SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.

Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.

Fluorescence turn-on detection of human serum albumin based on the assembly of gold nanoclusters and bromocresol green.

As the most abundant protein in plasma, human serum albumin plays a vital role in physiological processes, such as maintaining blood osmotic pressure and carrying small-molecule ligands. Since the content of albumin in the human serum can reflect the status of liver and renal function, albumin quantitation is significant in clinical diagnosis. In this work, fluorescence turn-on detection of human serum albumin (HSA) had been performed based on the assembly of gold nanoclusters and bromocresol green. Gold nanoclusters (AuNCs) capped by reduced glutathione (GSH) were assembled with bromocresol green (BCG), and the assembly was used as a fluorescent probe for HSA. After BCG assembling, the fluorescence of gold nanoclusters was nearly quenched. In acidic solution, HSA can selectively bind to BCG on the assembly and recover the fluorescence of the solution. Based on this turn-on fluorescence, ratiometric HSA quantification was realized. Under optimal conditions, HSA detection by the probe possessed a good linear relationship in the range of 0.40-22.50 mg·mL-1, and the detection limit was 0.27 ± 0.04 mg·mL-1 (3σ, n = 3). Common coexisting components in serum and blood proteins did not interfere with the detection of HSA. This method has the advantages of easy manipulation and high sensitivity, and the fluorescent response is insensitive to reaction time.

Detection of biomarkers using terahertz metasurface sensors and machine learning.

To achieve classification and concentration detection of cancer biomarkers, we propose a method that combines terahertz (THz) spectroscopy, metasurface sensors, and machine learning. A metasurface sensor suitable for biomarker detection was designed and fabricated with five resonance frequencies in the range of 0.3-0.9 THz. We collected biomarkers of five types and nine concentrations at 100 sets of time-domain spectra per concentration. The spectrum is processed by noise reduction and fast Fourier transform to obtain the frequency-domain spectrum. Five machine learning algorithms are used to analyze time- and frequency-domain spectra and ascertain which algorithm is more suitable for the classification of the biomarker THz spectrum. Experimental results show that random forest can better distinguish five biomarkers with an accuracy of 0.984 for the time-domain spectrum. For the frequency-domain spectrum, the support vector machine performs better, with an accuracy of 0.989. For biomarkers at different concentrations, we used linear regression to fit the relationship between biomarker concentration and frequency shift. Experimental results show that machine learning can distinguish different biomarker species and their concentrations by the THz spectrum. This work provides an idea and data processing method for the application of THz technology in biomedical detection.

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.

BACKGROUND: Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery. However, segmenting kidney tumors manually can be difficult because of their heterogeneity. METHODS: This paper proposes a 2.5D MFFAU-Net (multi-level Feature Fusion Attention U-Net) to segment kidneys, tumors and cysts. First, we propose a 2.5D model for learning to combine and represent a given slice in 2D slices, thereby introducing 3D information to balance memory consumption and model complexity. Then, we propose a ResConv architecture in MFFAU-Net and use the high-level and low-level feature in the model. Finally, we use multi-level information to analyze the spatial features between slices to segment kidneys and tumors. RESULTS: The 2.5D MFFAU-Net was evaluated on KiTS19 and KiTS21 kidney datasets and demonstrated an average dice score of 0.924 and 0.875, respectively, and an average Surface dice (SD) score of 0.794 in KiTS21. CONCLUSION: The 2.5D MFFAU-Net model can effectively segment kidney tumors, and the results are comparable to those obtained with high-performance 3D CNN models, and have the potential to serve as a point of reference in clinical practice.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was -0.04 L/min with a -1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between -20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

Point Cloud Deep Learning Network Based on Balanced Sampling and Hybrid Pooling.

The automatic semantic segmentation of point cloud data is important for applications in the fields of machine vision, virtual reality, and smart cities. The processing capability of the point cloud segmentation method with PointNet++ as the baseline needs to be improved for extremely imbalanced point cloud scenes. To address this problem, in this study, we designed a weighted sampling method based on farthest point sampling (FPS), which adjusts the sampling weight value according to the loss value of the model to equalize the sampling process. We also introduced the relational learning of the neighborhood space of the sampling center point in the feature encoding process, where the feature importance is distinguished by using a self-attention model. Finally, the global-local features were aggregated and transmitted using the hybrid pooling method. The experimental results of the six-fold crossover experiment showed that on the S3DIS semantic segmentation dataset, the proposed network achieved 9.5% and 11.6% improvement in overall point-wise accuracy (OA) and mean of class-wise intersection over union (MIoU), respectively, compared with the baseline. On the Vaihingen dataset, the proposed network achieved 4.2% and 3.9% improvement in OA and MIoU, respectively, compared with the baseline. Compared with the segmentation results of other network models on public datasets, our algorithm achieves a good balance between OA and MIoU.

Non-Enzymatically Colorimetric Bilirubin Sensing Based on the Catalytic Structure Disruption of Gold Nanocages.

As an essential indicator of liver function, bilirubin is of great significance for clinical diagnosis. A non-enzymatic sensor has been established for sensitive bilirubin detection based on the bilirubin oxidation catalyzed by unlabeled gold nanocages (GNCs). GNCs with dual-localized surface plasmon resonance (LSPR) peaks were prepared by a one-pot method. One peak around 500 nm was ascribed to gold nanoparticles (AuNPs), and the other located in the near-infrared region was the typical peak of GNCs. The catalytic oxidation of bilirubin by GNCs was accompanied by the disruption of cage structure, releasing free AuNPs from the nanocage. This transformation changed the dual peak intensities in opposite trend, and made it possible to realize the colorimetric sensing of bilirubin in a ratiometric mode. The absorbance ratios showed good linearity to bilirubin concentrations in the range of 0.20~3.60 µmol/L with a detection limit of 39.35 nM (3σ, n = 3). The sensor exhibited excellent selectivity for bilirubin over other coexisting substances. Bilirubin in real human serum samples was detected with recoveries ranging from 94.5 to 102.6%. The method for bilirubin assay is simple, sensitive and without complex biolabeling.

MTAN: A semi-supervised learning model for kidney tumor segmentation.

BACKGROUND: Medical image segmentation is crucial in disease diagnosis and treatment planning. Deep learning (DL) techniques have shown promise. However, optimizing DL models requires setting numerous parameters, and demands substantial labeled datasets, which are labor-intensive to create. OBJECTIVE: This study proposes a semi-supervised model that can utilize labeled and unlabeled data to accurately segment kidneys, tumors, and cysts on CT images, even with limited labeled samples. METHODS: An end-to-end semi-supervised learning model named MTAN (Mean Teacher Attention N-Net) is designed to segment kidneys, tumors, and cysts on CT images. The MTAN model is built on the foundation of the AN-Net architecture, functioning dually as teachers and students. In its student role, AN-Net learns conventionally. In its teacher role, it generates objects and instructs the student model on their utilization to enhance learning quality. The semi-supervised nature of MTAN allows it to effectively utilize unlabeled data for training, thus improving performance and reducing overfitting. RESULTS: We evaluate the proposed model using two CT image datasets (KiTS19 and KiTS21). In the KiTS19 dataset, MTAN achieved segmentation results with an average Dice score of 0.975 for kidneys and 0.869 for tumors, respectively. Moreover, on the KiTS21 dataset, MTAN demonstrates its robustness, yielding average Dice scores of 0.977 for kidneys, 0.886 for masses, 0.861 for tumors, and 0.759 for cysts, respectively. CONCLUSION: The proposed MTAN model presents a compelling solution for accurate medical image segmentation, particularly in scenarios where the labeled data is scarce. By effectively utilizing the unlabeled data through a semi-supervised learning approach, MTAN mitigates overfitting concerns and achieves high-quality segmentation results. The consistent performance across two distinct datasets, KiTS19 and KiTS21, underscores model's reliability and potential for clinical reference.

Surface-Functionalized Terahertz Metamaterial Biosensor Used for the Detection of Exosomes in Patients.

Owing to their stability in bodily fluids, exosomes have attracted increased attention as colorectal cancer (CRC) biomarkers for early diagnosis. To validate the potential of the plasma exosomes as a novel biomarker for the monitoring of CRC, we demonstrated a terahertz (THz) metamaterials (MMs) biosensor for the detection of exosomes in this work. The biosensor with two resonant frequencies is designed using full wave electromagnetic simulation software based on the finite integration time domain (FITD) method and fabricated by a surface micromachining process. The biosensor surface is first modified using Au nanoparticles (AuNPs), and then, anti-KRAS and anti-CD147, which are specific to the exosomes, are modified on the AuNPs assembled with HS-poly(ethylene glycol)-COOH (HS-PEG-COOH). Exosomes used in the experiment are extracted via the instructions in the exosomes isolation and purification kit and identified by using transmission electron microscopy (TEM), Western blot (WB), and nanoparticle tracking analysis (NTA). The biosensor covered with plasma-derived exosomes of CRC patients has a different resonance frequency shift compared to that with healthy-control-derived exosomes. This study proposes an emerging and quick method for diagnosing the CRC.

Facile bimetallic co-amplified electrochemical sensor for folic acid sensing based on CoNPs and CuNPs.

Folic acid (FA) is essential for human health, particularly for pregnant women and infants. In this work, a glassy carbon electrode (GCE) was modified by a bimetallic layer of Cu/Co nanoparticles (CuNPs/CoNPs) as a synergistic amplification element by simple step-by-step electrodeposition, and was used for sensitive detection of FA. The proposed CuNPs/CoNPs/GCE sensor was characterized by differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS) and field emission scanning electron microscopy (FESEM). Then, under optimal conditions, a linear relationship was obtained in the wide range of 110.00-1750.00 µM for the detection of FA with a limit of detection (LOD) of 34.79 µM (S/N = 3). The sensitivity was calculated as 0.096 µA µM-1 cm-2. Some interfering compounds including glucose (Glc), biotin, dopamine (DA), and glutamic acid (Glu) showed little effect on the detection of FA by amperometry (i-t). Finally, the average recovery obtained was in a range of 91.77-110.06%, with a relative standard deviation (RSD) less than 8.00% in FA tablets, indicating that the proposed sensor can accurately and effectively detect the FA content in FA tablets.

Metagenomic next-generation sequencing contributes to the diagnosis of mixed pulmonary infection: a case report.

BACKGROUND: Pulmonary cryptococcosis (PC) and mixed pulmonary infection are difficult to be diagnosed due to the non-specificity and their overlapping clinical manifestations. In terms of the clinical diagnosis of PC and mixed pulmonary infection, conventional tests have limitations such as a long detection period, a limited range of pathogens, and low sensitivity. Metagenomics next-generation sequencing (mNGS) is a nascent and powerful method that can detect pathogens without culture, to diagnose known and unexplained infections in reduced time. CASE PRESENTATION: A 43-year-old female was admitted to the hospital after suffering from a cough for one month. At the time of admission, a contrast-enhanced chest CT revealed multiple nodules and plaques in her right lung, as well as the formation of cavities. The blood routine assays showed evidently increased white blood cell count (mainly neutrophils), CRP, and ESR, which suggested she was in the infection phase. The serum CrAg-LFA test showed a positive result. Initially, she was diagnosed with an unexplained pulmonary infection. Bronchoalveolar lavage fluid (BALF) samples were collected for microbial culture, immunological tests and the mNGS. Microbial culture and immunological tests were all negative, while mNGS detected Corynebacterium striatum, Pseudomonas aeruginosa, Streptococcus pneumoniae, and Cryptococcus neoformans. The diagnosis was revised to PC and bacterial pneumonia. Lung infection lesions were healed after she received targeted anti-infection therapy with mezlocillin and fluconazole. In a follow-up after 2 months, the patient's symptoms vanished. CONCLUSIONS: Here, we demonstrated that mNGS was capable of accurately distinguishing Cryptococcus from M. tuberculosis in pulmonary infection, and notably mNGS was capable of swiftly and precisely detecting pathogens in mixed bacterial and fungal pulmonary infection. Furthermore, the results of mNGS also have the potential to adjust anti-infective therapies.

Terahertz metamaterial biosensor for diagnosis of hepatocellular carcinoma at early stage.

We propose a method for diagnosis of cirrhosis and hepatocellular carcinoma (HCC) by using a terahertz (THz) metamaterial (MM) biosensor. The biosensor has a resonance frequency at about 0.801 THz and can measure the concentration of alpha-fetoprotein (AFP) in serum. The sensitivity of the sensor is 124 GHz/refractive index unit (RIU), and the quality-factor (Q) is 6.913, respectively. When the surface of the biosensor is covered with healthy serum (AFP≤7ng/mL), the maximum resonance frequency shift is 50 GHz. However, when it is covered with serum from patients with cirrhosis and early HCC (AFP>7ng/mL), the resonance frequency shift is more than 59 GHz. Positive correlation exists between the frequency shift of the biosensor and serum levels of the AFP in the HCC patients. This study provides a method for quick diagnosis and prediction of cirrhosis and HCC.

Simultaneously improving the resolving power and sensitivity for planar high-field asymmetric waveform ion mobility spectrometry using a mixed gas inlet mode at two ends.

RATIONALE: Resolution and sensitivity are two key parameters for describing the performance of high-field asymmetric waveform ion mobility spectrometry (FAIMS). An increase in the resolving power of FAIMS has been realized by adding helium to nitrogen in planar FAIMS, but it comes at the expense of sensitivity. METHODS: Here, a new hollow needle-to-ring discharge device integrated on a PCB substrate is used as the ion source for FAIMS. Helium flows from the hollow part of the hollow needle to improve the ionization effect. Nitrogen carries the sample into the ionization chamber and is mixed with helium as the carrier gas. RESULTS: Under a nitrogen flow rate of 1 L min-1 , 1.5 L min-1 , 2 L min-1 , and 2.5 L min-1 , adding helium at different flow rates (0.2 L min-1 , 0.3 L min-1 , 0.5 L min-1 , and 1 L min-1 ) can simultaneously improve the separation ability and sensitivity. Helium and nitrogen with flow rates of 0.2, 0.3, 0.5, and 1 L min-1 were added to nitrogen (2 L min-1 ). The separation ability and sensitivity of the mixed gases doped with helium are better than those of nitrogen. The larger the RF voltage amplitude is, the more obvious the improvement in the separation ability when helium is added. However, helium doping has the opposite effect on the sensitivity. CONCLUSIONS: This study provides a new idea and technical means for the application of helium and nitrogen gas mixtures in planar FAIMS. This method can greatly improve the performance of FAIMS.

Helium-assisted enhanced discharge in a hollow needle for high-field asymmetric ion mobility spectrometry (FAIMS).

A carrier gas mixture of nitrogen and helium has been employed to improve the resolving power at the expense of sensitivity for planar high-field asymmetric ion mobility spectrometry (FAIMS) in previous work. In this paper, a new hollow needle-to-ring ion source was developed, where the helium and nitrogen enter from the hollow needle and ring, respectively. It was found that the signal strengths of acetone, ethanol, and ethyl acetate increased by 8.5, 2.0, and 3.3 times for helium ratios of 20%, 20%, and 10%, respectively. At the same time, the absolute value of compensation voltage and the number of ion peaks increases. It shows that adding an appropriate helium ratio to nitrogen simultaneously improved the sensitivity and resolving power of planar FAIMS, which is reported for the first time.

A rapid lateral flow immunoassay strip for detection of SARS-CoV-2 antigen using latex microspheres.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious and concealed virus that causes pneumonia, severe acute respiratory syndrome, and even death. Although the epidemic has been controlled since the development of vaccines and quarantine measures, many people are still infected, particularly in third-world countries. Several methods have been developed for detection of SARS-CoV-2, but owing to its price and efficiency, the immune strip could be a better method for the third-world countries. METHODS: In this study, two antibodies were linked to latex microspheres, using 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide, as the bridge to decrease the cost further and improve the detection performance. The specificity of the lateral flow immunoassay strip (LFIA) was tested by several common viruses and respiratory bacterial infections. Besides, the reproducibility and stability of the LFIAs were tested on the same batch of test strips. Under optimal conditions, the sensitivity of LFIA was determined by testing different dilutions of the positive specimens. RESULTS: The proposed LFIAs were highly specific, and the limit of detection was as low as 25 ng/mL for SARS-CoV-2 antigens. The clinical applicability was evaluated with 659 samples (230 positive and 429 negative samples) by using both LFIA and rRT-PCR. Youden's index (J) was used to assess the performance of these diagnostic tests. The sensitivity and specificity were 98.22% and 97.93%, respectively, and J is 0.9615. The sensitivity and specificity were 98.22% and 97.93%, respectively, and J is 0.9615. In addition, the consistency of our proposed LFIA was analyzed using Cohen's kappa coefficient (κ = 0.9620). CONCLUSION: We found disease stage, age, gender, and clinical manifestations have only a slight influence on the diagnosis. Therefore, the lateral flow immunoassay SARS-CoV-2 antigen test strip is suitable for point-of-care detection and provides a great application for SARS-CoV-2 epidemic control in the third-world countries.

Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning.

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning.

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals.

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Electrochemical immunosensor based on binary nanoparticles decorated rGO-TEPA as magnetic capture and Au@PtNPs as probe for CEA detection.

Using gold and magnetic nanoparticles co-decorated reduced graphene oxide-tetraethylenepentamine (rGO-TEPA/Au-MNPs) as the magnetic platform for capturing the primary antibody (Ab1), separation and preconcentration of immunocomplex, a novel homogeneous electrochemical immunosensor was successfully developed. The newly prepared magnetic rGO-TEPA/Au-MNPs, compared with MNPs, exhibited better stability and enhanced electrical conductivity attributed to rGO-TEPA, and showed higher biorecognition efficiency due to AuNPs. In addition, Au@PtNPs were prepared and modified with secondary antibody (Ab2) as an efficient signal probe for signal readout. Using carcinoembryonic antigen (CEA) as a model analyte, the prepared immunosensor demonstrated satisfactory properties like high stability, good repeatability and selectivity, wide linear range (5.0 pg mL-1~200.0 ng mL-1) as well as low detection limit (1.42 pg mL-1). The homogenous electrochemical immunosensor was applied to the detection of CEA in human serum and was found to exhibit good correlation with the reference method. Thus, the proposed rGO-TEPA/Au-MNPs-based homogenous immunoassay platform might open up a new way for biomarker diagnosis. Graphical Abstract.

A Gold Nanoclusters Film Supported on Polydopamine for Fluorescent Sensing of Free Bilirubin.

Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes for biorecognition. The polydopamine film was utilized as an adhesion layer for immobilization of AuNCs. When the composite film was exposed to free bilirubin, due to the complex that was formed between BSA and free bilirubin, the fluorescence intensity of the composite film was gradually weakened as the bilirubin concentration increased. The fluorescence quenching ratio (F0/F) was linearly proportional to free bilirubin over the concentration range of 0.8~50 µmol/L with a limit of detection of 0.61 ± 0.12 µmol/L (S/N = 3). The response was quick, the film was recyclable, and common ingredients in human serum did not interfere with the detection of free bilirubin.