Cholinergic macrophages promote the resolution of peritoneal inflammation.

The non-neural cholinergic system plays a critical role in regulating immune equilibrium and tissue homeostasis. While the expression of choline acetyltransferase (ChAT), the enzyme catalyzing acetylcholine biosynthesis, has been well documented in lymphocytes, its role in the myeloid compartment is less understood. Here, we identify a significant population of macrophages (Mϕs) expressing ChAT and synthesizing acetylcholine in the resolution phase of acute peritonitis. Using Chat-GFP reporter mice, we observed marked upregulation of ChAT in monocyte-derived small peritoneal Mϕs (SmPMs) in response to Toll-like receptor agonists and bacterial infections. These SmPMs, phenotypically and transcriptionally distinct from tissue-resident large peritoneal macrophages, up-regulated ChAT expression through a MyD88-dependent pathway involving MAPK signaling. Notably, this process was attenuated by the TRIF-dependent TLR signaling pathway, and our tests with a range of neurotransmitters and cytokines failed to induce a similar response. Functionally, Chat deficiency in Mϕs led to significantly decreased peritoneal acetylcholine levels, reduced efferocytosis of apoptotic neutrophils, and a delayed resolution of peritonitis, which were reversible with exogenous ACh supplementation. Intriguingly, despite B lymphocytes being a notable ChAT-expressing population within the peritoneal cavity, Chat deletion in B cells did not significantly alter the resolution process. Collectively, these findings underscore the crucial role of Mϕ-derived acetylcholine in the resolution of inflammation and highlight the importance of the non-neuronal cholinergic system in immune regulation.

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

The ATR-WEE1 kinase module promotes SUPPRESSOR OF GAMMA RESPONSE 1 translation to activate replication stress responses.

DNA replication stress threatens genome stability and is a hallmark of cancer in humans. The evolutionarily conserved kinases ATR (ATM and RAD3-related) and WEE1 are essential for the activation of replication stress responses. Translational control is an important mechanism that regulates gene expression, but its role in replication stress responses is largely unknown. Here we show that ATR-WEE1 control the translation of SUPPRESSOR OF GAMMA RESPONSE 1 (SOG1), a master transcription factor required for replication stress responses in Arabidopsis thaliana. Through genetic screening, we found that the loss of GENERAL CONTROL NONDEREPRESSIBLE 20 (GCN20) or GCN1, which function together to inhibit protein translation, suppressed the hypersensitivity of the atr or wee1 mutant to replication stress. Biochemically, WEE1 inhibits GCN20 by phosphorylating it; phosphorylated GCN20 is subsequently polyubiquitinated and degraded. Ribosome profiling experiments revealed that that loss of GCN20 enhanced the translation efficiency of SOG1, while overexpressing GCN20 had the opposite effect. The loss of SOG1 reduced the resistance of wee1 gcn20 to replication stress, whereas overexpressing SOG1 enhanced the resistance to atr or wee1 to replication stress. These results suggest that ATR-WEE1 inhibits GCN20-GCN1 activity to promote the translation of SOG1 during replication stress. These findings link translational control to replication stress responses in Arabidopsis.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Regional analysis to delineate intrasample heterogeneity with RegionalST.

MOTIVATION: Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. RESULTS: To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

Analysis of endophytic bacterial diversity in seeds of different genotypes of cotton and the suppression of Verticillium wilt pathogen infection by a synthetic microbial community.

BACKGROUND: In agricultural production, fungal diseases significantly impact the yield and quality of cotton (Gossypium spp.) with Verticillium wilt posing a particularly severe threat. RESULTS: This study is focused on investigating the effectiveness of endophytic microbial communities present in the seeds of disease-resistant cotton genotypes in the control of cotton Verticillium wilt. The technique of 16S ribosomal RNA (16S rRNA) amplicon sequencing identified a significant enrichment of the Bacillus genus in the resistant genotype Xinluzao 78, which differed from the endophytic bacterial community structure in the susceptible genotype Xinluzao 63. Specific enriched strains were isolated and screened from the seeds of Xinluzao 78 to further explore the biological functions of seed endophytes. A synthetic microbial community (SynCom) was constructed using the broken-rod model, and seeds of the susceptible genotype Xinluzao 63 in this community that had been soaked with the SynCom were found to significantly control the occurrence of Verticillium wilt and regulate the growth of cotton plants. Antibiotic screening techniques were used to preliminarily identify the colonization of strains in the community. These techniques revealed that the strains can colonize plant tissues and occupy ecological niches in cotton tissues through a priority effect, which prevents infection by pathogens. CONCLUSION: This study highlights the key role of seed endophytes in driving plant disease defense and provides a theoretical basis for the future application of SynComs in agriculture.

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear. PURPOSE: To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images. STUDY TYPE: Retrospective. POPULATION: 85 ACM patients (mean age: 45 ± 17 years, 52 male). FIELD STRENGTH/SEQUENCE: 3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE). ASSESSMENT: Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE). STATISTICAL TESTS: Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance. RESULTS: 26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately. DATA CONCLUSION: The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

MR Uniformity Ratio Estimates to Evaluate Ventricular Mechanical Dyssynchrony and Prognosis After ST-Segment Elevation Myocardial Infarction.

BACKGROUND: The impact of left ventricular mechanical dyssynchrony (LVMD) on the long-term prognosis of ST-segment elevation myocardial infarction (STEMI) is unclear. HYPOTHESIS: MR uniformity ratio estimates (URE) can detect LVMD and assess STEMI prognosis. STUDY TYPE: Retrospective analysis of a prospective multicenter registry (EARLY-MYO trial, NCT03768453). POPULATION: Overall, 450 patients (50 females) with first-time STEMI were analyzed, as well as 40 participants without cardiovascular disease as controls. FIELD STRENGTH/SEQUENCE: 3.0-T, balanced steady-state free precession cine and late gadolinium enhancement imaging. ASSESSMENT: MRI data were acquired within 1 week of symptom onset. Major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal re-infarction, hospitalization for heart failure, and stroke, were the primary clinical outcomes. LVMD was represented by circumferential URE (CURE) and radial URE (RURE) calculated using strain measurements. The patients were grouped according to clinical outcomes or URE values. Patients' clinical characteristics and MR indicators were compared. STATISTICAL TESTS: The Student's t-test, Mann-Whitney U test, chi-square test, Fisher's exact test, receiver operating characteristic curve analysis with area under the curve, Kaplan-Meier analysis, Cox regression, logistic regression, intraclass correlation coefficient, c-index, and integrated discrimination improvement were used. P < 0.05 was considered statistically significant. RESULTS: CURE and RURE were significantly lower in patients with STEMI than in controls. The median follow-up was 60.5 months. Patients with both lower CURE and RURE values experienced a significantly higher incidence of MACEs by 3.525-fold. Both CURE and RURE were independent risk factors for MACEs. The addition of UREs improved diagnostic efficacy and risk stratification based on infarct size and left ventricular ejection fraction (LVEF). The indicators associated with LVMD included male sex, serum biomarkers (peak creatine phosphokinase and cardiac troponin I), infarct size, and LVEF. DATA CONCLUSION: CURE and RURE may be useful to evaluate long-term prognosis after STEMI. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Efficient Template-Catalyzed In Situ Polymerization for Carbon Xerogels with Large Specific Surface Area and High Adsorption.

The limited specific surface area (SSA), long preparation period, and high cost are significant challenges for carbon xerogels (CXs). To overcome these limitations, we propose an approach to prepare tannin-resorcinol-formaldehyde-based CXs through template-catalyzed in situ polymerization. ZnCl2 acts as a catalyst and significantly accelerates the polymerization reaction through the coordination of Zn2+ to the carbonyl group in formaldehyde, while atmospheric drying instead of special drying and without solvent exchange reduces the preparation period to 24 h. In addition, ZnCl2 acts as an activator for the formation of many pores. Plant-derived tannins not only reduce the preparation cost but also regulate the pore structure. The resulted CXs with hierarchical porous structures show an optimal SSA of 1308 m2/g, high adsorption capabilities (for cationic, nitrosoaniline dyes, metal, and nonmetallic ions, especially for methylene blue with 454.93 mg/g), low shrinkage down to 10%, and reusability with 92.9% retention after 5 cycles. This work provides a promising and cost-effective method for the large-scale preparation of porous carbon materials with large SSA, offering potential applications in adsorption, energy storage, and catalysis.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Prognostic and predictive value of examined lymph node count in stage III colorectal cancer: a population based study.

BACKGROUND: The role of tumor-draining lymph nodes in the progression of malignant tumors, including stage III colorectal cancer (CRC), is critical. However, the prognostic and predictive value of the number of examined lymph nodes (ELNs) are not fully understood. METHODS: This population-based study retrospectively analyzed data from 106,843 patients with stage III CRC who underwent surgical treatment and registered in three databases from 2004 to 2021. The Surveillance, Epidemiology, and End Results (SEER) cohort was divided using into training and test cohorts at a ratio of 3:2. We employed restricted cubic spline (RCS) curves to explore nonlinear relationships between overall survival (OS) and ELNs counts and performed Cox regression to evaluate hazard ratios across different ELNs count subtypes. Additional validation cohorts were utilized from the First Affiliated Hospital, Sun Yat-sen University and The Cancer Genome Atlas (TCGA) under the same criteria. Outcomes measured included OS, cancer-specific survival (CSS), and progression-free survival (PFS). Molecular analyses involved differential gene expression using the "limma" package and immune profiling through CIBERSORT. Tissue microarray slides and multiplex immunofluorescence (MIF) were used to assess protein expression and immune cell infiltration. RESULTS: Patients with higher ELNs counts (≥ 17) demonstrated significantly better long-term survival outcomes across all cohorts. Enhanced OS, CSS, and PFS were notably evident in the LN-ELN group compared to those with fewer ELNs. Cox regression models underscored the prognostic value of higher ELNs counts across different patient subgroups by age, sex, tumor differentiation, and TNM stages. Subtype analysis based on ELNs count revealed a marked survival benefit in patients treated with adjuvant chemotherapy in the medium and large ELNs counts (≥ 12), whereas those with fewer ELNs showed negligible benefits. RNA sequencing and MIF indicated elevated immune activation in the LN-ELN group, characterized by increased CD3+, CD4+, and CD8 + T cells within the tumor microenvironment. CONCLUSIONS: The number of ELNs independently predicts survival and the immunological landscape at the tumor site in stage III CRC, underscoring its dual prognostic and predictive value.

Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk.

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.

A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility.

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk.

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.

Myocardial extracellular volume quantified by cardiac magnetic resonance predicts left ventricular aneurysm following acute myocardial infarction.

OBJECTIVE: This study aimed to investigate the correlation between increased extracellular matrix estimated by cardiac magnetic resonance (CMR) and left ventricular aneurysm after acute myocardial infarction. METHODS: A total of 175 patients from 3 centers with an isolated left anterior descending culprit vessel underwent CMR examinations within 1 week and at a 6-month follow-up. Of these, 92 were identified to have left ventricular aneurysms (LVAs): 74 with functional aneurysm and 18 with anatomical aneurysm. The predictive significance of acute extracellular volume (ECV), left gadolinium enhancement (LGE), and other characteristics were analyzed using binary logistic regression analysis. RESULTS: Patients with LVA were more likely to present with left ventricular adverse remodeling (LVAR) than those without (p = 0.009). With optimal cutoff values of 30.90% for LGE and 33% for ECV to discriminate LVA from non-LVA, the area under the curve (AUC) by receiver operator characteristic curve (ROC) analysis was 0.92 (95% CI: 0.87-0.96; p < 0.001) and 0.93 (95% CI: 0.88-0.96; p < 0.001), respectively. ECV was significantly better than LGE at discriminating between functional and anatomical LVA (p < 0.001). Both acute LGE and ECV were predictors of LVA, with an odds ratio of 1.35 (95% CI: 1.21-1.52, p < 0.001) and 1.23 (95% CI: 1.13-1.33, p < 0.001), respectively, by multivariable logistic regression analysis. CONCLUSIONS: Acute LGE and ECV of the myocardium provided predictive significance for LVA. The discriminative significance of ECV for functional versus anatomical LVA was better than the discriminative significance of LGE. KEY POINTS: • Patients with LVA were more likely to present with LVAR. • Acute LGE and ECV of the myocardium provided the strongest predictive significance for LVA. • The discriminative significance of ECV for functional versus anatomical LVA was better than that of LGE.

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.

OBJECTIVE: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. METHODS: A total of 401 participants (311 patients and 90 age-matched volunteers) were recruited for this prospective study. The two-dimensional UNet segmentation model of the LV and classification model for identifying paradoxical pulsation were established using the DCNN model. Features of 2- and 3-chamber images were extracted with 2-dimensional (2D) and 3D ResNets with masks generated by a segmentation model. Next, the accuracy of the segmentation model was evaluated using the Dice score and classification model by receiver operating characteristic (ROC) curve and confusion matrix. The areas under the ROC curve (AUCs) of the physicians in training and DCNN models were compared using the DeLong method. RESULTS: The DCNN model showed that the AUCs for the detection of paradoxical pulsation were 0.97, 0.91, and 0.83 in the training, internal, and external testing cohorts, respectively (p < 0.001). The 2.5-dimensional model established using the end-systolic and end-diastolic images combined with 2-chamber and 3-chamber images was more efficient than the 3D model. The discrimination performance of the DCNN model was better than that of physicians in training (p < 0.05). CONCLUSIONS: Compared to the model trained by 2-chamber or 3-chamber images alone or 3D multiview, our 2.5D multiview model can combine the information of 2-chamber and 3-chamber more efficiently and obtain the highest diagnostic sensitivity. CLINICAL RELEVANCE STATEMENT: A deep convolutional neural network model that integrates 2-chamber and 3-chamber CMR images can identify LV paradoxical pulsation which correlates with LV thrombosis, heart failure, ventricular tachycardia after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. KEY POINTS: • The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.

Microwave ablation without subsequent lumpectomy versus breast-conserving surgery for early breast cancer: a propensity score matching study.

PURPOSE: To compare the efficacy of ultrasound-guided percutaneous microwave ablation (MWA) without subsequent lumpectomy and breast-conserving surgery (BCS) in patients with early breast cancer (BC). MATERIALS AND METHODS: This retrospective cohort study enrolled 106 patients with early BC (T0/1/2 N0/1 M0) treated by MWA (n = 21) or BCS (n = 85) from October 2014 to December 2020. Propensity score matching (PSM) was performed to balance the baseline characteristics between MWA and BCS groups. The tumor progression, overall survival (OS), disease-specific survival (DSS), complications, and cosmetic results were compared. RESULTS: After PSM, there were 21 patients with balanced baseline characteristics in each group. After a median follow-up of 43 months (range, 15-89 months), there was no significant difference in tumor progression (10% vs 2%, p = 0.18), OS (96% vs 99%, p = 0.36), DSS (100% vs 99%, p > 0.99), and complications (0% vs 19%, p = 0.58). The operation time of MWA was shorter (60 min vs 101 min, p < 0.001) than that of BCS. For the management of metastatic lymph nodes, five (5/21, 24%) patients with six metastatic nodes underwent ablation in the MWA group and three patients (3/21, 14%) with six metastatic nodes underwent axillary lymph node dissection in the BCS group. All the patients in the MWA group reported excellent cosmetic results, but 29% of BCS patients expressed dissatisfaction with breast asymmetry (10%) and scar formation (19%) (p < 0.001). CONCLUSION: This pilot study indicated that in selected early BC patients, microwave ablation without subsequent lumpectomy had comparable tumor control effect with breast-conserving surgery and better cosmetic results at an intermediate follow-up.HighlightsMWA without subsequent lumpectomy has a comparable interim survival effect and better cosmetic results as BCS in the treatment of selected early breast cancer.MWA has the potential to be a viable and promising therapeutic option for breast cancer patients reluctant or intolerant to surgery with the advantage of minimal invasion.

Type I IFNs repolarized a CD169+ macrophage population with anti-tumor potentials in hepatocellular carcinoma.

Macrophages constitute a major component in human hepatocellular carcinoma (HCC) and perform various functions to facilitate disease progression. Reprogramming or reconstituting the tumor surveillance phenotypes of macrophages represents an attractive immunotherapeutic strategy in cancer treatments. The current study identified CD169 as a potential target for macrophage repolarization since it signified a population of macrophages positively correlated with an activated immune signature and better prognosis of patients with HCC. In vitro experiments revealed that a low dose of type I interferon (IFN) could effectively reprogram human monocyte-derived macrophages to upregulate CD169 expression, and such induced CD169+ macrophages exhibited significantly enhanced phagocytotic and CD8+ T cell-activating capacities compared to controls. A low dose of IFNα also inhibited hepatoma growth in mice in vivo, presumably through polarizing the CD169+ macrophage population and enhancing CD8+ T cell activities. Notably, IFNα also induced substantial PD-L1 expression on macrophages in vivo, and thus blockade of PD-L1 could further increase the anti-tumor efficacy of IFNα in the treatment of HCC. We propose a low dose of IFNα in combination with a PD-L1 blocking agent as a potential anti-tumor therapeutic strategy via its effects on macrophage polarization.