Immune subtype identification and multi-layer perceptron classifier construction for breast cancer.

Introduction: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. Methods: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. Results: By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. Conclusion: Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.

Screening of potential inhibitors targeting the main protease structure of SARS-CoV-2 via molecular docking.

The novel coronavirus disease (COVID-19) caused by SARS-CoV-2 virus spreads rapidly to become a global pandemic. Researchers have been working to develop specific drugs to treat COVID-19. The main protease (Mpro) of SARS-CoV-2 virus plays a pivotal role in mediating viral replication and transcription, which makes it a potential therapeutic drug target against COVID-19. In this study, a virtual drug screening method based on the Mpro structure (Protein Data Bank ID: 6LU7) was proposed, and 8,820 compounds collected from the DrugBank database were used for molecular docking and virtual screening. A data set containing 1,545 drug molecules, derived from compounds with a low binding free energy score in the docking experiment, was established. N-1H-Indazol-5-yl-2-(6-methylpyridin-2-yl)quinazolin-4-amine, ergotamine, antrafenine, dihydroergotamine, and phthalocyanine outperformed the other compounds in binding conformation and binding free energy over the N3 inhibitor in the crystal structure. The bioactivity and ADMET properties of these five compounds were further investigated. These experimental results for five compounds suggested that they were potential therapeutics to be developed for clinical trials. To further verify the results of molecular docking, we also carried out molecular dynamics (MD) simulations on the complexes formed by the five compounds and Mpro. The five complexes showed stable affinity in terms of root mean square distance (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and hydrogen bond. It was further confirmed that the five compounds had potential inhibitory effects on SARS-CoV-2 Mpro.

Electrochemical sensor based on gold nanoparticles fabricated molecularly imprinted polymer film at chitosan-platinum nanoparticles/graphene-gold nanoparticles double nanocomposites modified electrode for detection of erythromycin.

A molecularly imprinted electrochemical sensor was fabricated based on gold electrode decorated by chitosan-platinum nanoparticles (CS-PtNPs) and graphene-gold nanoparticles (GR-AuNPs) nanocomposites for convenient and sensitive determination of erythromycin. The synergistic effects of CS-PtNPs and GR-AuNPs nanocomposites improved the electrochemical response and the sensitivity of the sensor. The molecularly imprinted polymers (MIPs) were prepared by HAuCl(4), 2-mercaptonicotinic acid (MNA) and erythromycin. Erythromycin and MNA were used as template molecule and functional monomer, respectively. They were first assembled on the surface of GR-AuNPs/CS-PtNPs/gold electrode by the formation of Au-S bonds and hydrogen-bonding interactions. Then the MIPs were formed by electropolymerization of HAuCl(4), MNA and erythromycin. The sensor was characterized by cyclic voltammetry (CV), scanning electron microscope (SEM), UV-visible (UV-vis) absorption speactra and amperometry. The linear range of the sensor was from 7.0 × 10(-8)mol/L-9.0 × 10(-5)mol/L, with the limit of detection (LOD) of 2.3 × 10(-8)mol/L (S/N=3). The sensor showed high selectivity, excellent stability and good reproducibility for the determination of erythromycin, and it was successfully applied to the detection of erythromycin in real spiked samples.

A novel label-free electrochemical aptasensor based on graphene-polyaniline composite film for dopamine determination.

A novel label-free electrochemical aptasensor based on graphene-polyaniline (GR-PANI) nanocomposites film for dopamine (DA) determination was reported. The resulting GR-PANI layer exhibited good current response for DA determination. The good electron transfer activity might be attributed to the effect of GR and PANI. The highly conductive and biocompatible nanostructure of GR-PANI nanocomposites was characterized by scanning electron microscopy (SEM), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). To quantify the amount of DA, the peaks of square-wave voltammetry (SWV) were monitored using the redox couple of an [Fe(CN)(6)](4-/3-) probe. The electrochemical aptasensor showed a linear response to DA in the range 0.007-90 nmol/L and a limit of detection of 0.00198 nmol/L (S/N=3). The electrochemical aptasensor was successfully tested on human serum samples.

Electrochemical sensor based on molecularly imprinted film at polypyrrole-sulfonated graphene/hyaluronic acid-multiwalled carbon nanotubes modified electrode for determination of tryptamine.

An imprinted electrochemical sensor based on polypyrrole-sulfonated graphene (PPy-SG)/hyaluronic acid-multiwalled carbon nanotubes (HA-MWCNTs) for sensitive detection of tryptamine was presented. Molecularly imprinted polymers (MIPs) were synthesized by electropolymerization using tryptamine as the template, and para-aminobenzoic acid (pABA) as the monomer. The surface feature of the modified electrode was characterized by cyclic voltammetry (CV). The proposed sensor was tested by chronoamperometry. Several important parameters controlling the performance of the molecularly imprinted sensor were investigated and optimized. The results showed that the PPy-SG composites films showed improved conductivity and electrochemical performances. HA-MWCNTs bionanocomposites could enhance the current response evidently. The good selectivity of the sensor allowed three discriminations of tryptamine from interferents, which include tyramine, dopamine and tryptophan. Under the optimal conditions, a linear ranging from 9.0×10(-8) mol L(-1) to 7.0×10(-5) mol L(-1) for the detection of tryptamine was observed with the detection limit of 7.4×10(-8) mol L(-1) (S/N=3). This imprinted electrochemical sensor was successfully employed to detect tryptamine in real samples.