Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy.

OBJECTIVES: This study was aimed to quantitatively assess hyperperfusion using arterial spin labeling (ASL) to predict hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. METHODS: This study enrolled 98 AIS patients with anterior circulation large vessel occlusion within 24 h of symptom onset. ASL was performed before mechanical endovascular therapy. On pre-treatment ASL maps, a region with relative cerebral blood flow (CBF) ≥ 1.4 was defined as an area of hyperperfusion. The maximum CBF (CBFmax) of hyperperfusion was calculated for each patient. A non-contrast CT scan was performed during the subacute phase for the evaluation of HT. Good clinical outcome was defined as a 90-day modified Rankin scale score of 0-2. RESULTS: The CBFmax of hyperperfusion (odds ratio, 1.023; 95% confidence interval [CI], 1.005-1.042; p = 0.012) was an independent risk factor for the status of HT. The CBFmax of hyperperfusion for HT showed an area under the curve of 0.735 (95% CI, 0.588-0.882) with optimal cutoff value, sensitivity, and specificity being 146.5 mL/100 g/min, 76.9%, and 69.6%, respectively. There was a statistically significant relationship between HT grades (from no HT to PH2) and CBFmax of hyperperfusion with a Spearman rank correlation of 0.446 (p = 0.001). In addition, low CBFmax of hyperperfusion were associated with good functional outcome (95% CI, 17.130-73.910; p = 0.002). CONCLUSIONS: High CBFmax of hyperperfusion was independently associated with subsequent HT and low CBFmax of hyperperfusion linked to good functional outcome. There was a positive correlation between HT grade and CBFmax. CLINICAL RELEVANCE STATEMENT: Arterial spin labeling is a noninvasive and contrast agent-independent technique, which is sensitive in detecting hyperperfusion. This study shows that the cerebral blood flow of hyperperfusion is associated with clinical prognosis, which will benefit more patients. KEY POINTS: • Quantitative assessment of hyperperfusion using pre-treatment arterial spin labeling to predict hemorrhagic transformation and prognosis in acute ischemic stroke patients. • The maximum cerebral blood flow of hyperperfusion was associated with hemorrhagic transformation and clinical prognosis and higher maximum cerebral blood flow of hyperperfusion was associated with higher grade hemorrhagic transformation. • The maximum cerebral blood flow of hyperperfusion can predict hemorrhagic transformation which enables timely intervention to prevent parenchymal hematoma.

Pharmacological inhibition of the cysteine protease cathepsin C improves graft function after heart transplantation in rats.

BACKGROUND: Heart transplantation (HTX) is the standard treatment for end-stage heart failure. However, reperfusion following an ischemic period can contribute to myocardial injury. Neutrophil infiltration, along with the subsequent release of tissue-degrading neutrophil elastase (NE)-related serine proteases and oxygen-derived radicals, is associated with adverse graft outcomes. The inhibition of cathepsin C (CatC) has been shown to block NE-related protease activation. We hypothesized that the CatC inhibitor BI-9740 improves graft function after HTX. METHODS: In a rat model of HTX, the recipient Lewis rats were orally administered with either a placebo (n = 12) or BI-9740 (n = 11, 20 mg/kg) once daily for 12 days. Donor hearts from untreated Lewis rats were explanted, preserved in a cardioplegic solution, and subsequently heterotopically implanted. In vivo left-ventricular (LV) graft function was assessed after 1 h of reperfusion. The proteolytic activity of neutrophil serine proteases was determined in bone marrow lysates from BI-9740-treated and control rats. Additionally, myocardial morphological changes were examined, and heart samples underwent immunohistochemistry and western blot analysis. RESULTS: The NE-related proteolytic activity in bone marrow cell lysates was markedly decreased in the BI-9740-treated rats compared to those of the placebo group. Histopathological lesions, elevated CatC and myeloperoxidase-positive cell infiltration, and nitrotyrosine immunoreactivity with an increased number of poly(ADP-ribose) polymerase (PARP)-1-positive cells were lowered in the hearts of animals treated with BI-9740 compared to placebo groups. Regarding the functional parameters of the implanted graft, improvements were observed in both systolic function (LV systolic pressure 110 ± 6 vs 74 ± 6 mmHg; dP/dtmax 2782 ± 149 vs 2076 ± 167 mmHg/s, LV developed pressure, at an intraventricular volume of 200 µl, p < 0.05) and diastolic function in the hearts of BI-9740 treated animals compared with those receiving the only placebo. Furthermore, the administration of BI-9740 resulted in a shorter graft re-beating time compared to the placebo group. However, this study did not provide evidence of DNA fragmentation, the generation of both superoxide anions and hydrogen peroxide, correlating with the absence of protein alterations related to apoptosis, as evidenced by western blot in grafts after HTX. CONCLUSIONS: We provided experimental evidence that pharmacological inhibition of CatC improves graft function following HTX in rats.

Synthetic-to-real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion-weighted imaging.

BACKGROUND: Intravoxel incoherent motion (IVIM) is a type of diffusion-weighted imaging (DWI), and IVIM model parameters (water molecule diffusion rate Dt , pseudo-diffusion coefficient Dp , and tissue perfusion fraction Fp ) have been widely used in the diagnosis and characterization of malignant lesions. PURPOSE: This study proposes a deep-learning model with synthetic-to-real domain adaptation to fit the IVIM model parameters of DWI. METHODS: Ninety-eight consecutive patients diagnosed with hepatocellular carcinoma between January 2017 and September 2020 were included in the study, and routine IVIM-DWI serial examinations were performed using a 3.0 T magnetic resonance imaging system in preoperative MR imaging. The proposed method is mainly composed of two modules: a convolutional neural network-based IVIM model fitting network to map b-value images to the IVIM parameter maps and a domain discriminator to improve the accuracy of the IVIM parameter maps in the real data. The proposed method was compared with previously reported fitting methods, including the nonlinear least squares (NLSs), IVIM-NEToptim , and self-supervised U-network methods. The IVIM parameter-fitting performance was assessed by measuring the DWI reconstruction performance and testing the robustness of each method against noise using noise-corrupted data. RESULTS: The DWI reconstruction performance demonstrates that the proposed method has better reconstruction accuracy for DWI with a low signal-to-noise ratio, which implies that the proposed method improves the fitting accuracy of the IVIM parameters. Noise-corrupt experiments show that the proposed method is more robust against noise-corrupted signals. With the proposed method, no outliers were found in Dt , and outliers were reduced for Fp in the abnormal regions (proposed method: 1.85%; NLS: 5.90%; IVIM-NEToptim : 6.61%; and self-U-net: 25.36%). Moreover, experiments show that the proposed method has a more stable parameter estimation performance than the existing methods in the absence of real data. CONCLUSIONS: IVIM parameters can be estimated using a synthetic-to-real domain-adaptation framework with deep learning, and the proposed method outperforms previously reported methods.

IVIM using convolutional neural networks predicts microvascular invasion in HCC.

OBJECTIVES: The study aimed to investigate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging for prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using convolutional neural networks (CNNs). METHODS: This retrospective study included 114 patients with pathologically confirmed HCC from December 2014 to August 2021. All patients underwent MRI examination including IVIM sequence with 9 b-values preoperatively. First, 9 b-value images were superimposed in the channel dimension, and a b-value volume with a shape of 32 × 32 × 9 dimension was obtained. Secondly, an image resampling method was performed for data augmentation to generate more samples for training. Finally, deep features to predict MVI in HCC were directly derived from a b-value volume based on the CNN. Moreover, a deep learning model based on parameter maps and a fusion model combined with deep features of IVIM, clinical characteristics, and IVIM parameters were also constructed. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance for MVI prediction in HCC. RESULTS: Deep features directly extracted from IVIM-DWI (0.810 (range 0.760, 0.829)) using CNN yielded better performance for prediction of MVI than those from IVIM parameter maps (0.590 (range 0.555, 0.643)). Furthermore, the performance of the fusion model combined with deep features of IVIM-DWI, clinical features (α-fetoprotein (AFP) level and tumor size), and apparent diffusion coefficient (ADC) (0.829 (range 0.776, 0.848)) was slightly improved. CONCLUSIONS: Deep learning with CNN based on IVIM-DWI can be conducive to preoperative prediction of MVI in patients with HCC. KEY POINTS: • Deep learning assessment of IVIM data for prediction of MVI in HCC can overcome the unstable and low performance of IVIM parameters. • Deep learning model based on IVIM performs better than parameter values, clinical features, and deep learning model based on parameter maps. • The fusion model combined with deep features of IVIM, clinical characteristics, and ADC yields better performance for prediction of MVI than the model only based on IVIM.

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging.

The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with differentb-values has great potential as a tool for detecting, diagnosing, staging, and monitoring disease progression or the response to treatment. The current clinical tumour characterisation using IVIM-DWI is based on the parameter values derived from the IVIM model. On the one hand, the calculation accuracy of such parameter values is susceptible to deviations due to noise and motion; on the other hand, the performance of the parameter values is rather limited with respect to tumour characterisation. In this article, we propose a deep learning approach to directly extract spatiotemporal features from a series ofb-value images of IVIM-DWI using a deep learning network for lesion characterisation. Specifically, we introduce an attention mechanism to select dominant features from specificb-values, channels, and spatial areas of the multipleb-value images for better lesion characterisation. The experimental results for clinical hepatocellular carcinoma (HCC) when using IVIM-DWI demonstrate the superiority of the proposed deep learning model for predicting the microvascular invasion (MVI) of HCC. In addition, the ablation study reflects the effectiveness of the attention mechanism for improving MVI prediction. We believe that the proposed model may be a useful tool for the lesion characterisation of IVIM-DWI in clinical practice.

Estimated glomerular filtration rate and postoperative mortality in patients undergoing non-cardiac and non-neuron surgery: a single-center retrospective study.

BACKGROUND: There is limited evidence to clarify the specific relationship between preoperative estimated glomerular filtration rate (preop-eGFR) and postoperative 30-day mortality in Asian patients undergoing non-cardiac and non-neuron surgery. We aimed to investigate details of this relationship. METHODS: We reanalyzed a retrospective analysis of the clinical records of 90,785 surgical patients at the Singapore General Hospital from January 1, 2012 to October 31, 2016. The main outcome was postoperative 30-day mortality. RESULTS: The average age of these recruited patients was 53.96 ± 16.88 years, of which approximately 51.64% were female. The mean of preop-eGFR distribution was 84.45 ± 38.56 mL/min/1.73 m2. Multivariate logistic regression analysis indicated that preop-eGFR was independently associated with 30-day mortality (adjusted odds ratio: 0.992; 95% confidence interval [CI] 0.990-0.995; P < 0.001). A U-shaped relationship was detected between preop-eGFR and 30-day mortality with an inflection point of 98.688 (P for log likelihood ratio test < 0.001). The effect sizes and confidence intervals on the right and left sides of the inflection point were 1.013 (1.007 to 1.019) [P < 0.0001] and 0.984 (0.981 to 0.987) [P < 0.0001], respectively. Preoperative comorbidities such as congestive heart failure (CHF), type 1 diabetes, ischemic heart disease (IHD), and anemia were associated with the odds ratio of preop-eGFR to 30-day mortality (interaction P < 0.05). DISCUSSION: The relationship between preop-eGFR and 30-day mortality is U-shaped. The recommended preop-eGFR at which the rate of the 30-day mortality was lowest was 98.688 mL/min/1.73 m2.

Anemia and perioperative mortality in non-cardiac surgery patients: a secondary analysis based on a single-center retrospective study.

BACKGROUND: Evidence regarding the relationship between anemia and perioperative prognosis is controversial. The study was conducted to highlight the specific relationship between anemia and perioperative mortality in non-cardiac surgery patients over 18 years of age. METHODS: This study was a retrospective analysis of the electronic medical records of 90,784 patients at the Singapore General Hospital from January 1, 2012 to October 31, 2016. Multivariate regression, propensity score analysis, doubly robust estimation, and an inverse probability-weighting model was used to ensure the robustness of our findings. RESULTS: We identified 85,989 patients, of whom75, 163 had none or mild anemia (Hemoglobin>90g/L) and 10,826 had moderate or severe anemia (Hemoglobin≤90g/L). 8,857 patients in each study exposure group had similar propensity scores and were included in the analyses. In the doubly robust model, postoperative 30-day mortality rate was increased by 0.51% (n = 219) in moderate or severe anemia group (Odds Ratio, 1.510; 95% Confidence Interval (CI), 1.049 to 2.174) compared with none or mild anemia group (2.47% vs.1.22%, P<0.001). Moderate or severe anemia was also associated with increased postoperative blood transfusion rates (OR, 5.608; 95% CI, 4.026 to 7.811, P < 0.001). There was no statistical difference in Intensive Care Unit (ICU) admission rate among different anemia groups within 30 days after surgery (P=0.104). DISCUSSION: In patients undergoing non-cardiac surgery over 18 years old, moderate or severe preoperative anemia would increase the occurrence of postoperative blood transfusion and the risk of death, rather than ICU admission within 30 days after surgery.

Progress in the application of organoids to breast cancer research.

Breast cancer is the most common cancer diagnosed in women. Breast cancer research is currently based mainly on animal models and traditional cell culture. However, the inherent species gap between humans and animals, as well as differences in organization between organs and cells, limits research advances. The breast cancer organoid can reproduce many of the key features of human breast cancer, thereby providing a new platform for investigating the mechanisms underlying the development, progression, metastasis and drug resistance of breast cancer. The application of organoid technology can also promote drug discovery and the design of individualized treatment strategies. Here, we discuss the latest advances in the use of organoid technology for breast cancer research.

The relationship between anesthetic technique and thirty-day mortality in patients undergoing noncardiac- and nonneurosurgery: A retrospective, propensity score-matched cohort study.

BACKGROUND: Currently, 310 million patients undergo surgery every year worldwide, and there is still controversy over which anesthetic technique to choose for a considerable of surgeries.This study evaluates the association of the anesthetic technique with thirty-day mortality after noncardiac- and nonneurosurgery. METHODS: Electronic medical records of 90,785 patients who underwent non-cardiac- and nonneurosurgery at the *** General Hospital from January 1, 2012 to October 31, 2016, were subject to secondary retrospective analysis. The principal exposure was regional versus general anesthesia. Outcome measures were death, intensive care unit (ICU) admission and blood transfusion requirement within 30 days after surgery. Propensity-score matching was used to assemble a cohort of patients with similar baseline characteristics. RESULTS: We identified 90,785 patients, of whom 76,442 received regional anesthesia and 14,343 received general anesthesia. A total of 11,351 patients in the general anesthesia group had propensity scores similar to those of patients who received regional anesthesia and were included in the analyses. In the propensity-score matched cohort, the postoperative 30-day mortality rate was 0.75% (n = 85) in the regional anesthesia group (Odds Ratio, 0.567; 95% CI, 0.434 to 0.741; P = 0.00003) compared with 1.31% (n = 149) in the general anesthesia group. Regional anesthesia was also associated with a reduced rate of ICU admission compared with that of patients who received general anesthesia (0.44% vs. 2.68%; OR, 0.161; 95% CI, 0.119 to 0.217, P < 0.00001). There was a nonsignificant relationship between the anesthetic technique and postoperative blood transfusion (P = 0.082). CONCLUSIONS: The results of this observational, propensity score-matched cohort study suggest a significant association between regional anesthesia and low thirty-day mortality and a worse postoperative prognosis in patients who underwent noncardiac- and nonneurosurgery, which provides information for anesthetic technique decision making in the clinical setting.

Identification of LCN1 as a Potential Biomarker for Breast Cancer by Bioinformatic Analysis.

The biological functions of lipocalin-1 (LCN1) are involved in innate immune responses and act as a physiological scavenger of potentially harmful lipophilic molecules. However, the relevance of LCN1 with cancer is rarely concerned currently. The aim of this study is to address the relevance of LCN1 with BRCA by bioinformatics. In this study, we found that the expressions of LCN1 increased significantly in various cancerous tissues, including BRCA, compared with their adjacent normal tissues through the TIMER database. Furthermore, UALCAN database analysis showed that the expression of LCN1 increased gradually from stage 1 to stage 4 and was upregulated in BRCA patients with different races and subtypes compared with that in the normal. In addition, those patients with perimenopause and postmenopause status displayed higher LCN1 expression. Importantly, LCN1 genetic alterations, including copy number amplification, deep deletion, and missense mutation, could be found, and the alteration frequency showed difference in various invasive BRCA through cBioPortal database. Moreover, a positive correlation between LCN1 somatic copy number alterations and immune cell enrichments was revealed in basal like BRCA by GISTIC 2.0. Finally, analysis on prognostic value of LCN1 by Kaplan-Meier plotter showed that low LCN1 expression correlated with poor prognosis for relapse-free survival in all types of BRCA, overall survival in luminal B BRCA, distant metastasis free survival in human epithelial growth factor receptor-2 (HER2) positive BRCA, and postprogression survival (PPS) in luminal A BRCA. But high LCN1 expression also displayed poor prognosis for PPS in HER2 positive BRCA. The results together verified the significance of LCN1 in BRCA, suggesting that it may be a potential biomarker for BRCA diagnosis.

Potential Role of lncRNA H19 as a Cancer Biomarker in Human Cancers Detection and Diagnosis: A Pooled Analysis Based on 1585 Subjects.

Long noncoding RNAs (lncRNAs) have been reported to serve as diagnostic and prognostic biomarkers of cancers, which play vital roles in tumorigenesis and tumor progression. Several studies have been performed to explore diagnostic value of lncRNA H19 in cancer detection and diagnosis. However, there are still inconsistent results in diagnostic accuracy and reliability in individual studies. Therefore, the present study was performed to summarize the overall diagnostic performance of lncRNA H19 in cancer detection and diagnosis. A total of eight studies with 770 cases and 815 controls were included in this pooled analysis. The pooled diagnostic results were as follows: sensitivity, 0.69 (95%CI=0.62-0.76), specificity, 0.79 (95% CI=0.70-0.86), positive likelihood ratio (PLR), 3.31 (95%CI=2.29-4.78), negative likelihood (NLR), 0.39 (95%CI=0.31-0.49), diagnostic odds ratio (DOR), 8.53 (95%CI=4.99-14.60), and area under the curve (AUC), 0.79 (95%CI=0.76-0.83). Deeks' funnel plot asymmetry test (P=0.13) suggested no potential publication bias. Our results indicated that lncRNA H19 had a relatively moderate accuracy in cancer detection and diagnosis. Further comprehensive prospective studies with large sample sizes are urgently required to validate our findings.

MicroRNA-195 as a diagnostic biomarker in human cancer detection: A meta-analysis.

MicroRNAs (miRNAs/miRs) show great promise as novel cancer biomarkers. Several studies have revealed an association between abnormal miRNA expression and the risk of various cancer types. However, the diagnostic accuracy and reliability of miRNAs remains unclear. The present meta-analysis was performed to summarize the overall diagnostic performance of miR-195 for cancer. The PubMed, Cochrane Library, Wanfang and China National Knowledge Infrastructure databases were searched for associated literature published until December 10, 2017. Eligible studies were selected using multiple search strategies based on study selection criteria. Measures, including sensitivity and specificity, of the performance of miR-195 as a cancer diagnostic tool were pooled using bivariate meta-analysis models. All analyses were performed using Stata 14.0. The pooled analysis included 8 studies comprising 735 cases and 547 controls. The pooled diagnostic results calculated from all studies were as follows: Sensitivity, 0.79 [95% confidence interval (CI), 0.69-0.87]; specificity, 0.84 (95% CI, 0.68-0.93); positive likelihood ratio, 4.9 (95% CI, 2.50-9.50); negative likelihood ratio, 0.25 (95% CI, 0.18-0.35); diagnostic odds ratio, 20 (95% CI, 10.00-38.00); and area under the curve, 0.87 (95% CI, 0.84-0.90). Deeks' funnel plot asymmetry test suggested no potential publication bias (P=0.53). The present meta-analysis indicated that miR-195 could be a reliable non-invasive biomarker for the diagnosis of cancer. Further large-scale prospective studies are necessary to confirm the present findings and the clinical value of miR-195 for future diagnostics.

High expression of enhancer RNA MARC1 or its activation by DHT is associated with the malignant behavior in bladder cancer.

Enhancer RNAs (eRNAs), a subclass of noncoding RNA from enhancers, have biological functions in gene expression. However, their potential role in bladder cancer (BCa) remains largely unknown. The present study investigated the functional role of androgen-associated androgen receptor (AR) mediated-eRNA MARC1 (eMARC1) in BCa progression. Cell proliferation, migration, and apoptosis of BCa cell lines (5637 and T24) with different eMARC1 expression levels or treated with 5α-dehydrotestosterone (DHT) were investigated. In the current study, we discovered that eMARC1 was highly expressed in BCa tissues and cell lines, and eMARC1 overexpression promoted the progression of BCa cells, while knockdown of eMARC1 suppressed tumorigenesis. DHT treatment significantly elevated eMARC1 expression levels, which also facilitated cell proliferation, motility, and inhibited cell apoptosis. We further found that eMARC1 silencing impaired the androgenic effect of DHT in BCa cells. These results suggested that eMARC1 exerted its effects on BCa cell progression, and DHT promoted bladder cancer progression by activating eMARC1.

rs11614913 polymorphism in miRNA-196a2 and cancer risk: an updated meta-analysis.

Several epidemiological studies have reported that polymorphisms in microRNA-196a2 (miR-196a2) were associated with various cancers. However, the results remained unverified and were inconsistent in different cancers. Therefore, we carried out an updated meta-analysis to elaborate the effects of rs11614913 polymorphism on cancer susceptibility. A total of 84 articles with 35,802 cases and 41,541 controls were included to evaluate the association between the miR-196a2 rs11614913 and cancer risk by pooled odds ratios (ORs) and 95% confidence intervals (CIs). The results showed that miR-196a2 rs11614913 polymorphism is associated with cancer susceptibility, especially in lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734-0.961; recessive model, OR =0.858, 95% CI =0.771-0.955), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800-0.998; homozygote comparison, OR =0.900, 95% CI =0.813-0.997; recessive model, OR =0.800, 95% CI =0.678-0.944), and head and neck cancer (allelic contrast, OR =1.076, 95% CI =1.006-1.152; homozygote comparison, OR =1.214, 95% CI =1.043-1.413). In addition, significant association was found among Asian populations (allele model, OR =0.847, 95% CI =0.899-0.997, P=0.038; homozygote model, OR =0.878, 95% CI =0.788-0.977, P=0.017; recessive model, OR =0.895, 95% CI =0.824-0.972, P=0.008) but not in Caucasians. The updated meta-analysis confirmed the previous results that miR-196a2 rs11614913 polymorphism may serve as a risk factor for patients with cancers.