Nanoparticles targeting OPN loaded with BY1 inhibits vascular restenosis by inducing FTH1-dependent ferroptosis in vascular smooth muscle cells.

Vascular restenosis following angioplasty continues to pose a significant challenge. The heterocyclic trioxirane compound [1, 3, 5-tris((oxiran-2-yl)methyl)-1, 3, 5-triazinane-2, 4, 6-trione (TGIC)], known for its anticancer activity, was utilized as the parent ring to conjugate with a non-steroidal anti-inflammatory drug, resulting in the creation of the spliced conjugated compound BY1. We found that BY1 induced ferroptosis in VSMCs as well as in neointima hyperplasia. Furthermore, ferroptosis inducers amplified BY1-induced cell death, while inhibitors mitigated it, indicating the contribution of ferroptosis to BY1-induced cell death. Additionally, we established that ferritin heavy chain1 (FTH1) played a pivotal role in BY1-induced ferroptosis, as evidenced by the fact that FTH1 overexpression abrogated BY1-induced ferroptosis, while FTH1 knockdown exacerbated it. Further study found that BY1 induced ferroptosis by enhancing the NCOA4-FTH1 interaction and increasing the amount of intracellular ferrous. We compared the effectiveness of various administration routes for BY1, including BY1-coated balloons, hydrogel-based BY1 delivery, and nanoparticles targeting OPN loaded with BY1 (TOP@MPDA@BY1) for targeting proliferated VSMCs, for prevention and treatment of the restenosis. Our results indicated that TOP@MPDA@BY1 was the most effective among the three administration routes, positioning BY1 as a highly promising candidate for the development of drug-eluting stents or treatments for restenosis.

MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.

PURPOSE: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. METHODS: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. RESULTS: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). CONCLUSION: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.

Optimization of anti-tachycardia pacing efficacy through scar-specific delivery and minimization of re-initiation: a virtual study on a cohort of infarcted porcine hearts.

AIMS: Anti-tachycardia pacing (ATP) is a reliable electrotherapy to painlessly terminate ventricular tachycardia (VT). However, ATP is often ineffective, particularly for fast VTs. The efficacy may be enhanced by optimized delivery closer to the re-entrant circuit driving the VT. This study aims to compare ATP efficacy for different delivery locations with respect to the re-entrant circuit, and further optimize ATP by minimizing failure through re-initiation. METHODS AND RESULTS: Seventy-three sustained VTs were induced in a cohort of seven infarcted porcine ventricular computational models, largely dominated by a single re-entrant pathway. The efficacy of burst ATP delivered from three locations proximal to the re-entrant circuit (septum) and three distal locations (lateral/posterior left ventricle) was compared. Re-initiation episodes were used to develop an algorithm utilizing correlations between successive sensed electrogram morphologies to automatically truncate ATP pulse delivery. Anti-tachycardia pacing was more efficacious at terminating slow compared with fast VTs (65 vs. 46%, P = 0.000039). A separate analysis of slow VTs showed that the efficacy was significantly higher when delivered from distal compared with proximal locations (distal 72%, proximal 59%), being reversed for fast VTs (distal 41%, proximal 51%). Application of our early termination detection algorithm (ETDA) accurately detected VT termination in 79% of re-initiated cases, improving the overall efficacy for proximal delivery with delivery inside the critical isthmus (CI) itself being overall most effective. CONCLUSION: Anti-tachycardia pacing delivery proximal to the re-entrant circuit is more effective at terminating fast VTs, but less so slow VTs, due to frequent re-initiation. Attenuating re-initiation, through ETDA, increases the efficacy of delivery within the CI for all VTs.

Regio-selective synthesis and activity research on 7-icaritin norcantharidin conjugates.

Due to complexity of tumor diseases and resistance of targeted drug, targeted drug usually cannot meet the needs of cancer treatment. Therefore, the conjugate constructed by two anticancer agents maybe a better solution for the tumor diseases. As natural anticancer agents, icaritin and norcantharidin are selected for the construction of conjugate. In the condition of EDCI/DMAP, icaritin is reacted with norcantharidin esters to give the desired 7-esters selectively in a moderate yield. MTT method was used to test the cytotoxicity and intensity on Hep G2 and MCF-7 in vitro. Some of the compounds (4a, 4i and 4j) show a better inhibition against Hep G2 and MCF-7 cell lines in vitro, and are deserved to be a potential drug candidate to develop in vivo.

Effect of Scalp Nerve Block Combined with Intercostal Nerve Block on the Quality of Recovery in Patients with Parkinson's Disease after Deep Brain Stimulation: Protocol for a Randomized Controlled Trial.

BACKGROUND: Parkinson's disease (PD) patients who receive deep brain stimulation (DBS) have a higher risk of postoperative pain, which will affect their postoperative quality of recovery (QoR). Scalp nerve block (SNB) and intercostal nerve block (ICNB) can alleviate postoperative pain, yet their effect on postoperative QoR in PD patients has proven to be unclear. Therefore, we have aimed to explore the effect of SNB paired with ICNB on postoperative QoR. METHODS: To explore the effect, we have designed a randomized controlled trial in which 88 patients with PD will be randomly assigned to either an SNB group or control group, receiving either SNB combined with ICNB or without before surgery. The primary outcome will be a 15-item QoR score at 24 h after surgery. The secondary outcomes will include: 15-item QoR scores at 72 h and 1 month after surgery; the numeric rating scale pain scores before discharge from the postanesthesia care unit (PACU) at 24 h, 72 h, and 1 month after surgery; rescue analgesics; nausea and vomiting 24 h after operation and remifentanil consumption during operation; emergence agitation; the duration of anesthesia and surgery; time to respiratory recovery, time to response, and time to extubation; the PACU length of stay; as well as adverse events. Proposed protocol and conclusion: Our findings will provide a novel method for the management of recovery and acute pain after DBS in PD patients. This research was registered at clinicaltrials.gov NCT05353764 on 19 April 2022.

Transcriptome sequencing analysis revealed the molecular mechanism of podoplanin neutralization inhibiting ischemia/reperfusion-induced microglial activation.

Background: Anti-podoplanin antibody (α-PDPN, clone 8.1.1) reduces microglia-mediated inflammation and decreases cerebral infarct volume in mice with stroke. However, the molecular mechanism by which this occurs is unknown. This study sought to systematically analyze the molecular mechanism of α-PDPN treatment on ischemia/reperfusion (I/R)-injured microglia. Methods: Microglia BV2 cells were pre-cultured with α-PDPN and then exposed to oxygen-glucose deprivation and reoxygenation (OGD-R) insult. The differentially expressed genes (DEGs) underwent a transcriptome sequencing technology analysis, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Quantitative real-time polymerase chain reaction (PCR) was performed to confirm the transcriptional expression of some DEGs. Results: The results showed that α-PDPN downregulated 338 genes and upregulated 340 genes in the BV2 cells. The GO items of the downregulated DEGs mainly involved biological processes, such as the response to the interferon (IFN), lipopolysaccharide-mediated signaling pathway, and the regulation of cell chemotaxis and migration. The upregulated molecular function mainly involved glucocorticoid-receptor binding. Further, the KEGG pathway analysis indicated that the enriched categories for the upregulated DEGs mainly involved the adenosine triphosphate (ATP) binding cassette transporters. However, the interleukin-17 signaling pathway, IFN signaling pathway, tumor necrosis factor signaling pathway, transforming growth factor beta (TGF-ꞵ) signaling pathway, nucleotide-binding and oligomerization domain (NOD)-like receptor signaling pathway, cytokine-cytokine receptor interaction, and chemokine signaling pathway were downregulated by the α-PDPN treatment. Conclusions: Numerous inflammation-related signaling pathways were regulated by the α-PDPN treatment in the OGD-R injured BV2 cells. This study provided further insights into the protective mechanism of α-PDPN treatment in ischemic stroke.

Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer.

Objective: To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). Methods: A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy. Results: A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900). Conclusions: The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.

One-step algorithm for fast-track localization and multi-category classification of histological subtypes in lung cancer.

BACKGROUNDS: Accumulated evidence has proven that computer-derived features from computed tomography (CT) through radiomics and deep learning technologies can identify extensive characteristics of pulmonary malignancies, such as nodules detection and malignant lesion discrimination. However, there are few studies on whether CT images can reflect histological subtypes of lung cancer through computer-derived features. METHODS: Contrast-enhanced CT images prior treatment from 417 patients diagnosed with small cell lung cancer (SCLC), lung adenocarcinoma (ADC), or lung squamous cell carcinoma (SCC) were collected. ITK-SNAP software was used by trained radiologists for the manual delineation of tumor volume. Patients of each category (SCLC, ADC, SCC) were then randomly split into training datasets and test datasets in an approximately ratio of 8:2. After image pre-processing and augmentation, 25,042 CT images from the training datasets were used to train our self-developed deep learning model for fast-tracking tumor lesions and classifying corresponding histological subtypes simultaneously. The performance of the network was evaluated by accuracy, F1-score and weighted F1-average using 1,921 testing images based on parameters generated during training. RESULTS: The prediction accuracy of SCLC, ADC, and SCC were 0.83, 0.75 and 0.67, respectively. The weighted F1-average was 0.75. ADC obtained the best F1-score of 0.78, which was outperformed SCLC (0.77) and SCC (0.66). The corresponding AUC values of SCLC, ADC, and SCC were 0.87, 0.84, and 0.76, respectively. Only 0.24 s were required to simultaneously achieve functions of tumor localization and histological classification on a thoracic CT image slice. The heat map visualization illustrated the extracted tumor features to classify subtypes of lung cancer by the proposed model. CONCLUSIONS: The newly developed multi-task algorithm provides a CNN-based DL approach in lung cancer for automatically fast-tracking tumor lesions and classifying corresponding histological subtypes in one-step.

Interleukin-35 exhibits protective effects in a rat model of hypoxic-ischemic encephalopathy through the inhibition of microglia-mediated inflammation.

Background: Hypoxic-ischemic encephalopathy (HIE) brain damage is related to inflammatory responses and oxidative stress. Interleukin (IL)-35 is an antioxidant and anti-inflammatory cytokine. Thus, the effect of IL-35 treatment on neonatal rats with hypoxic-ischemic brain injury was investigated. Methods: A total of 96 7-day-old Sprague Dawley rats were randomly divided into three groups: sham group, HIE group, and IL-35 group. After left common carotid occlusion and 2.5 h hypoxia (HI injury), IL-35 (20 µg/g) was intraperitoneally (i.p.) administered to the pups. In vitro, BV2 cells were treated with or without IL-35 6 h before oxygen-glucose deprivation (OGD) insult and the microglia culture medium (MCM) was co-cultured with b.End3 cerebral vascular endothelial cells. Microglial polarization and activation were assessed by real-time quantitative polymerase chain reaction (RT-qPCR), Western blot, and enzyme-linked immunosorbent assay (ELISA). Endothelial cell dysfunction was measured by cell counting kit-8 and Western blot assays. Results: Administration of IL-35 alleviated neurological deficiencies, decreased brain edema, ameliorated cerebral infarction, and limited M1 microglial polarization in HI-injured pups. Meanwhile, IL-35 decreased pro-inflammatory cytokines, tumor necrosis factor-α, IL-1ß, and reactive oxygen species generation in OGD-induced bEnd.3 cells. Furthermore, IL-35 treatment could reverse the vascular endothelial cell injury induced by microglial polarization. Finally, IL-35 markedly suppressed the activation of hypoxia-inducible factor-1α (HIF-1α) and the nuclear factor-κB (NF-κB) signaling pathway in vivo and in vitro. Conclusions: IL-35 relieved hypoxic-ischemic-induced brain injury and inhibited the inflammatory response by suppressing microglial polarization and activation. These results suggest that IL-35 might have potential applications for the treatment of HIE.