Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis.

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF. Utilizing these differential gene expressions, we developed a risk prediction model that effectively distinguished healthy and IHF samples. Furthermore, we conducted a comprehensive evaluation of the association between cuproptosis and the immune microenvironment in IHF, encompassing infiltrated immunocytes, immune reaction gene-sets and human leukocyte antigen (HLA) genes. Moreover, we identified two different cuproptosis-mediated expression patterns in IHF and explored the immune characteristics associated with each pattern. In conclusion, this study elucidates the significant influence of cuproptosis on the immune microenvironment in ischemic heart failure (IHF), providing valuable insights for future mechanistic research exploring the association between cuproptosis and IHF.

Causal effect of lipoprotein(a) level on chronic kidney disease of European ancestry: a two-sample Mendelian randomization study.

INTRODUCTION: Chronic kidney disease is a growing health issue, and the options of prevention and therapy remain limited. Although a number of observational studies have linked higher Lp(a) [lipoprotein(a)] levels to the kidney impairment, the causal relationship remains to be determined. The purpose of this study was to assess the causal association between Lp(a) levels and CKD. METHODS: We selected eight single-nucleotide polymorphisms (SNPs) significantly associated with Lp(a) levels as instrumental variables. Genome-wide association study (GWAS) from CKDGen consortium yielded the summary data information for CKD. We designed the bidirectional two-sample Mendelian randomization (MR) analyses. The estimates were computed using inverse-variance weighted (IVW), simple median, weighted median, and maximum likelihood. MR-Egger regression was used to detect pleiotropy. RESULTS: Fixed-effect IVW analysis indicated that genetically predicted Lp(a) levels were associated with CKD significantly (odds ratio, 1.039; 95% CI, 1.009-1.069; p = 0.010). The SNPs showed no pleiotropy according to result of MR-Egger test. Results from sensitivity analyses were consistent. In the inverse MR analysis, random-effect IVW method showed CKD had no causal effect on the elevated Lp(a) (odds ratio, 1.154; 95% CI, 0.845-1.576; p = 0.367). CONCLUSION: In this bidirectional two-sample MR analysis, the causal deteriorating effects of genetically predicted plasma Lp(a) levels on the risk of CKD were identified. On the contrary, there is no evidence to support a causal effect of CKD on Lp(a) levels.

Aiming at early-stage vulnerable plaques: A nanoplatform with dual-mode imaging and lipid-inflammation integrated regulation for atherosclerotic theranostics.

The vulnerable plaques in atherosclerosis can cause severe outcome with great danger of acute cardiovascular events. Thus, timely diagnosis and treatment of vulnerable plaques in early stage can effectively benefit the clinical management of atherosclerosis. In this work, a targeting theranostic strategy on early-stage vulnerable plaques in atherosclerosis is realized by a LAID nanoplatform with X-CT and fluorescent dual-mode imaging and lipid-inflammation integrated regulation abilities. The iodinated contrast agents (ICA), phenylboronic acid modified astaxanthin and oxidized-dextran (oxDEX) jointly construct the nanoparticles loaded with the lipid-specific probe LFP. LAID indicates an active targeting to plaques along with the dual-responsive disassembly in oxidative stress and acidic microenvironment of atherosclerosis. The X-CT signals of ICA execute the location of early-stage plaques, while the LFP combines with lipid cores and realizes the recognition of vulnerable plaques. Meanwhile, the treatment based on astaxanthin is performed for restraining the progression of plaques. Transcriptome sequencing suggests that LAID can inhibit the lipid uptake and block NF-κB pathway, which synergistically demonstrates a lipid-inflammation integrated regulation to suppression the plaques growing. The in vivo investigations suggest that LAID delivers a favorable theranostics to the early-stage vulnerable plaques, which provides an impressive prospect for reducing the adverse prognosis of atherosclerosis.

Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive. PURPOSE: The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers. METHODS: Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (n = 120) and testing set (n = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score. RESULTS: In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1-3 by two expert radiation oncologists, respectively, meaning only minor edits needed. CONCLUSIONS: The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.

ROS-responsive & scavenging NO nanomedicine for vascular diseases treatment by inhibiting endoplasmic reticulum stress and improving NO bioavailability.

Vascular diseases seriously threaten human life and health. Exogenous delivery of nitric oxide (NO) represents an effective approach for maintaining vascular homeostasis during pathological events. However, the overproduction of reactive oxygen species (ROS) at vascular injury sites would react with NO to produce damaging peroxynitrite (ONOO-) species and limit the therapeutic effect of NO. Hence, we design a ROS-responsive NO nanomedicine (t-PBA&NO NP) with ROS scavenging ability to solve the dilemma of NO-based therapy. t-PBA&NO NP targets NO and anti-oxidant ethyl caffeate (ECA) to the injury sites via collagen IV homing peptide. The ROS-triggered ROS depletion and ECA release potently alleviate local oxidative stress via ROS scavenging, endoplasmic reticulum and mitochondrial regulation. It subsequently maximizes vascular modulation effects of NO, without production of harmful compounds, reactive nitrogen species (RNS). Therefore, it significantly increases competitiveness of human umbilical vein endothelial cells (HUVECs) over human aortic smooth muscle cells (HASMCs) both in vitro and in vivo. The strategy proved effective in inducing faster re-endothelialization, inhibiting neointimal formation and restoring vascular homeostasis. The synergy between ROS depletion and NO therapy served as a new inspiration for the treatment of cardiovascular diseases and other ROS-associated illnesses.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network.

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy. In addition, a deep learning-based segmentation model (AF-UNet) is designed to achieve accurate segmentation of rectal tumors. This model takes multi-parametric MRI images as input and effectively integrates the features from different multi-parametric MRI images by embedding the attention fusion module. Finally, three types of MRI images (T2, ADC, DWI) of 250 patients with rectal cancer were collected, with the tumor regions delineated by two oncologists. The experimental results show that the proposed method is superior to the most advanced image segmentation method with a Dice coefficient of [Formula: see text], which is also better than other multi-modal fusion methods. Framework of the AF-UNet. This model takes multi-modal MRI images as input, and integrates complementary information using attention mechanism and suppresses redundancy.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data.

PURPOSE: To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods. METHODS: Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models. RESULTS: Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2. CONCLUSION: There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.

Combined effects of physical activity and sedentary behavior on all-cause mortality in heart failure patients: A cohort study of national health and nutrition examination survey analysis.

Background: Physical activity and sedentary behavior are independently related to the risk of cardiovascular disease. Physical activity is recognized as having a protective effect, while being sedentary seems to be adverse. Nonetheless, the interactions between physical activity and sedentary behavior and the combined effect on the prognosis of heart failure patients remain unclear. Methods and results: This cohort study included 886 heart failure patients from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Physical activity and sedentary behavior were assessed by the NHANES questionnaires. The all-caused deaths of enrolled subjects were identified from National Death Index (NDI) database. During a median follow-up of 51 months, 321 (36.2%) deaths from any causes occurred. Multivariable Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence interval (CI) for the all-cause mortality in heart failure patients associated with physical activity and sedentary behavior. Physical activity was independently associated with lower mortality [HR = 0.51, 95% CI (0.38-0.68), p < 0.001] and sedentary behavior was associated with adverse prognosis [HR = 1.79, 95% CI (1.41-2.28), p < 0.001]. Kaplan-Meier survival curve showed that physical activity appeared to attenuate the negative consequences of SB, while sedentary behavior increased the all-cause mortality, particularly those without physical activity. Conclusion: Physical activity has a protective effect on HF patients' prognosis, particularly those with sedentary behavior. Sedentary behavior independently exhibited a negative association in populations without physical activity, while it does not increase mortality in those with moderate physical activity.

Association between electronic cigarettes use and whole blood cell among adults in the USA-a cross-sectional study of National Health and Nutrition Examination Survey analysis.

Electronic cigarettes (E-cigarettes) use is an emerging public health problem. Trying to assess the independent associations between E-cigarettes use and whole blood cell in a nationally representative sample of the US adults is very important for the smoking population. Using E-cigarettes data from NHANES (National Health and Nutrition Examination Survey) 2013-2018, 17,180 adults were included in this cross-sectional analysis. All participants were stratified into four different groups (non-smoke group N=10087, E-cigarettes group N=52, dual-smoke group N=249, cigarettes group N=6792) based on questions SMQ020 (smoked at least 100 cigarettes in life) and SMQ690H (used last 5 days E-cigarettes). Whole blood cell tests included white blood cell (WBC) with differentials, red blood cell (RBC) with characteristics, and platelet variables. With adjusted by age, gender, and race ethnicity, multivariate logistic regression analyses were used to assess independent associations between E-cigarettes group and other groups for different whole blood cell variables. A total of 17,180 participants were included in the study; 47.9% were males, with a mean age of 46.99 (±0.29). In WBC-related variables, non-smoke group had the lowest value in WBC counts (7.15±0.05), lymphocyte (2.15±0.02), and monocyte (0.57±0.01), among the four different groups. In RBC-related variables, non-smoke group had the lowest value in mean cell volume (MCV, 88.46±0.14, p<0.05) and mean cell hemoglobin (MCH, 29.73±0.06, p<0.05), among the four different groups. In adjusted analysis, WBC (OR = 0.97, 95% CI: 0.96-0.98, p<0.001), especially lymphocyte (OR = 0.97, 95% CI: 0.96-0.98, p<0.001) and monocyte (OR = 0.11, 95% CI: 0.02-0.66, p<0.001) of non-smoke group, showed negative significant effect for E-cigarettes group. Meanwhile, lower odds of MCV (OR = 0.91, 95% CI: 0.81-1.04, p<0.05) and MCH (OR = 0.81, 95% CI: 0.65-1.00, p<0.05) in non-smoke group were observed compared to E-cigarettes group. Conversely, for dual-smoke group and cigarette group, there was no significant results in all whole blood cell variables compared to E-cigarettes group. E-cigarettes use might be associated with a systemic response that could lead to an increase in WBC, especially lymphocytes and monocytes, in the US adults. Meanwhile, the properties of RBC might also be influenced simultaneously; MCV and MCH in E-cigarettes population were bigger than the non-smoke population.

Targeted delivery of liver X receptor agonist to inhibit neointimal hyperplasia by differentially regulating cell behaviors.

Restenosis induced by neointimal hyperplasia is one of the key reasons limiting the long-term success of cardiovascular interventional therapy. However, it remains a serious challenge to completely overcome restenosis because of the dilemma of simultaneously activating human umbilical vein endothelial cells (HUVECs) and inhibiting human aortic smooth muscle cells (HASMCs). Herein, we developed a targeted nanomedicine encapsulating the liver X receptor (LXR) agonist, T0901317, for differentially regulating the behaviors of HUVECs and HASMCs. The stimulatory effect on HUVEC proliferation/migration and the inhibitory effect on HASMC proliferation/migration were confirmed in vitro, respectively. In the co-culture system, the competitiveness of HUVECs over HASMCs was notably improved after being treated with T0901317-loaded liposomes. Compared to free T0901317 and non-targeted liposomes, the type IV collagen (Col-IV) targeted liposomes could accumulate in the vascular injured area more effectively and inhibit neointimal hyperplasia in a balloon-induced rat carotid artery injury model. Therefore, targeted delivery of LXR agonist might be a very promising therapeutic strategy for anti-restenosis therapy.

Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.

PURPOSE: To investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). METHODS: One hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: Among all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE. CONCLUSION: CE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.