A fluorescence-electrochemical dual-mode aptasensor based on novel DNA-dependent PBNFs@PtPd for highly selective and sensitive detection of procymidone through hybridization chain reaction.

Herein, a study for the first application of a hybridization chain reaction, a 1,8-naphthalimides-DNA (NDs) intercalator, and DNA-dependent Prussian blue nanoflowers@PtPd materials (PBNFs@PtPd) in the development of a fluorescence-electrochemical (FL-EC) aptasensor. This construction establishes an efficient sensing platform for the detection of procymidone (PCM). In the context of the described experiment, dual-mode detection is achieved through the generation of FL signals by an aptamer labeled with a Cy5 moiety and the formation of DPV signals by the modification of a thionine-appended 1,8-naphthalimide (Thi-NDs). In the presence of PCM, specific recognition occurs, followed by the utilization of magnetic separation technology to release DNA1 (S1) and aptamer-Cy5 (Apt-Cy5), subsequently introducing them onto both fluorescence and EC platforms. The presence of S1 effectively activates hybridization chain reaction (HCR) for the electrode surface, thereby significantly increasing the binding sites for Thi-NDs and consequently greatly amplifying the response signal of differential pulse voltammetry (DPV). The developed FL-EC dual-mode sensing platform demonstrates high sensitivity in the detection of PCM, with the detection limits of 0.173 µg·ml-1 (within the detection range of 500 pg·ml-1 to 500 ng·ml-1) and 0.074 ng·ml-1 (within the detection range of 100 pg·ml-1 to 100 ng·ml-1), respectively. The designed dual-mode sensor exhibits notable characteristics, including high selectivity, reproducibility, synergy, and reliable monitoring/capability for PCM in real samples.

"Two-in-One" PtPdCu Trimetallic Multifunctional Nanoparticles-Mediated Dual-Signal-Integrated Aptasensor for Ultradetection of Enrofloxacin.

Balancing the accuracy and simplicity of aptasensors is a challenge in their construction. This study addresses this issue by leveraging the remarkable loading capacity and peroxidase-like catalytic activity of PtPdCu trimetallic nanoparticles, which reduces the reliance on precious metals. A dual-signal readout aptasensor for enrofloxacin (ENR) detection is designed, incorporating DNA dynamic network cascade reactions to further amplify the output signal. Exploiting the strong loading capacity of PtPdCu nanoparticles, they are self-assembled with thionine (Thi) to form a signal label capable of generating signals in two independent modes. The label exhibits excellent enzyme-like catalytic activity and enhances electron transfer capabilities. Differential pulse voltammetry (DPV) and square-wave voltammetry (SWV) are employed to independently read signals from the oxidation-reduction reaction of Thi and the catalytic oxidation of hydroquinone (HQ) to benzoquinone (BQ) by H2O2. The introduced DNA dynamic network cascade reaction modularizes sample processing and electrode surface signal generation, avoiding electrode contamination and efficiently increasing the output of the catalyzed hairpin assembly (CHA) cycle. Under optimized conditions, the developed aptasensor demonstrates detection limits of 0.112 (DPV mode) and 0.0203 pg/mL (SWV mode). Additionally, the sensor successfully detected enrofloxacin in real samples, expanding avenues for designing dual-mode signal amplification strategies.

Compression-Shear Specimen Stress-State Response and Distribution Characteristics with Wide Stress Triaxiality.

Conventional methods for studying the plastic behavior of materials involve uniaxial tension and uniaxial compression. However, in the metal rolling process, the deformation zone undergoes a complex loading of multidirectional compression and shear. Characterizing the corresponding plastic evolution process online poses challenges, and the existing specimen structures struggle to accurately replicate the deformation-induced loading characteristics. In this study, we aimed to design a compression-shear composite loading specimen that closely mimics the actual processing conditions. The goal was to investigate how the specimen structure influences the stress-strain response in the deformation zone. Using commercial finite element software, a compression-shear composite loading specimen was meticulously designed. Five 304 stainless steel specimens underwent uniaxial compressive loading, with variation angles between the preset notch angle (PNA) of the specimen and compression direction. We employed digital image correlation methods to capture the impact of the PNA on the strain field during compression. Additionally, we aimed to elucidate the plastic response resulting from the stress state of the specimen, particularly in relation to specimen fracture and microstructural evolution.

Serine/threonine-protein kinase D2-mediated phosphorylation of DSG2 threonine 730 promotes esophageal squamous cell carcinoma progression.

Desmoglein-2 (DSG2) is a transmembrane glycoprotein belonging to the desmosomal cadherin family, which mediates cell-cell junctions; regulates cell proliferation, migration, and invasion; and promotes tumor development and metastasis. We previously showed serum DSG2 to be a potential biomarker for the diagnosis of esophageal squamous cell carcinoma (ESCC), although the significance and underlying molecular mechanisms were not identified. Here, we found that DSG2 was increased in ESCC tissues compared with adjacent tissues. In addition, we demonstrated that DSG2 promoted ESCC cell migration and invasion. Furthermore, using interactome analysis, we identified serine/threonine-protein kinase D2 (PRKD2) as a novel DSG2 kinase that mediates the phosphorylation of DSG2 at threonine 730 (T730). Functionally, DSG2 promoted ESCC cell migration and invasion dependent on DSG2-T730 phosphorylation. Mechanistically, DSG2 T730 phosphorylation activated EGFR, Src, AKT, and ERK signaling pathways. In addition, DSG2 and PRKD2 were positively correlated with each other, and the overall survival time of ESCC patients with high DSG2 and PRKD2 was shorter than that of patients with low DSG2 and PRKD2 levels. In summary, PRKD2 is a novel DSG2 kinase, and PRKD2-mediated DSG2 T730 phosphorylation promotes ESCC progression. These findings may facilitate the development of future therapeutic agents that target DSG2 and DSG2 phosphorylation. © 2024 The Pathological Society of Great Britain and Ireland.

Photoinduced [3+2] Cycloaddition of Alkyl-Acceptor Diazoalkanes: Diversity-Oriented Synthesis of Pyrazolines Containing a Quaternary Center.

We present a new [3+2] cycloaddition reaction between alkyl-acceptor diazoalkanes under visible light irradiation. By employing easily accessible alkyl-acceptor-type diazoalkanes or their precursor hydrazones as both 1,3-dipoles and dipolarophiles, a diverse range of pyrazoline derivatives featuring a quaternary center have been efficiently synthesized in a predictable manner, with excellent functional group tolerance and good yields. Furthermore, scale-up experiments and downstream transformations of the product were also detailed.

Serum IGFBP-1 as a promising diagnostic and prognostic biomarker for colorectal cancer.

Our previous study showed that levels of circulating insulin-like growth factor binding protein-1 (IGFBP-1) has potential diagnostic value for early-stage upper gastrointestinal cancers. This study aimed to assess whether serum IGFBP-1 is a potential diagnostic and prognostic biomarker for CRC patients. IGFBP-1 mRNA expression profile data of peripheral blood in colorectal cancer (CRC) patients were downloaded and analyzed from Gene Expression Omnibus database. We detected serum IGFBP-1 in 138 CRC patients and 190 normal controls using enzyme-linked immunosorbent assay. Blood IGFBP-1 mRNA levels were higher in CRC patients than those in normal controls (P = 0.027). In addition, serum IGFBP-1 protein levels in the CRC group were significantly higher than those in normal control group (P < 0.0001). Serum IGFBP-1 demonstrated better diagnostic accuracy for all CRC and early-stage CRC, respectively, when compared with carcinoembryonic antigen (CEA), carbohydrate antigen19-9 (CA 19-9) or the combination of CEA and CA19-9. Furthermore, Cox multivariate analysis revealed that serum IGFBP-1 was an independent prognostic factor for OS (HR = 2.043, P = 0.045). Our study demonstrated that serum IGFBP-1 might be a potential biomarker for the diagnosis and prognosis of CRC. In addition, the nomogram might be helpful to predict the prognosis of CRC.

Pt@AuNF nanozyme and horseradish peroxidase-based lateral flow immunoassay dual enzymes signal amplification strategy for sensitive detection of zearalenone.

Lateral flow immunoassay (LFIA) has been employed extensively for the rapid, accurate, and portable detection of foodborne toxins. Here, the platinum gold nanoflower core-shell (Pt@AuNF) nanozyme with excellent optical properties, good catalytic ability and controllable reaction conditions were prepared to effectively improve the performance of lateral flow immunoassay (LFIA) strips. The Pt@AuNF nanozyme and horseradish peroxidase (HRP) combined with monoclonal antibody were used as signal probes based on the dual enzymes catalytic signal amplification strategy to detect Zearalenone sensitively. Dual enzymes catalyze the decomposition of hydrogen peroxide into hydroxyl radicals, and under the influence of hydroxyl radicals, colorless 3,3',5,5' -tetramethylbenzidine (TMB) is oxidized to blue ox-TMB, which is superimposed on the strips for signal amplification to broaden the detection range. The limit of detection (LOD) of the Pt@AuNF-HRP labeled LFIA strips after signal amplification was 0.052 ng/mL, and the detection range was 0.052-7.21 ng/mL. Compared with the Pt@AuNF labeled strips, while reducing the probes amount by half to achieve antibody conservation, the detection range was expanded by 5-fold based on achieving improved sensitivity. The study provided a meaningful reference for expanding the detection range based on immunoassay.

Collective Information Seeking During a Health Crisis : Predictors of Google Trends During COVID-19.

This article approaches collective health information seeking from computational method by investigating patterns of Google Trends data in the United States during the early stages of the COVID-19 pandemic. We analyzed factors that prompted a community's curiosity, and information that communities were most curious about. The results of our cross-sectional and time-series-based analyses reveal a few salient findings: (1) Republican leaning states searched less frequently, and while states with more cases searched more, partisan lean is a more significant predictor; (2) States with greater level of poverty searched less frequently; (3) Leadership on the national level significantly influenced people's searching behavior; (4) Communities were most interested in "local risk" information as well as quantifiable information. We show in this work that established individual information seeking theoretical predictors (risk) can predict online collective information demand and information seeking subcategories with important contributions from collective conditions (leadership). Health communication practitioners can design health messages and choose media channels more purposefully according to what people are most interested in searching.

Reinforcement Learning-Based Fractional-Order Adaptive Fault-Tolerant Formation Control of Networked Fixed-Wing UAVs With Prescribed Performance.

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

Elevated plasma pyruvate kinase M2 concentrations are associated with the clinical severity and prognosis of coronary artery disease.

Introduction: Pyruvate kinase M2 (PKM2) was involved in the pathophysiology of atherosclerosis and coronary artery disease (CAD). We tested whether plasma PKM2 concentrations were correlated with clinical severity and major adverse cardiovascular events (MACEs) in CAD patients. Materials and methods: A total of 2443 CAD patients and 238 controls were enrolled. The follow-up time was two years. Plasma PKM2 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone, Wuhan, China) using SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, San Jose, USA). The predictors of acute coronary syndrome (ACS) were assessed by logistic regression analysis. The association between PKM2 concentration in different quartiles and MACEs was evaluated by Kaplan-Meier (KM) curves with log-rank test and Cox proportional hazard models. The predictive value of PKM2 and a cluster of conventional risk factors was determined by Receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were utilized to evaluate the enhancement in risk prediction when PKM2 was added to a predictive model containing a cluster of conventional risk factors. Results: In CAD patients, PKM2 concentration was the independent predictor of ACS (P < 0.001). Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses revealed that patients with a higher PKM2 concentration had higher incidence of MACEs compared to those with a lower PKM2 concentration (P < 0.001). The addition of PKM2 to a cluster of conventional risk factors significantly increased its prognostic value of MACEs. Conclusion: Baseline plasma PKM2 concentrations predict the clinical severity and prognosis of CAD.

Mn2+-Triggered Swing-Arm Robot Strategy Using Anemone-Like Thi@AuPd@Cu-MOFs as Signaling Probes for the Detection of T-2 Toxin.

Herein, we synthesized anemone-like copper-based metal-organic frameworks (MOFs) loaded with gold-palladium nanoparticles (AuPd@Cu-MOFs) and polyethylenimine-reduced graphene oxide/gold-silver nanosheet composites (PEI-rGO/AuAg NSs) for the first time to construct the sensor and to detect T-2 toxin (T-2) using triple helix molecular switch (THMS) and signal amplification by swing-arm robot. The aptasensor used PEI-rGO/hexagonal AuAg NSs as the electrode modification materials and anemone-like AuPd@Cu-MOFs as the signal materials. The prepared PEI-rGO/hexagonal AuAg NSs had a large specific surface area, excellent electrical conductivity, and good stability, which successfully improved the electrochemical performance of the sensors. The AuPd@Cu-MOFs with high porosity provided a great deal of attachment sites for the signaling molecule thionine (Thi), thereby increasing the signal response. The aptasensor developed in this study demonstrated a remarkable detection limit of 0.054 fg mL-1 under optimized conditions. Furthermore, the successful detection of T-2 in real samples was achieved using the fabricated sensor. The simplicity of the THMS-based method, which entails modifying the aptamer sequence, allows for easy adaptation to different target analytes. Thus, the sensor holds immense potential for applications in quality supervision and food safety.

Genome degradation promotes Salmonella pathoadaptation by remodeling fimbriae-mediated proinflammatory response.

Understanding changes in pathogen behavior (e.g. increased virulence, a shift in transmission channel) is critical for the public health management of emerging infectious diseases. Genome degradation via gene depletion or inactivation is recognized as a pathoadaptive feature of the pathogen evolving with the host. However, little is known about the exact role of genome degradation in affecting pathogenic behavior, and the underlying molecular detail has yet to be examined. Using large-scale global avian-restricted Salmonella genomes spanning more than a century, we projected the genetic diversity of Salmonella Pullorum (bvSP) by showing increasingly antimicrobial-resistant ST92 prevalent in Chinese flocks. The phylogenomic analysis identified three lineages in bvSP, with an enhancement of virulence in the two recently emerged lineages (L2/L3), as evidenced in chicken and embryo infection assays. Notably, the ancestor L1 lineage resembles the Salmonella serovars with higher metabolic flexibilities and more robust environmental tolerance, indicating stepwise evolutionary trajectories towards avian-restricted lineages. Pan-genome analysis pinpointed fimbrial degradation from a virulent lineage. The later engineered fim-deletion mutant, and all other five fimbrial systems, revealed behavior switching that restricted horizontal fecal-oral transmission but boosted virulence in chicks. By depleting fimbrial appendages, bvSP established persistent replication with less proinflammation in chick macrophages and adopted vertical transovarial transmission, accompanied by ever-increasing intensification in the poultry industry. Together, we uncovered a previously unseen paradigm for remodeling bacterial surface appendages that supplements virulence-enhanced evolution with increased vertical transmission.

ASSESS-IE: a Novel Risk Score for Patients with Infective Endocarditis.

Mortality in patients with infective endocarditis (IE) remains high. The existing risk scores are relatively complex with limited clinical application. This study was conducted to establish a new risk model to predict in-hospital and 6-month mortality in IE patients. A total of 1549 adult patients with definite IE admitted to Guangdong Provincial People's Hospital (n=1354) or Xiamen Cardiovascular Hospital (n=195) were included. The derivation cohort consisted of 1141 patients. The score was developed using the multivariate stepwise logistic regression analysis for in-hospital death. Bootstrap analysis was used for validation. Discrimination and calibration were evaluated by the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test. Six risk factors were used as score parameters (1 point for each): aortic valve affected, previous valve replacement surgery, severe heart failure, elevated serum direct bilirubin, moderate-severe anemia and acute stage. The predictive value and calibration of the ASSESS-IE score for in-hospital death were excellent in the derivation (area under the curve [AUC]=0.781, p<0.001; Hosmer-Lemeshow p=0.948) and validation (AUC=0.779, p<0.001; Hosmer-Lemeshow p=0.520) cohorts. The score remained excellent in bootstrap validation (AUC=0.783). The discriminatory ability of the ASSESS-IE score for in-hospital (AUC: 0.781 vs. 0.799, p=0.398) and 6-month mortality (AUC: 0.778 vs. 0.814, p=0.040) were similar with that of Park's score which comprised 14 variables. The ASSESS-IE risk score is a new and robust risk-stratified tool for patients with IE, which might further facilitate clinical decision-making.

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm.

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Nomogram based on pretreatment hepatic and renal function indicators for survival prediction of locally advanced esophageal squamous cell carcinoma with treatment of neoadjuvant chemoradiotherapy plus surgery.

The parameters for survival prediction of esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant chemoradiotherapy (NCRT) combined with surgery are unclear. Here, we aimed to construct a nomogram for survival prediction of ESCC patients treated with NCRT combined with surgery based on pretreatment serological hepatic and renal function tests. A total of 174 patients diagnosed as ESCC were enrolled as a training cohort from July 2007 to June 2019, and approximately 50% of the cases (n = 88) were randomly selected as an internal validation cohort. Univariate and multivariate Cox survival analyses were performed to identify independent prognostic factors to establish a nomogram. Predictive accuracy of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration curve. ALT, ALP, TBA, TP, AST, TBIL and CREA were identified as independent prognostic factors and incorporated into the construction of the hepatic and renal function test nomogram (HRFTNomogram). The C-index of the HRFTNomogram for overall survival (OS) was 0.764 (95% CI 0.701-0.827) in the training cohort, which was higher than that of the TNM staging system (0.507 (95% CI 0.429-0.585), P < 0.001). The 5-year OS calibration curve of the training cohort demonstrated that the predictive accuracy of the HRFTNomogram was satisfactory. Moreover, patients in the high-risk group stratified by the HRFTNomogram had poorer 5-year OS than those in the low-risk group in the training cohort (27.4% vs. 80.3%, P < 0.001). Similar results were observed in the internal validation cohort. A novel HRFTNomogram might help predict the survival of locally advanced ESCC patients treated with NCRT followed by esophagectomy.

NY-ESO-1 antigen-antibody interaction process based on an TFBG plasmonic sensor.

Autoantibodies against New York esophageal squamous cell cancer 1 (NY-ESO-1) play a crucial role in the diagnosis of esophageal cancer. In this work, a surface plasmonic tilted fiber Bragg grating (TFBG) biosensor is proposed for the detection of NY-ESO-1 antibody, as well as the investigation of the hook effect (which refers to the false negative result in some immunoassays when the concentration of antibodies in the sample is very high) during biomolecular binding between NY-ESO-1 antigen and antibody. The biosensor is made by an 18° TFBG coated with a 50-nm-thick gold film over the fiber surface together with NY-ESO-1 antigens attached to the metallic surface serving as bio-receptors. This biosensor can provide a limit of detection at a concentration of 2 × 10-7 µg/ml with a good linearity in the range from 2 × 10-7 to 2 × 10-5 µg/ml. For a concentration higher than 2 × 10-3 µg/ml, the performance of the sensor probe is reduced owing to the hook effect. Furthermore, experimental results have also demonstrated the repeatability of the proposed biosensor. This proposed biosensor features label-free, compactness, and fast response, which could be potentially applied in the diagnosis of esophageal cancer.

Design of target response wettability switchable core-shell-shell electrochemiluminescence nanoprobes for sensitive hyaluronidase detection.

Electrochemiluminescence nanoprobes with a core-shell-shell structure have been designed and applied for hyaluronidase detection. The nanoprobes can precipitate efficiently through target-regulation wettability for collection, and enrich near to the hydrophobic electrode surface through hydrophobic interaction to enhance the performance of the biosensor.

Relationship between esophageal squamous cell carcinoma risk and alcohol-related ALDH2 and ADH1B polymorphisms: Evidence from a meta-analysis and Mendelian randomization analysis.

BACKGROUND: Previous studies have shown that ALDH2 and ADH1B genes may be associated with alcohol metabolism and the risk of esophageal squamous cell carcinoma (ESCC), with inconsistent results. This meta-analysis aimed at comprehensively assessing the associations between ALDH2 and ADH1B polymorphisms and the risk of ESCC to synthesize and clarify the evidence. METHODS: We calculated summary estimates of the associations between four genetic variants (rs671 and rs674 in ALDH2, and rs1229984 and rs1042026 in ADH1B) and the ESCC risk across 23 publications in the additive model and allelic model. Venice criteria, Bayesian false discovery probability (BFDP), and false-positive reporting probability (FPRP) were used to assess the strength of epidemiological evidence. Heterogeneity among studies was evaluated by using the Higgin's I2 statistic, and publication bias was assessed by using funnel plots and Begg's test. A Mendelian randomization (MR) analysis was performed to determine the causal association between alcohol intake and esophageal cancer risk. Data from the HaploReg v4.1 and PolyPhen-2 were analyzed for functional annotations. RESULTS: Of the four genetic variants, rs671 of ALDH2 was associated with a significantly reduced risk of ESCC (OR: 0.60, 95% CI: 0.50-0.73), whereas rs1229984 of ADH1B was associated with a significantly increased risk (2.50, 95% CI: 1.70-3.69) in the additive model. In the allelic model, the variant rs1229984 of ADH1B also increased the risk of ESCC (OR: 1.50; 95% CI: 1.21-1.87). The result for the variant rs671 was considered as strong epidemiological evidence. Functional annotations identified that the four variants were related to the enhancer histone marks and motif changes. The other two variants were not associated with the ESCC risk (rs674 of ALDH2 OR: 1.22, 95% CI: 0.71-2.12; rs1042026 of ADH1B OR: 1.28, 95% CI: 0.52-3.14) in the additive model. The MR analysis did not find a causal effect of alcohol on the esophageal cancer risk. CONCLUSIONS: The results showed that ADH1B rs1229984 was significantly associated with an increased the risk of ESCC.

A novel magneto-mediated electrochemical biosensor integrated DNAzyme motor and hollow nanobox-like Pt@Ni-Co electrocatalyst as dual signal amplifiers for vanilla detection.

Herein, a novel magneto-mediated electrochemical aptasensor using the signal amplification technologies of DNAzyme motor and electrocatalyst for vanilla (VAN) detection was fabricated. The D/B duplex, formed by the DNAzyme motor that was each silenced by a blocker, and hairpin DNA1 (H1) containing adenosine ribonucleotide (rA) site were tethered on the sites of the gold nanoparticles@hollow porphyrinic-Metal-organic framework/polyethyleneimine-reduced graphene oxide (AuHPCN-222/PEI-rGO)-modified gold electrode (AuE). Then, after homogeneous and specific recognition in the presence of the VAN, trigger DNA was released and enriched by magnetic separation technique and introduced to the sensing platform to activate the DNAzyme motor, which efficiently improved target recognition capability and avoided the obstacle of multiple DNA strands tangling. More interestingly, the activated DNAzyme motor could repeatedly bind to and cleave H1 in the presence of Mg2+, leading to the exposure of a plethora of capture probes. The thionine (Thi) functionalized hairpin DNA2 (H2)-Pt@Ni-Co as signal probes could hybridize with capture probes. Additionally, the Pt@Ni-Co electrocatalysts presented catalytic activity towards Thi to obtain stronger electrochemical signals. VAN with concentrations ranging from 1 × 10-6 to 10 µM was determined and a detection limit was down to 0.15 pM. The designed electrochemical sensor was highly selective with specificity, stability, reproducibility, and reliable capability for monitoring the VAN in real samples.

Secreted proteins encoded by super enhancer-driven genes could be promising biomarkers for early detection of esophageal squamous cell carcinoma.

BACKGROUND: Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC). METHODS: We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy. RESULTS: Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907). CONCLUSIONS: Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.