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.

Rational Fabrication of Nickel Vanadium Sulfide Encapsulated on Graphene as an Advanced Electrode for High-Performance Supercapacitors.

Supercapacitors (SCs) are widely recognized as competitive power sources for energy storage. The hierarchical structure of nickel vanadium sulfide nanoparticles encapsulated on graphene nanosheets (NVS/G) was fabricated using a cost-effective and scalable solvothermal process. The reaction contents of the composites were explored and optimized. TEM images displayed the nickel vanadium sulfide nanoparticles (NVS NPs) with 20-30 nm average size anchored to graphene nanosheets. The interconnection of graphene nanosheets encapsulating NVS nanoparticles effectively reduces the ion diffusion path between the electrode and electrolyte, thereby enhancing electrochemical performance. The NVS/G composite demonstrated improved electrochemical performance, achieving a maximum of 1437 F g-1 specific capacitance at 1 A g-1, remarkable rate capability retaining of 1050 F g-1 at 20 A g-1, and exceptional cycle stability with 91.2% capacitance retention following 10,000 cycles. The NVS/G composite was employed as a cathode, and reduced graphene oxide (rGO) was used as an anode material to assemble a device. Importantly, asymmetric SCs using NVS/G//rGO achieved 74.7 W h kg-1 energy density at 0.8 kW kg-1 power density, along with outstanding stability with 88.2% capacitance retention following 10,000 cycles. These superior properties of the NVS/G electrode highlight its significant potential in energy storage applications.

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.

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.

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.

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.

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.

[Brown tumor: clinical, pathological and imaging manifestations].

OBJECTIVE: To explore the clinical, pathological characteristics and imaging manifestations of brown tumors caused by hyperparathyroidism, to improve the cognition of the disease. METHODS: The clinical, pathological characteristics and imaging manifestations of 15 cases of pathologically confirmed brown tumors were retrospectively reviewed. To analyze the imaging and pathological correlations, in addition, between the primary and secondary hyperparathyroidism led to brown tumors differential diagnoses. Rseults: In the 15 cases, aged from 26 to 66 years with median age of 51 years, 7 cases were caused by primary hyperparathyroidism, 8 cases secondary to long-term hemodialysis. Of the 15 cases(total of 34 lesions), 11 cases were multi-part bone involved. 13 of 15 patients for osteoporosis and 2 cases were osteosclerosis.32 lesions had bone destruction, 26 lesions showed expansive bone destruction, 6 showed soluble bone destruction, 12 lesions had harden edge. 23 lesions in bone destruction had calcification or ossification internal. 13 cases with soft tissue mass, enhanced significantly can be found in 3 cases. 2 lesions occured in the skull, bone density increased and the plate edge blur. pathological findings fibrous tissue , rich in blood vessels, scattered hemosiderin deposition, osteoid tissue around it. CONCLUSION: Brown tumor has relatively specific clinical history and laboratory characteristics. The imaging features include harden edge, in bone destruction has calcification or ossification internal and with soft tissue mass some enhanced significantly. The secondary brown tumor more multi-part destruction and more vertebra were involved than the primary.We can find osteosclerosis in secondary brown tumor. Those characteristic features were important to help for the diagnosis of the brown tumor.

Graphene oxide and metal-mediated base pairs based "molecular beacon" integrating with exonuclease I for fluorescence turn-on detection of biothiols.

A novel fluorescence turn-on strategy, based on the resistance of metal-mediated molecular-beacons (MBs) toward nuclease digestion and the remarkable difference in the affinity of graphene oxide (GO) with MBs and the mononucleotides, is designed for the biothiols assay. Specifically, the metal-mediated base pairs facilitate the dye labeled MBs to fold into a hairpin structure preventing the digestion by exonuclease I, and thus allow the fluorescence quenching. The competition binding by biothiols removes metal ions from the base pairs, causing the nuclease reaction, and less decrease in the fluorescence is obtained after incubating with GO due to the weak affinity of the product-mononucleotides to GO. Hg(2+)-mediated MBs were firstly designed for the biothiols detection, and glutathione (GSH) was applied as the model target. Under the optimal conditions, the approach exhibits high sensitivity to GSH with a detection limit of 1.53 nM. Ag(+)-mediated MBs based sensor was also constructed to demonstrate its versatility, and cysteine was studied as the model target. The satisfactory results in the determination of biothiols in serum demonstrate that the method possesses great potential for detecting thiols in biological fluids. This new approach is expected to promote the exploitation of metal-mediated base pairs-based biosensors in biochemical and biomedical studies.

Influence of three anionic gemini surfactants with different chain lengths on the optical properties of a cationic polyfluorene.

The effects of three sulfonate gemini surfactants with different hydrophobic chain lengths (8, 10, and 12 carbon atoms) on the optical properties of a fluorene-based conjugated cationic polymer poly{[9,9-bis(6'-N,N,N-trimethylammonium)hexyl]-fluorene-phenylene} bromide (PFP) dissolved in DMSO-water solutions (4% v/v) or water were investigated, respectively. When surfactants with PFP dissolved in DMSO-water solutions (4% v/v) are incubated, a decrease in photoluminescence (PL) intensity and a red shift of emission maxima are obtained at low surfactant concentrations. Intriguingly, two different Stern-Volmer constants (KSV1 and KSV2) are obtained and analyzed in detail for the first time. Further increase in the surfactant concentration enhanced PL intensity, and distinct blue shifts of both absorption and emission maxima are observed. Importantly, the turning point between the emission quenching and enhancement is closely related to the hydrophobic chain length: the longer the chain length, the earlier the turning point appears. Simulation studies provide strong evidence to explain these phenomena. Surface tension measurements show more insight on the interactions between PFP and surfactant. On the contrary, no emission quenching is obtained at low surfactant concentrations for PFP dissolved in water.

Correlations between tumor stroma characters and dynamic enhanced MDCT findings in nodular pulmonary adenocarcinoma.

OBJECTIVE: This study aimed to evaluate correlations between tumor stroma characters and dynamic contrast-enhanced computed tomographic (CT) findings in nodular pulmonary adenocarcinoma. METHODS: Thirty-three patients with nodular pulmonary adenocarcinoma underwent dynamic contrast-enhancement CT scan before surgery. CT findings include wash-in, wash-out, and distribution of enhancement. The proportion of invasive and noninvasive stroma in tumor was calculated. RESULTS: Invasive and noninvasive stroma proportion in tumor was correlated positively with wash-in and wash-out enhancement, respectively. CONCLUSIONS: Tumor stroma proliferation may explain the pathologic basis of CT dynamic enhancement and be a useful prognostic factor of pulmonary adenocarcinoma.

[A preliminary study of dual-source computed tomography dual energy imaging in the diagnosis of acute traumatic bone marrow lesions in spine].

OBJECTIVE: To explore the feasibility of virtual noncalcium technique from dual-source computed tomography (DSCT) for the diagnosis of vertebral bone marrow lesions. METHODS: A total of 13 patients with acute vertebral bone marrow lesions underwent both DSCT and MRI within 3 days. And the DSCT dual-energy CT data were postprocessed for generating virtual noncalcium images and color-coded maps. Two radiologists analyzed the lesions of bone marrow by magnetic resonance (MR) imaging and virtual noncalcium images on a three-level. MR imaging interpretation served as the reference standard. Consistency check was conducted by using kappa statistics. And then the sensitivity of DSCT dual energy imaging was examined in the diagnosis of acute traumatic bone marrow lesions in spine. RESULTS: Among them, 13 vertebral body with bone marrow lesions were detected by MRI. The T2WI fat-suppression irregular high signal and slightly high density shadow under the background in noncalcium images corresponded to corresponding high signal areas on MRI. Interreader agreement was substantial for qualitative grading of DE CT images (κ = 0.629). The sensitivity of DSCT dual energy virtual noncalcium images in the diagnosis of acute traumatic bone marrow lesions in spine were 92.3% and 84.6% for observers 1 and 2. CONCLUSION: Distinct traumatic bone marrow lesions of spine may be diagnosed with a high sensitivity on virtual noncalcium images reconstructed from DSCT and color-coded maps. And it is worth further explorations.

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.

Amplified fluorescent sensing of DNA using graphene oxide and a conjugated cationic polymer.

We explore the interactions between a fluorescein (FAM)-labeled single-stranded DNA (P), graphene oxide (GO), and a cationic conjugated polymer, poly [(9,9-bis(6'-N,N,N-trimethylammonium)hexyl)-fluorenylene phenylene dibromide] (PFP). It is found that the fluorescence change of P-GO-PFP system is dependent on the addition order of P and PFP. When adding PFP into P/GO complex, the fluorescence resonance energy transfer (FRET) from PFP to P is inefficient. If P is added to PFP/GO complex, efficient FRET is obtained. This may be attributed to the equal binding ability for P and PFP to GO. The results of time-resolved fluorescence and fluorescence anisotropy support the different fluorescent response under different addition order of P and PFP to GO. Based on the above phenomenon, we demonstrate a method to reduce the high background signal of a traditional PFP-based DNA sensor by introducing GO. In comparison to the use of single PFP, the combination of PFP with GO-based method shows enhanced sensitivity with a detection limit as low as 40 pM for target DNA detection.

Amplified fluorescent assay of potassium ions using graphene oxide and a conjugated cationic polymer.

We present a low background, highly selective and amplified fluorescent sensor for potassium ions using graphene oxide (GO) and a cationic conjugated polymer (CCP). This method takes advantage of the phenomenon that the addition of CCP cannot release the dye labeled guanine-rich DNA from the GO surface, and the conformational switch of the guanine-rich DNA from random coil to G-quadruplex induced by the target.

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.

Surface tension and aggregation properties of novel cationic gemini surfactants with diethylammonium headgroups and a diamido spacer.

A series of novel cationic gemini surfactants with diethylammonium headgroups and a diamido spacer were synthesized, and their surface and bulk properties were investigated by surface tension, electrical conductivity, fluorescence, viscosity, dynamic light scattering (DLS), and transmission electron microscopy (TEM) measurements. An interesting phenomenon, that is, the obvious decline in surface tension upon increasing concentration above the critical micelle concentration (cmc), was found in these gemini surfactant solutions, and two explanations were proposed. This surface tension behavior could be explained by the rapid increase in the counterion activity in the bulk phase or the continued filling of the interface with increasing surfactant concentration above the cmc. More interestingly, not only vesicles but also the surfactant-concentration-induced vesicle to larger aggregate (spongelike aggregate) transition and the salt-induced vesicle and spongelike aggregate to micelle transition were found in the aqueous solutions of these gemini surfactants. The spongelike aggregate that is first reported in the cationic gemini surfactant-water binary system is probably caused by the adhesion and fusion of vesicles at high surfactant concentration.

Magnetic resonance imaging of radiation-induced brain injury using targeted microparticles of iron oxide.

BACKGROUND: Radiation-induced brain injury (RBI) is the most serious complication of primary and metastatic brain and neck malignant tumors following radiation therapy. However, at present, RBI is difficult to diagnose in the early period. Recently, studies have demonstrated that the early stage of RBI is characterized by an inflammatory reaction, and that intercellular adhesion molecule-1 (ICAM-1) is significantly up-regulated in the irradiated brain tissues. PURPOSE: To provide an early diagnosis of RBI using molecular magnetic resonance imaging (MRI) with microparticles of iron oxide (MPIO) targeted to ICAM-1 in the vascular endothelium of brains. MATERIAL AND METHODS: A monoclonal antibody against ICAM-1 was conjugated to MPIO to form the targeted MRI contrast agent ICAM-MPIO. The adhesion of ICAM-MPIO to endothelial cells was quantified by optical imaging and MRI. Sprague-Dawley rats were irradiated to establish an animal model of the early period of RBI. ICAM-MPIO and free-MPIO were injected via tail vein, respectively. T(2) signal intensity and T(2) values of the irradiated brains and normal brains were subsequently evaluated by MRI. RESULTS: In vitro, the adhesion of ICAM-MPIO to the activated endothelial cells was 5 ± 0.5-fold greater than to the non-stimulated cells, which could be detected by optical imaging and MRI (R(2) = 1.0, P < 0.01). In vivo, ICAM-MPIO caused a marked negative MRI contrast effect in irradiated brains. As compared with brains without irradiation, the specific contrast effect increased more than seven-fold after administration of ICAM-MPIO (F = 751.495, P < 0.05). CONCLUSION: MPIO coated with monoclonal antibody of ICAM-1 could be used for detecting the early period of RBI by optical imaging and MRI.

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.

Evaluating the growth of pulmonary nodular ground-glass opacity on CT: comparison of volume rendering and thin slice images.

This study examined the value of volume rendering (VR) interpretation in assessing the growth of pulmonary nodular ground-glass opacity (nGGO). A total of 47 nGGOs (average size, 9.5 mm; range, 5.7-20.6 mm) were observed by CT scanning at different time under identical parameter settings. The growth of nGGO was analyzed by three radiologists by comparing the thin slice (TS) CT images of initial and repeat scans with side-by-side cine mode. One week later synchronized VR images of the two scans were compared by side-by-side cine mode to evaluate the nGGO growth. The nodule growth was rated on a 5-degree scale: notable growth, slight growth, dubious growth, stagnant growth, shrinkage. Growth standard was defined as: Density increase ≥ 30 HU and (or) diameter increase (by 20% in nodules ≥ 10 mm, 30% in nodules of 5-9 mm). Receiver operating characteristic (ROC) was performed. The results showed that 32 nGGOs met the growth criteria (29 nGGOs showed an increase in density; 1 nGGO showed an increase in diameter; 2 nGGOs showed an increase in both diameter and density). Area under ROC curve revealed that the performance with VR interpretation was better than that with TS interpretation (P<0.01, P<0.05 and P<0.05 for observers A, B and C respectively). Consistency between different observers was excellent with both VR interpretation (κ=0.89 for observers A&C, A&B, B&C) and TS interpretation (κ=0.71 for A&B, κ=0.68 for A&C, κ= 0.74 for B&C), but time spending was less with VR interpretation than with TS interpretation (P<0.0001, P<0.0001 and P<0.05 for observers A, B and C, respectively). It was concluded that VR is a useful technique for evaluating the growth of nGGO.