Nanomaterials possess unique properties, including excellent electrochemical properties, high surface area, and tunable electric conductivity, making them attractive for sensing applications, especially for electrochemical sensing. Notably, the compatibility of nanomaterials with other techniques opens up opportunities for electrochemical sensors in various biomedical applications. Herein, the enhancing mechanisms and preparation protocols of electrochemical sensors, regarding the material choices, are introduced. The integration of nanomaterial‐based electrochemical sensors with other techniques toward precise health management and controlled drug release is further highlighted. It is concluded with perspectives on the future directions for this topic. Future research directions of nanomaterial‐based electrochemical sensors and the challenges faced by commercialized implementation of the sensors are presented, paving the way for the upcoming era of accurate biosensing toward both academic and industrial use.

Cardiovascular diseases lead to 31.5% of deaths globally, and particularly myocardial infarction (MI) results in 7.4 million deaths per year. Diagnosis of MI and monitoring for prognostic use are critical for clinical management and biomedical research, which require advanced tools with accuracy and speed. Herein, we developed a plasmonic gold nano-island (pGold) chip assay for diagnosis and monitoring of MI. On-chip microarray analysis of serum biomarkers (e.g., cardiac troponin I) afforded up to 130-fold enhancement of near-infrared fluorescence for ultra-sensitive and quantitative detection within controlled periods, using 10 μL of serum only. The pGold chip assay achieved MI diagnostic sensitivity of 100% and specificity of 95.54%, superior to the standard chemiluminescence immunoassay in cardiovascular clinics. Further, we monitored biomarker concentrations regarding percutaneous coronary intervention for prognostic purpose. Our work demonstrated a designed approach using plasmonic materials for enhanced diagnosis and monitoring for prognostic use towards point-of-care testing.

Background: Metabolic disorder increases the risk of abdominal aortic aneurysm (AAA). NRs (nuclear receptors) have been increasingly recognized as important regulators of cell metabolism. However, the role of NRs in AAA development remains largely unknown. Methods: We analyzed the expression profile of the NR superfamily in AAA tissues and identified NR1D1 (NR subfamily 1 group D member 1) as the most highly upregulated NR in AAA tissues. To examine the role of NR1D1 in AAA formation, we used vascular smooth muscle cell (VSMC)–specific, endothelial cell–specific, and myeloid cell–specific conditional Nr1d1 knockout mice in both AngII (angiotensin II)– and CaPO ₄ -induced AAA models. Results: Nr1d1 gene expression exhibited the highest fold change among all 49 NRs in AAA tissues, and NR1D1 protein was upregulated in both human and murine VSMCs from AAA tissues. The knockout of Nr1d1 in VSMCs but not endothelial cells and myeloid cells inhibited AAA formation in both AngII- and CaPO ₄ -induced AAA models. Mechanistic studies identified ACO2 (aconitase-2), a key enzyme of the mitochondrial tricarboxylic acid cycle, as a direct target trans-repressed by NR1D1 that mediated the regulatory effects of NR1D1 on mitochondrial metabolism. NR1D1 deficiency restored the ACO2 dysregulation and mitochondrial dysfunction at the early stage of AngII infusion before AAA formation. Supplementation with αKG (α-ketoglutarate, a downstream metabolite of ACO2) was beneficial in preventing and treating AAA in mice in a manner that required NR1D1 in VSMCs. Conclusions: Our data define a previously unrecognized role of nuclear receptor NR1D1 in AAA pathogenesis and an undescribed NR1D1-ACO2 axis involved in regulating mitochondrial metabolism in VSMCs. It is important that our findings suggest αKG supplementation as an effective therapeutic approach for AAA treatment.

Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.