODS: We retrospectively enrolled 3603 patients who received long-term hydroxychloroquine treatment from 2009 to 2019, of whom 167 had electrocardiography (ECG) results before and during hydroxychloroquine therapy. Baseline characteristics, laboratory data, comorbidities, concurrent medications, and related clinical outcomes were reviewed. RESULTS: Overall, 225 patients (6.2%) died within the study period, with 50 patients (1.4%) continuously receiving hydroxychloroquine treatment until death. Three patients had fatal ventricular arrhythmia. No significant change in corrected QT interval (QTc) was noted before and during hydroxychloroquine treatment (451.1 ± 39.9 ms vs. 456.0 ± 37.3 ms, P = 0.140) in the ECG cohort. Multivariable logistic regression showed that diabetes mellitus [odds ratio (OR): 9.55, 95% confidence interval (CI): 2.02-45.22; P = 0.005] and use of additional QT-prolonging drugs (OR: 2.89, 95% CI: 1.40-5.94; P = 0.004) were independent risk factors for significant QTc prolongation. Multiple linear regression, with the number of QT-prolonging drugs and comorbidities including diabetes mellitus, hypertension, and atrial fibrillation as explanatory variables, predicted QTc response (adjusted R(2) = 0.385) in the long-term hydroxychloroquine users. CONCLUSIONS: In the long-term users of hydroxychloroquine, those with diabetes mellitus and concurrent use of additional QT-prolonging drugs were at a higher risk of significant QTc prolongation. Baseline QTc interval, concurrent medications, and comorbidities predicted QTc response.

Additional file 1.

Additional file 1. Additional tables and figures.

RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated several batch removal techniques and discussed their effectiveness. We illustrate that by adjusting for batch effect, more reliable differentially expressed genes can be identified. Our study on batch effect in miRNAseq data can serve as a guideline for future miRNAseq studies that might contain batch effect.