Recent Progress in Biosensors for Detection of Tumor Biomarkers

Cancer is a leading cause of death worldwide, with an increasing mortality rate over the past years. The early detection of cancer contributes to early diagnosis and subsequent treatment. How to detect early cancer has become one of the hot research directions of cancer. Tumor biomarkers, biochemical parameters for reflecting cancer occurrence and progression have caused much attention in cancer early detection. Due to high sensitivity, convenience and low cost, biosensors have been largely developed to detect tumor biomarkers. This review describes the application of various biosensors in detecting tumor markers. Firstly, several typical tumor makers, such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), squamous cell carcinoma antigen (SCCA), carbohydrate, antigen19-9 (CA19-9) and tumor suppressor p53 (TP53), which may be helpful for early cancer detection in the clinic, are briefly described. Then, various biosensors, mainly focusing on electrochemical biosensors, optical biosensors, photoelectrochemical biosensors, piezoelectric biosensors and aptamer sensors, are discussed. Specifically, the operation principles of biosensors, nanomaterials used in biosensors and the application of biosensors in tumor marker detection have been comprehensively reviewed and provided. Lastly, the challenges and prospects for developing effective biosensors for early cancer diagnosis are discussed.     

Protective Effect of Flavonoids against Methylglyoxal-Induced Oxidative Stress in PC-12 Neuroblastoma Cells and Its Structure–Activity Relationships

Methylglyoxal-induced oxidative stress and cytotoxicity are the main factors causing neuronal death-related, diabetically induced memory impairment. Antioxidant and anti-apoptotic therapy are potential intervention strategies. In this study, 25 flavonoids with different substructures were assayed for protecting PC-12 cells from methylglyoxal-induced damage. A structure–activity relationship (SAR) analysis indicated that the absence of the double bond at C-2 and C-3, substitutions of the gallate group at the 3 position, the pyrogallol group at the B-ring, and the R configuration of the 3 position enhanced the protection of flavan-3-ols, and a hydroxyl substitution at the 4′ and meta-positions were important for the protection of flavonol. These SARs were further confirmed by molecular docking using the active site of the Keap1–Nrf2 complex as the receptor. The mechanistic study demonstrated that EGCG with the lowest EC₅₀ protected the PC-12 cells from methylglyoxal-induced damage by reducing oxidative stress via the Nrf2/Keap1/HO-1 and Bcl-2/Bax signaling pathways. These results suggested that flavan-3-ols might be a potential dietary supplement for protection against diabetic encephalopathy.     

Metabolites Identification and Mechanism Prediction of Neobavaisoflavone In Vitro and In Vivo of Rats through UHPLC-Q-Exactive Plus Orbitrap MS Integrated Network Pharmacology

Neobavaisoflavone is an important isoflavone component isolated from Psoraleae Fructus. It is used extensively worldwide because of its antibacterial, antioxidant, anti-inflammatory, anticancer, and anti-osteoporotic activities. However, there is no systematic and comprehensive research on the metabolism of neobavaisoflavone in vivo and in vitro. The study aimed to analyze the metabolic characteristics and mechanism of neobavaisoflavone for the first time. Firstly, biological samples were pretreated by the solid-phase extraction (SPE) method, methanol precipitation, and acetonitrile precipitation. Secondly, the samples were analyzed on UHPLC-Q-Exactive Plus Orbitrap MS. Thirdly, metabolites were tentatively identified based on retention time, parallel reaction monitoring strategy, diagnostic product ions, and neutral loss fragments. A total of 72 metabolites of neobavaisoflavone were tentatively identified, including 28 in plasma, 43 in urine, 18 in feces, six in the liver, and four in the liver microsome. The results suggested that neobavaisoflavone mainly underwent glucuronidation, sulfation, hydroxylation, methylation, cyclization, hydration, and their composite reactions in vivo and in vitro. In addition, nine active components with high bioavailability and 191 corresponding targets were predicted by the Swiss Drug Design database. The 1806 items of GO and 183 KEGG signaling pathways were enriched. These results showed that metabolites expanded the potential effects of neobavaisoflavone. The present study would provide the scientific basis for the further exploitation and application of neobavaisoflavone.     

Stepwise Diagnostic Product Ions Filtering Strategy for Rapid Discovery of Diterpenoids in Scutellaria barbata Based on UHPLC-Q-Exactive-Orbitrap-MS

Diterpenoids are considered the major bioactive components in Scutellaria barbata to treat cancer and inflammation, but few comprehensive profiling studies of diterpenoids have been reported. Herein, a stepwise diagnostic product ions (DPIs) filtering strategy for efficient and targeted profiling of diterpenoids in Scutellaria barbata was developed using UHPLC-Q-Exactive-Orbitrap-MS. After UHPLC-HRMS/MS analysis of six diterpenoid reference standards, fragmentation behaviors of these references were studied to provide DPIs. Then, stepwise DPIs filtering aimed to reduce the potential interferences of matrix ions and achieve more chromatographic peaks was conducted to rapidly screen the diterpenoids. The results demonstrated that stepwise DPIs were capable of simplifying the workload in data post-processing and the effective acquisition of low abundance compounds. Subsequently, DPIs and MS/MS fragment patterns were adopted to identify the targeted diterpenoids. As a result, 381 diterpenoids were unambiguously or tentatively identified, while 141 of them with completely new molecular weights were potential new diterpenoids for Scutellaria barbata. These results demonstrate that the developed stepwise DPIs filtering method could be employed as an efficient, reliable, and valuable strategy to screen and identify the diterpenoid profile in Scutellaria barbata. This might accelerate and simplify target constituent profiling from traditional Chinese medicine (TCM) extracts.     

Contact-Piezoelectric Bi-Catalysis of an Electrospun ZnO@PVDF Composite Membrane for Dye Decomposition

The treatment of organic pollutants in wastewater is becoming a great challenge for social development. Herein, a novel contact-piezoelectric bi-catalysis of a ZnO@ PVDF composite membrane was prepared by electrospinning technology. The obtained ZnO@PVDF composite membranes is superior to the pure PVDF membrane in decomposing methyl orange (MO) under ultrasonication at room temperature, which is mainly attributed to the synergy effect of the contact-electro-catalysis of dielectric PVDF, as well as the piezoelectric catalysis of tetrapodal ZnO and the β-phase of PVDF. The heterostructure of the piezoelectric-ZnO@dielectric-PVDF composite is beneficial in reducing the electron/hole pair recombination. As compared to the pure PVDF membrane, the catalytic degradation efficiency of the ZnO@PVDF composite membrane was improved by 444.23% under ultrasonication. Moreover, the reusability and stability of the composite membrane are comparable to those of the traditional powdered catalyst. This work offers a promising strategy for improving the pollutant degradation by combining contact-electro-catalysis with piezoelectric catalysis.     

A smelting–rolling strategy for ZnIn bulk phase alloy anodes

Reversibility and stability are considered as the key indicators for Zn metal anodes in aqueous Zn-ion batteries, yet they are severely hindered by uncontrolled Zn stripping/plating and side reactions. Herein, we fabricate a bulk phase ZnIn alloy anode containing trace indium by a typical smelting–rolling process. A uniformly dispersed bulk phase of the whole Zn anode is constructed rather than only a protective layer on the surface. The Zn deposition can be regarded as instantaneous nucleation due to the adsorption of the evenly dispersed indium, and formation of the exclusion zone for further nucleation can be prevented at the same time. Owing to the bulk phase structure of ZnIn alloy, the indium not only plays a crucial role in Zn deposition, but also improves the Zn stripping. Consequently, the as-designed ZnIn alloy anode can sustain stable Zn stripping/plating for over 2500 h at 4.4 mA cm⁻² with nearly 6 times smaller voltage hysteresis than that of pure Zn. Moreover, it enables a substantially stable ZnIn//NH₄V₄O₁₀ battery with 96.44% capacity retention after 1000 cycles at 5 A g⁻¹. This method of regulating the Zn nucleation by preparing a Zn-based alloy provides a potential solution to the critical problem of Zn dendrite growth and by-product generation fundamentally.

A ZnIn alloy anode with trace indium is obtained by smelting–rolling method. The Zn deposition could be regarded as instantaneous nucleation that can alleviate the effect of deposition layer for the diffusion zone and suppresses dendrite growth.     

In situ monitoring of functional activity of extracellular matrix stiffness-dependent multidrug resistance protein 1 using scanning electrochemical microscopy

Extracellular matrix (ECM) stiffness affects the drug resistance behavior of cancer cells, while multidrug resistance protein 1 (MRP1) on the cell membrane confers treatment resistance via actively transporting drugs out of cancer cells. However, the relationship between ECM stiffness and MRP1 functional activity in cancer cells remains elusive, mainly due to the technical challenge of in situ monitoring. Herein, we engineered in vitro cancer cell models using breast cancer cells (MCF-7 and MDA-MB-231 cells) as the reprehensive cells on polyacrylamide (PA) gels with three stiffness, mimicking different developmental stages of cancer. We in situ characterized the functional activity of MRP1 and investigated the effect of ECM stiffness on MRP1 of cancer cells before and after vincristine treatment using scanning electrochemical microscopy (SECM) with ferrocenecarboxylic acid (FcCOOH) as the redox mediator and endogenous glutathione (GSH) as the indicator. The SECM results show that the functional activity of MRP1 is enhanced with increasing ECM stiffness, and the MRP1-mediated vincristine efflux activity of MCF-7 cells is more affected by ECM stiffness than that of MDA-MB-231 cells. This work, for the first time, applied SECM to in situ and quantitatively monitor the functional activity of MRP1 in cancer cells in different tumor mechanical microenvironments, which could help to elucidate the mechanism of matrix stiffness-dependent drug resistance behavior in cancer cells.

SECM using FcCOOH as the redox mediator and endogenous GSH as the indicator was employed to investigate the effect of extracellular matrix stiffness on the functional activity of MRP1 in cancer cells in situ.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

Packed-Fiber Solid Phase-Extraction Coupled with HPLC-MS/MS for Rapid Determination of Lipid Oxidative Damage Biomarker 8-Iso-Prostaglandin F2α in Urine

The 8-iso-prostaglandin F_2α (8-iso-PGF_2α) biomarker is used as the gold standard for tracing lipid oxidative stress in vivo. The analysis of urinary 8-iso-PGF_2α is challenging when dealing with trace amounts of 8-iso-PGF_2α and the complexity of urine matrixes. A packed-fiber solid-phase extraction (PFSPE)–coupled with HPLC-MS/MS–method, based on polystyrene (PS)-electrospun nanofibers, was developed for the specific determination of 8-iso-PGF_2α in urine and compared with other newly developed LC-MS/MS methods. The method, which simultaneously processed 12 samples within 5 min on a self-made semi-automatic array solid-phase extraction processor, was the first to introduce PS-electrospun nanofibers as an adsorbent for the extraction of 8-iso-PGF_2α and was successfully applied to real urine samples. After optimizing the PFSPE conditions, good linearity in the range of 0.05–5 ng/mL with R² > 0.9996 and a satisfactory limit of detection of 0.015 ng/mL were obtained, with good intraday and interday precision (RSD < 10%) and recoveries of 95.3–103.8%. This feasible method is expected to be used for the batch quantitative analysis of urinary 8-iso-PGF_2α.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.