Construction of a novel near-infrared fluorescent Nile blue@MOF nanoprobe for imaging mitochondrial ATP in living cells.

A near-infrared fluorescent nanoprobe consisting of Nile blue-capped ZIF-90 is first proposed for real-time imaging of mitochondrial ATP. Owing to the strong binding of ATP with Zn2+, the structure of the probe is disrupted, leading to the release of fluorescent NB.

MOF-mediated dual energy transfer nanoprobe integrated with exonuclease III amplification strategy for highly sensitive detection of DNA.

Accurate quantitative detection of DNA is an advanced strategy in various fields (such as disease diagnosis and environmental monitoring), but the classical DNA detection method usually suffers from low sensitivity, expensive thermal cyclers, or strict annealing conditions. Herein, a MOF-ERA platform for ultrasensitive HBV-DNA detection is constructed by integrating metal-organic framework (MOF)-mediated double energy transfer nanoprobe with exonuclease III (Exo III)-assisted target recycling amplification. The proposed double energy transfer containing a donor and two receptors is simply composed of MOFs (UiO-66-NH2, a well-studied MOF) modified with a signal probe formed by the hybridization of carboxyuorescein (FAM)-labeled DNA (FDNA) and black hole quencher (BHQ1)-terminated DNA (QDNA), resulting in low fluorescence signal. After the addition of HBV-DNA, Exo III degradation to FDNA is activated, leading to the liberation of the numerous FAM molecules, followed by the generation of a significant fluorescence signal owing to the negligible binding of MOFs with free FAM molecules. The results certify that the MOF-ERA platform can be successfully used to assay HBV-DNA in the range of 1.0-25.0 nM with a detection limit of 97.2 pM, which is lower than that without BHQ1 or Exo III. The proposed method with the superiorities of low background signal and high selectivity holds promise for early disease diagnosis and clinical biomedicine applications.

Facile Construction of Advanced 1D Metal-Organic Coordination Polymer for Efficient Lithium Storage.

Recently, coordination polymers (CPs) have been frequently reported in the field of energy storage as electrode materials for lithium-ion batteries (LIBs) due to their highly adjustable architectures, which have a variety of active sites and obviously defined lithium transport routes. A well-designed redox-active organic linker with potential active sites for storing lithium ions, pyrazine-2,3-dicarboxylate (H2PDA), was applied for generating CPs by a simple hydrothermal method. When employed as anode materials in LIBs, those two one-dimensional (1D) CPs with an isomorphic composition, [M(PDA)(H2O)2]n (M = Co for Co-PDA and Ni for Ni-PDA), produced outstanding reversible capacities and stable cycling performance. The Co-PDA displays a substantial reversible capacity of 936 mAh g-1 at 200 mA g-1 after 200 cycles, as well as an excellent cycling life at high currents. According to the ex situ characterizations, the high reversible specific capacity of the post-cycled electrodes was found to be a result of both the transition metal ions and the organic ligands, and Co-PDA and Ni-PDA electrode materials show reversible insertion/extraction processes that are accompanied by crystallization to an amorphous state.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

PURPOSE: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). METHODS: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. RESULTS: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. CONCLUSIONS: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Small nodules (≤ 6 mm in diameter) of multiple primary lung cancers: prevalence and management.

BACKGROUND: Synchronous multiple primary lung cancers associated with small non-dominant nodules are commonly encountered. However, the incidence, follow-up, and treatment of small non-dominant tumors have been but little studied. We explored the prevalence and management of small non-dominant tumors and factors associated with interval growth. METHODS: This observational, consecutive, retrospective single-center study enrolled patients diagnosed with synchronous multiple primary lung cancers and small non-dominant tumors (≤ 6 mm in diameter) who underwent resection of the dominant tumor. The incidence, follow-up, and management of small non-dominant tumors and predictors of nodule growth were analyzed. RESULTS: There were 88 patients (12% of all lung cancer patients) with pathological diagnoses of synchronous multiple primary lung cancers. A total of 131 (18%) patients were clinically diagnosed with at least one small (≤ 6 mm in diameter) multiple primary lung cancer non-dominant tumor. 94 patients with 125 small-nodule non-dominant tumors clinically diagnosed as multiple primary lung cancers were followed-up for at least 6 months. A total of 29 (29/125, 23.2%) evidenced small pulmonary nodules (≤ 6 mm in diameter) that exhibited interval growth on follow-up computed tomography (CT). On multivariate analysis, a part-solid nodule (compared to a pGGN) (OR 1.23; 95% CI 1.08-1.40) or a solid nodule (compared to a pGGN) (OR 3.50; 95% CI 1.94-6.30) predicted small nodule interval growth. CONCLUSION: We found a relatively high incidence of multiple primary lung cancers with small non-dominant tumors exhibiting interval growth on follow-up CT, suggesting that resection of non-dominant tumors at the time of dominant tumor resection, especially when the nodules are part-solid or solid, is the optimal treatment.

Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.

OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). MATERIALS AND METHODS: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). RESULTS: In total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824-0.936), 0.915 (95% CI: 0.846-0.959), and 0.914 (95% CI: 0.848-0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. CONCLUSION: The DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

BACKGROUND: Artificial intelligence (AI) products have been widely used for the clinical detection of primary lung tumors. However, their performance and accuracy in risk prediction for metastases or benign lesions remain underexplored. This study evaluated the accuracy of an AI-driven commercial computer-aided detection (CAD) product (InferRead CT Lung Research, ICLR) in malignancy risk prediction using a real-world database. METHODS: This retrospective study assessed 486 consecutive resected lung lesions, including 320 adenocarcinomas, 40 other malignancies, 55 metastases, and 71 benign lesions, from September 2015 to November 2018. The malignancy risk probability of each lesion was obtained using the ICLR software based on a 3D convolutional neural network (CNN) with DenseNet architecture as a backbone (without clinical data). Two resident doctors independently graded each lesion using patient clinical history. One doctor (R1) has 3 years of chest radiology experience, and the other doctor (R2) has 3 years of general radiology experience. Cochran's Q test was used to assess the performances of the AI compared to the radiologists. RESULTS: The accuracy of malignancy-risk prediction using the ICLR for adenocarcinomas, other malignancies, metastases, and benign lesions was 93.4% (299/320), 95.0% (38/40), 50.9% (28/55), and 40.8% (29/71), respectively. The accuracy was significantly higher in adenocarcinomas and other malignancies compared to metastases and benign lesions (all P<0.05). The overall accuracy of risk prediction for R1 was 93.6% (455/486) and 87.4% for R2 (425/486), both of which were higher than the 81.1% accuracy obtained with the ICLR (394/486) (R1 vs. ICLR: P<0.001; R2 vs. ICLR: P=0.001), especially in assessing the risk of metastases (P<0.05). R1 performed better than R2 at risk prediction (P=0.001). CONCLUSIONS: The accuracy of the ICLR for risk prediction is very high for primary lung cancers but poor for metastases and benign lesions.

Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging.

PURPOSE: To evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity. METHODS: We conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals. RESULTS: Median VR max was 18.6% (IQR 9.1-32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4-5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4-5.5%) in severe and 0.4% (IQR 0.1-0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%. CONCLUSION: Five days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS: Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). METHODS: Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). RESULTS: DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. CONCLUSIONS: The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. KEY POINTS: • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule.

PURPOSE: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). METHOD: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. RESULTS: Three factors - radiomics signature, age, and spiculation sign - were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390-0.9931), 0.9342 (95% CI, 0.8944-0.9739), and 0.9064 (95% CI, 0.8639-0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. CONCLUSION: The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.

Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

BACKGROUND: It is a permanent challenge to differentiate small solid lung nodules. Massive data, extracted from medical image through radiomics analysis, may help early diagnosis of lung cancer. The aim of this study was to assess the usefulness of a quantitative radiomic model developed from baseline low-dose computed tomography (LDCT) screening for the purpose of predicting malignancy in small solid pulmonary nodules (SSPNs). METHODS: This retrospective study included malignant and benign SSPNs (6 to 15 mm) detected in baseline low-dose CT screening. The malignancy was confirmed pathologically, and benignity was confirmed by long term follow-up or pathological diagnosis. The non-contrast CT images were reconstructed with a lung kernel of a slice thickness of 1 mm and were processed to extract 385 quantitative radiomic features using Analysis-Kinetic software. A predictive model was established with the training set of 156 benign and 40 malignant nodules, and was tested with the validation set of 77 benign and 21 malignant nodules through the analysis of R square. The performance of the radiomic model in predicting malignancy was compared with that of the ACR Lung Imaging Reporting and Data System (ACR lung-RADS). RESULTS: In 294 cases of SSPNs, 61 lung cancers and 24 benign nodules were confirmed pathologically and the remaining 209 nodules were stable with long-term follow-up (4.1±0.9 years). Eleven non-redundant features, including 8 high-order texture features, were extracted from the training data set. The sensitivity and specificity of the prediction model in malignancy differentiation were 81.0% and 92.2% respectively. The accuracy was superior to ACR-lung RADS (89.8% vs. 76.5%). CONCLUSIONS: A radiomic model based on baseline low-dose CT screening for lung cancer can improve the accuracy in predicting malignancy of SSPNs.

Ordered mesoporous silica cubic particles decorated with silver nanoparticles: a highly active and recyclable heterogeneous catalyst for the reduction of 4-nitrophenol.

In this work, using a modified Stöber process, we synthesized ordered mesoporous silica cubic particles (OMS-C) and prepared a nanocatalyst (Ag-OMS-C) based on OMS-C with a high surface area via an in situ auto-reduction strategy. The as-prepared Ag-OMS-C nanocomposites demonstrated open mesopores (3.51 nm), a large specific surface area (540 m2 g-1) and a high pore volume (0.88 cm3 g-1). The catalytic reduction of 4-nitrophenol (4-NP) over the Ag-OMS-C nanocatalyst was almost complete within 150 s without stirring and the rate constant k (30 × 10-3 s-1) is much higher than those of other substrate-supported Ag nanocatalysts. Moreover, the Ag-OMS-C nanocomposites hold a stable catalytic efficiency over five reaction cycles. The results indicate that the Ag-OMS-C nanocatalyst exhibited high catalytic activity and good reusability toward the reduction of 4-NP, which might be attributed to the large specific surface area, the pore structure of the nanocatalyst, as well as the synergistic effect between OMS-C and AgNPs.

Fluorescent assay for alkaline phosphatase activity based on graphene oxide integrating with λ exonuclease.

A novel fluorescence turn-on strategy for the alkaline phosphatase (ALP) assay is developed based on the preferential binding of graphene oxide (GO) to single-stranded DNA (ssDNA) over double-stranded DNA (dsDNA) coupled with λ exonuclease (λ exo) cleavage. Specifically, in the absence of ALP, the substrate-dsDNA constructed by one oligonucleotide with a fluorophore at the 3'-end (F-DNA) and its complementary sequence modified with a 5'-phosphoryl termini (p-DNA), is promptly cleaved by λ exo, and the resulting F-DNA is adsorbed on GO surface, allowing fluorescence quenching. Whereas the introduction of ALP leads to the hydrolysis of the P-DNA, and the yielding 5'-hydroxyl end product hampers the λ exo cleavage, inducing significant fluorescence enhancement due to the weak binding of dsDNA with GO. Under the optimized conditions, the approach exhibits high sensitivity and specificity to ALP with a detection limit of 0.19 U/L, and the determination of ALP in spiked human serum samples has also been realized. Notably, this new approach not only provides a novel and sensitive platform for the ALP activity detection but also promotes the exploitation of the GO-based biosensing for the detection of the protein with no specific binding element, and thus extending the GO-based sensing applications into a new field.

A fluorescent aptasensor using double-stranded DNA/graphene oxide as the indicator probe.

We developed a fluorescent aptasensor based on the making use of double-stranded DNA (dsDNA)/graphene oxide (GO) as the signal probe and the activities of exonuclease I (Exo I). This method takes advantage of the stronger affinity of the aptamer to its target rather than to its complementary sequence (competitor), and the different interaction intensity of dsDNA, mononucleotides with GO. Specifically, in the absence of target, the competitor hybridizes with the aptamer, preventing the digestion of the competitor by Exo I, and thus the formed dsDNA is adsorbed on GO surface, allowing fluorescence quenching. When the target is introduced, the aptamer preferentially binds with its target. Thereby, the corresponding nuclease reaction takes place, and slight fluorescence change is obtained after the introduction of GO due to the weak affinity of the generated mononucleotides to GO. Adenosine (AD) was chosen as a model system and tested in detail. Under the optimized conditions, smaller dissociation constant (Kd, 311.0 µM) and lower detection limit (LOD, 3.1 µM) were obtained in contrast with traditional dye-labeled aptamer/GO based platform (Kd=688.8 µM, LOD=21.2 µM). Satisfying results were still obtained in the evaluation of the specificity and the detection of AD in human serum, making it a promising tool for the diagnosis of AD-relevant diseases. Moreover, we demonstrated the effect of the competitor on the LOD, and the results reveal that the sensitivity could be enhanced by using the rational competitor. The present design not only constructs a label-free aptamer based platform but also extends the application of dsDNA/GO complex in biochemical and biomedical studies.

Efficient labeling in vitro with non-ionic gadolinium magnetic resonance imaging contrast agent and fluorescent transfection agent in bone marrow stromal cells of neonatal rats.

Although studies have been undertaken on gadolinium labeling-based molecular imaging in magnetic resonance imaging (MRI), the use of non-ionic gadolinium in the tracking of stem cells remains uncommon. To investigate the efficiency in tracking of stem cells with non-ionic gadolinium as an MRI contrast agent, a rhodamine-conjugated fluorescent reagent was used to label bone marrow stromal cells (BMSCs) of neonatal rats in vitro, and MRI scanning was undertaken. The fluorescent-conjugated cell uptake reagents were able to deliver gadodiamide into BMSCs, and cell uptake was verified using flow cytometry. In addition, the labeled stem cells with paramagnetic contrast medium remained detectable by an MRI monitor for a minimum of 28 days. The present study suggested that this method can be applied efficiently and safely for the labeling and tracking of bone marrow stromal cells in neonatal rats.