Abstract Epigenetic DNA methylations are early and frequently observed events in a diversity of diseases such as cancer. Despite the considerable clinical values for cancer liquid biopsy, quantitative analysis of DNA methylations remains a major challenge due to the lack of rapid, sensitive detection techniques. Here, an artificial intelligence‐assisted label‐free surface‐enhanced Raman spectroscopy (SERS) (iMeSERS) biosensor is reported for simultaneous quantification of C 5 ‐methylcytosine ( 5m C) level and methylation ratio in DNA samples. This method utilizes the plasmonic Pickering emulsions as the biosensing platform for label‐free SERS detection, formed upon the addition of a sub‐microliter DNA sample to the hydrophobic Au nanostar‐containing n ‐decane. Distinct spectral signatures of monophosphates of canonical deoxyribonucleotides (dNMPs) and the common methylation modification 5‐methyl‐2′‐deoxycytidine (d 5m CMP) are identified and distinguished by the iMeSERS biosensor. The deep learning algorithms trained with SERS signatures of dNMPs and d 5m CMP are then applied to the quantitative analysis of global DNA methylation. The exceptional capability of the deep learning‐driven approach is demonstrated for simultaneous quantification of the methylation ratio and level using a sub‐microliter volume of DNA samples. This work shows the power of label‐free SERS techniques combined with deep learning algorithms for quantitative analysis of epigenetic DNA modifications with great promises for clinical diagnosis.
Protein profiles of exosomes (EXOs) in clinical samples of cancer patients have become a promising diagnostic and therapeutic biomarker. However, simultaneous quantitative analysis of multiple exosomal proteins of interest remains challenging. To address the unmet need, we develop a paper-based surface-enhanced Raman spectroscopy (SERS)-vertical flow biosensor, named iREX (integrated Raman spectroscopic EXO) biosensor, for multiplexed quantitative profiling of exosomal proteins in clinical serum samples of patients. Utilizing this iREX biosensor, we are able to quantitatively profile MUC1, HER2 and CEA in EXO samples derived from various breast cancer cell subtypes. The results show discriminative expression profiles of the three exosomal proteins in these cell subtypes, which allows for accurate diagnosis and molecular subtyping of breast cancer. We further validate the clinical utility of the iREX biosensor for simultaneous quantitative analysis of MUC1, HER2 and CEA in patient's blood serums, thereby aiding in noninvasive breast cancer subtyping and longitudinal treatment monitoring. Our iREX biosensor integrating the SERS detection in a vertical flow diagnostic device offers great advantages of high sensitivity, molecular specificity, powerful multiplexing capability, and high diagnostic accuracy. We believe that the iREX biosensor could be a promising clinical tool for comprehensive analysis of exosomal proteins in clinical samples for personalized diagnosis and precise management of breast cancer.
N-functionalization of amines with CO2 and H2 is one of the most important processes to make use of CO2. Although noble metal-based catalysts with remarkable performance have been widely used in this process, developing efficient non-noble-metal-based catalysts remains a grand challenge. Herein, we report In2O3 nanocrystals with high density of grain boundaries (HGB-In2O3), which show excellent activity toward methylation of amines. Impressively, HGB-In2O3 achieved the optimal yield of 82.7% for N,N-dimethylaniline with a mass activity of 21.2 mmol·g−1h−1 in methylation of N-methylaniline, comparable to noble-metal-based catalysts. As a bonus, HGB-In2O3 held noticeable stability, remarkable selectivity, and comprehensive applicability. Further mechanistic studies revealed that the presence of high density of grain boundaries not only facilitated the adsorption and activation of CO2 to generate CH3OH as the intermediate but also enhanced the activation of N-H bond in amines, contributing to the attractive activity of HGB-In2O3 toward methylation of amines.
Aerobic oxidation by using molecular oxygen (O 2 ) as the oxidant is highly attractive, in which activating O 2 to reactive oxygen species (ROS) is a prerequisite. Although some progress has been achieved in regulating ROS by heterogeneous catalysts, the strategies to efficiently control ROS in aerobic oxidation are still urgently desired. Herein, grain boundaries (GBs) in metal oxides are discovered to be able to facilely regulate ROS. Impressively, MoO 3 nanocrystals with high density of GBs (MoO 3 ‐600) deliver a mass activity of 83 mmol g ‐1 h ‐1 in aerobic oxidation of benzyl alcohol, 7 and 8 times as high as that of MoO 3 nanoparticles without GBs and Pt/C, respectively. In addition, the selectivity of benzoic acid is 100% during whole reaction process over MoO 3 ‐600. Mechanistic studies reveal that the oxygen atoms at GBs in MoO 3 ‐600 are highly active to form ∙OH radicals with the generation of oxygen vacancies, while the oxygen vacancies are replenished by O 2 . The reaction path directly contributes to the excellent catalytic performance.
The standard of clinical care of most malignant solid cancers is surgery, followed by postsurgical adjuvant therapy, but microtumor lesions left behind after surgery and invisible distant metastases are the major reasons for treatment failure. Here, we report an integrated strategy combining surface-enhanced Raman spectroscopy (SERS) surgical navigation with postsurgical immunotherapy elicited by near-infrared II photothermal treatment and programmed death-1 antibody. The SERS surgical navigation is principally based on the multifunctional optical probes (namely, MATRA probes) integrating with T 1 -weighted magnetic resonance (MR) imaging, photothermal effect and Raman spectroscopic detection. We demonstrate in a 4T1 breast tumor mouse model that the pre-surgical MR/SERS dual-modal imaging is capable of providing comprehensive tumor information, and intraoperative SERS detection allows accurately delineating the tumor margins and guiding the surgical resection in real time with the least residual microscopic foci. We verify that the postsurgical immunotherapy effectively eradicates those local microtumor lesions and invisible distant metastases, greatly inhibiting the postsurgical cancer recurrence and distant metastasis.
Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.
The prevalence of fentanyl abuse raises global public health concerns with an unprecedented surge in overdose deaths. Rapid identification and quantification of fentanyl in biofluids is of paramount importance to combat fentanyl abuse for law enforcement agencies and promptly treat patients for medical professionals. Herein, a freestanding surface-enhanced Raman spectroscopy (SERS) biosensor with excellent condensing enrichment capability, termed FrEnSERS biosensor, is reported for quantitative label-free detection of trace fentanyl in biofluids. This biosensor comprises a reduced graphene oxide membrane decorated with high-density hydrophobic Au nanostars. A combination of the high SERS enhancement and the focusing effect for analyte enrichment of the hydrophobic surface accounts for the remarkable SERS performance of the FrEnSERS biosensor. We demonstrate that the FrEnSERS biosensor achieves the sensitive and quantitative detection of fentanyl in both serum and urine over a wide dynamic range spanning more than 4 orders of magnitude, with a limit of detection of 0.47 ng/mL for serum samples and 0.73 ng/mL for urine samples. Our biosensor is sensitive, cost-effective, and reliable for rapid quantitative analysis of fentanyl in biofluids with great promise for forensic analysis and clinical diagnosis.
Porous high entropy alloy CrMnFeCoNi exhibited remarkable catalytic activity and stability toward p-nitrophenol hydrogenation. The enhanced catalytic performance not only resulted from the high surface area, but also from exposed high-index facets with terraces.
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