Metal nanoclusters (NCs) have been developed as a new class of luminescent nanomaterials with potential applications in various fields. However, for most of the metal NCs reported so far, the relatively low photoluminescence quantum yield (QY) in aqueous solution hinders their applications. Here, we describe the utilization of bis-Schiff base linkages to restrict intramolecular motion of surface motifs at the single-cluster level. Based on Au22(SG)18 (SG: glutathione) NCs, an intracluster cross-linking system was constructed with 2,6-pyridinedicarboxaldehyde (PDA), and water-soluble gold NCs with luminescence QY up to 48% were obtained. The proposed approach for achieving high emission efficiency can be extended to other luminescent gold NCs with core-shell structure. Our results also show that the content of surface-bound Au(I)-SG complexes has a significant impact on the PDA-induced luminescence enhancement, and a high ratio of Au(I)-SG will be beneficial to increasing the photoluminescence intensity of gold NCs.

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Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography−mass spectrometrybased nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.