Nomogram to determine predictive risk for active tuberculosis based on the QuantiFERON-TB Gold In-Tube test.

Interferon-γ release assay (IGRA) is a widely used blood test for detecting TB infection. However, a positive result of IGRA cannot differentiate active tuberculosis (ATB) infection from inactive tuberculosis (IATB). In this study, we established a nomogram model for predictive risk of ATB, differentiated from IATB, based on the concentration of interferon-γ (IFN-γ) of QuantiFERON-TB Gold In-Tube Test (QFT-GIT) and clinical characteristics. Participants with a positive QFT-GIT result were recruited and divided into a training and validation cohort according to hospitalisation date. The nomogram model for the differential diagnosis of ATB from IATB was established according to gender, age, pleural effusion (PE), and the concentration of IFN-γ in the Nil, TB antigen, and mitogen tube of QFT-GIT in the training cohort by logistic regression and validated in the validation cohort, and then combined with adenosine deaminase (ADA) to evaluated the performance value in ATB cases with PE. The area under receiver operating characteristic curve (AUC) of the diagnostic nomogram model, which we called the NSMC-ATB model for ATB diagnosis was 0.819 (95% CI 0.797-0.841), with sensitivity 73.16% and specificity 75.95% in training cohort, and AUC was 0.785 (95% CI 0.744-0.827), with sensitivity 67.44% and specificity 75.14% in validation cohort. A combination of the NSMC-ATB model and ADA performed better than the NSMC-ATB model and ADA alone in predicting ATB cases with PE, as AUC was 0.903 (95% CI 0.856-0.950) with sensitivity 78.63% and specificity 87.50%. We established an effective diagnostic nomogram model, called the NSMC-ATB model to differentiate ATB from IATB. Meanwhile, the combination of the NSMC-ATB model and ADA improved the performance value of ATB with PE.

LncRNA NEAT1/miR-185-5p/IGF2 axis regulates the invasion and migration of colon cancer.

BACKGROUND: Long noncoding RNAs (lncRNA) are important in the growth and metastasis of colon cancer. The objective of this study was to describe the potential role of lncRNA NEAT1 in the progression of colon cancer. METHODS: Quantitative real-time polymerase chain reaction was used for detecting NEAT1, miR-185-5p, and IGF2 in colon cancer cells and tissues. The potential diagnostic value of NEAT1 in colon cancer was analyzed with the receiver operating characteristic curve. Kaplan-Meier method was applied for evaluating the association between NEAT1 expression and the overall survival of osteosarcoma patients, whereas Transwell assay was introduced to examine the potential invasion and migration of colon cancer cells. In addition, the binding of NEAT1/IGF2 to miR-185-5p was confirmed by RNA pull-down and RNA-binding protein immunoprecipitation assays and dual-luciferase reporter gene assay. Finally, rescue experiments were conducted to confirm the role of NEAT1/miR-185-5p/IGF2 axis in colon cancer. RESULTS: Colon cancer patients with low NEAT1 expression presented with longer overall survival than those with high expression. The migration and invasion of colon cancer cells were considerably promoted by overexpressed NEAT1. Both NEAT1 and IGF2 bound to miR-185-5p. CONCLUSION: NEAT1 upregulate IGF2 expression through absorbing miR-185-5p to enhances the migration and invasion of colon cancer cells.

Cryodesiccation-driven crystallization preparation approach for zinc(II)-phthalocyanine nanodots in cancer photodynamic therapy and photoacoustic imaging.

Multifunctional nanodots represent an emerging platform for overcoming the delivery challenges of poorly water-soluble drugs for use in the diagnosis and treatment of cancer. The authors describe the preparation of nanocrystallites composed of the water-insoluble photosensitizer zinc(II)-phthalocyanine in the form of nanodots by applying a cryodesiccation-driven crystallization approach. Modification of the surface of the nanodots with Pluronic F127 and folic acid endows them with excellent water solubility and stealth properties in blood. Under near-infrared (NIR) photoexcitation at 808 nm, the nanodots are shown to produce singlet oxygen, which is widely used in photodynamic therapy of cancer. The nanodots exhibit strong NIR absorbance at 808 nm and can be used as a non-toxic contrast agent for photoacoustic imaging of tissue. Graphical abstract Schematic presentation of the preparation of ZnPcNDs by droplet-confined/cryodesiccation-driven crystallization.

Exosome-like Nanozyme Vesicles for H2O2-Responsive Catalytic Photoacoustic Imaging of Xenograft Nasopharyngeal Carcinoma.

Photoacoustic imaging (PAI) is an attractive imaging modality, which is promising for clinical cancer diagnosis due to its advantages on deep tissue penetration and fine spatial resolution. However, few tumor catalytic/responsive PAI strategies are developed. Here, we design an exosome-like nanozyme vesicle for in vivo H2O2-responsive PAI of nasopharyngeal carcinoma (NPC). The intrinsic peroxidase-like activity of graphene quantum dot nanozyme (GQDzyme) effectively converts the 2,2'-azino- bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) into its oxidized form in the presence of H2O2. The oxidized ABTS exhibits strong near-infrared (NIR) absorbance, rendering it to be an ideal contrast agent for PAI. Thus, GQDzyme/ABTS nanoparticle is a novel type of catalytic PAI contrast agent, which is sensitive to H2O2 produced from NPC cells. Furthermore, we develop an approach to construct exosome-like nanozyme vesicle via biomimetic functionalization of GQDzyme/ABTS nanoparticle with natural erythrocyte membrane modified with folate acid. In vivo animal experiments demonstrated that this exosome-like nanozyme vesicle effectively accumulated in NPC and selectively triggered catalytic PAI for NPC. In addition, our nanozyme vesicle exhibits excellent biocompatibility and stealth ability for long blood circulation. Together, we demonstrate that GQDzyme/ABTS based exosome-like nanozyme vesicle is an ideal nanoplatform for developing deep-tissue tumor-targeted catalytic PAI in vivo.