Highly sensitive and selective electrochemical biosensor using odorant-binding protein to detect aldehydes.

Recently, various biosensors based on odorant-binding proteins (OBPs) were developed for the detection of odorants and pheromones. However, important data gaps exist regarding the sensitive and selective detection of aldehydes with various carbon numbers. In this work, an OBP2a-based electrochemical impedance spectroscopy (EIS) biosensor was developed by immobilizing OBP2a on a gold interdigital electrode, and was characterized by EIS and atomic force microscopy. EIS responses showed the OBP2a-based biosensor was highly sensitive to citronellal, lily aldehyde, octanal, and decanal (detection limit of 10-11 mol/L), and was selective towards aldehydes compared with interfering odorants such as small-molecule alcohols and fatty acids (selectivity coefficients lower than 0.15). Moreover, the OBP2a-based biosensor exhibited high repeatability (relative standard deviation: 1.6%-9.1 %, n = 3 for each odorant), stability (NIC declined by 3.6 % on 6th day), and recovery (91.2%-96.6 % on three real samples). More specifically, the sensitivity of the biosensor to aldehydes was positively correlated to the molecular weight and the heterocyclic molecule structure of the odorants. These results proved the availability and the potential usage of the OBP2a-based EIS biosensor for the rapid and sensitive detection of aldehydes in aspects such as medical diagnostics, food and favor analysis, and environmental monitoring.

Targeting the regulation of iron homeostasis as a potential therapeutic strategy for nonalcoholic fatty liver disease.

With aging and the increasing incidence of obesity, nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. NAFLD mainly includes simple hepatic steatosis, nonalcoholic steatohepatitis (NASH), liver fibrosis and hepatocellular carcinoma (HCC). An imbalance in hepatic iron homeostasis is usually associated with the progression of NAFLD and induces iron overload, reactive oxygen species (ROS) production, and lipid peroxide accumulation, which leads to ferroptosis. Ferroptosis is a unique type of programmed cell death (PCD) that is characterized by iron dependence, ROS production and lipid peroxidation. The ferroptosis inhibition systems involved in NAFLD include the solute carrier family 7 member 11 (SLC7A11)/glutathione (GSH)/glutathione peroxidase 4 (GPX4) and ferroptosis suppressor protein 1 (FSP1)/coenzyme Q10 (CoQ10)/nicotinamide adenine dinucleotide phosphate (NADPH) regulatory axes. The main promotion system involved is the acyl-CoA synthetase long-chain family (ACSL4)/arachidonic lipoxygenase 15 (ALOX15) axis. In recent years, an increasing number of studies have focused on the multiple roles of iron homeostasis imbalance and ferroptosis in the progression of NAFLD. This review highlights the latest studies about iron homeostasis imbalance- and ferroptosis-associated NAFLD, mainly including the physiology and pathophysiology of hepatic iron metabolism, hepatic iron homeostasis imbalance during the development of NAFLD, and key regulatory molecules and roles of hepatic ferroptosis in NAFLD. This review aims to provide innovative therapeutic strategies for NAFLD.

Quantitative Assessment of Hemodynamics Associated With Embolization Degree in Brain Arteriovenous Malformations.

BACKGROUND AND OBJECTIVES: Grading systems, including the novel brain arteriovenous malformation endovascular grading scale (NBAVMES) and arteriovenous malformation embocure score (AVMES), predict embolization outcomes based on arteriovenous malformation (AVM) morphological features. The influence of hemodynamics on embolization outcomes remains unexplored. In this study, we investigated the relationship between hemodynamics and embolization outcomes. METHODS: We conducted a retrospective study of 99 consecutive patients who underwent transarterial embolization at our institution between 2012 and 2018. Hemodynamic features of AVMs were derived from pre-embolization digital subtraction angiography sequences using quantitative digital subtraction angiography. Multivariate logistic regression analysis was performed to determine the significant factors associated with embolization outcomes. RESULTS: Complete embolization (CE) was achieved in 17 (17.2%) patients, and near-complete embolization was achieved in 18 (18.2%) patients. A slower transnidal relative velocity (TRV, odds ratio [OR] = 0.71, P = .002) was significantly associated with CE. Moreover, higher stasis index of the drainage vein (OR = 16.53, P = .023), shorter transnidal time (OR = 0.15, P = .013), and slower TRV (OR = 0.9, P = .049) were significantly associated with complete or near-complete embolization (C/nCE). The area under the receiver operating characteristic curve for predicting CE was 0.87 for TRV, 0.72 for NBAVMES scores (ρ = 0.287, P = .004), and 0.76 for AVMES scores. The area under the receiver operating characteristic curve for predicting C/nCE was 0.77 for TRV, 0.61 for NBAVMES scores, and 0.75 for AVMES scores. Significant Spearman correlation was observed between TRV and NBAVMES scores and AVMES scores (ρ = 0.512, P < .001). CONCLUSION: Preoperative hemodynamic factors have the potential to predict the outcomes of AVM embolization. A higher stasis index of the drainage vein, slower TRV, and shorter transnidal time may indicate a moderate blood flow status or favorable AVM characteristics that can potentially facilitate embolization.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Evaluation of the Mechanism of Yishan Formula in Treating Breast Cancer Based on Network Pharmacology and Experimental Verification.

BACKGROUND: Yishan formula (YSF) has a significant effect on the treatment of breast cancer, which can improve the quality of life and prolong the survival of patients with breast cancer; however, its mechanism of action is unknown. OBJECTIVE: In this study, network pharmacology and molecular docking methods have been used to explore the potential pharmacological effects of the YSF, and the predicted targets have been validated by in vitro experiments. METHODS: Active components and targets of the YSF were obtained from the TCMSP and Swiss target prediction website. Four databases, namely GeneCards, OMIM, TTD, and DisGeNET, were used to search for disease targets. The Cytoscape v3.9.0 software was utilized to draw the network of drug-component-target and selected core targets. DAVID database was used to analyze the biological functions and pathways of key targets. Finally, molecular docking and in vitro experiments have been used to verify the hub genes. RESULTS: Through data collection from the database, 157 active components and 618 genes implicated in breast cancer were obtained and treated using the YSF. After screening, the main active components (kaempferol, quercetin, isorhamnetin, dinatin, luteolin, and tamarixetin) and key genes (AKT1, TP53, TNF, IL6, EGFR, SRC, VEGFA, STAT3, MAPK3, and JUN) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the YSF could affect the progression of breast cancer by regulating biological processes, such as signal transduction, cell proliferation and apoptosis, protein phosphorylation, as well as PI3K-Akt, Rap1, MAPK, FOXO, HIF-1, and other related signaling pathways. Molecular docking suggested that IL6 with isorhamnetin, MAPK3 with kaempferol, and EGFR with luteolin have strong binding energy. The experiment further verified that YSF can control the development of breast cancer by inhibiting the expression of the hub genes. CONCLUSION: This study showed that resistance to breast cancer may be achieved by the synergy of multiple active components, target genes, and signal pathways, which can provide new avenues for breast cancer-targeted therapy.