Chemodynamic therapy (CDT) utilizes iron‐initiated Fenton chemistry to destroy tumor cells by converting endogenous H2O2 into the highly toxic hydroxyl radical (.OH). There is a paucity of ...Fenton‐like metal‐based CDT agents. Intracellular glutathione (GSH) with .OH scavenging ability greatly reduces CDT efficacy. A self‐reinforcing CDT nanoagent based on MnO2 is reported that has both Fenton‐like Mn2+ delivery and GSH depletion properties. In the presence of HCO3−, which is abundant in the physiological medium, Mn2+ exerts Fenton‐like activity to generate .OH from H2O2. Upon uptake of MnO2‐coated mesoporous silica nanoparticles (MS@MnO2 NPs) by cancer cells, the MnO2 shell undergoes a redox reaction with GSH to form glutathione disulfide and Mn2+, resulting in GSH depletion‐enhanced CDT. This, together with the GSH‐activated MRI contrast effect and dissociation of MnO2, allows MS@MnO2 NPs to achieve MRI‐monitored chemo–chemodynamic combination therapy.
Self‐reinforcing weapon: The Fenton‐like Mn2+ delivery and glutathione (GSH) depletion abilities of MnO2 allow it to exert enhanced chemodynamic efficacy in cancer treatment. An activatable theranostic platform based on multifunctional MnO2‐coated mesoporous silica nanoparticles (MS@MnO2 NPs) has been developed for MRI‐monitored combination chemotherapy and chemodynamic therapy (CDT). ADS=antioxidant defense system.
Since December 2019, an epidemic caused by novel coronavirus (2019-nCoV) infection has occurred unexpectedly in China. As of 8 pm, 31 January 2020, more than 20 pediatric cases have been reported in ...China. Of these cases, ten patients were identified in Zhejiang Province, with an age of onset ranging from 112 days to 17 years. Following the latest
National recommendations for diagnosis and treatment of pneumonia caused by 2019-nCoV
(the 4th edition) and current status of clinical practice in Zhejiang Province, recommendations for the diagnosis and treatment of respiratory infection caused by 2019-nCoV for children were drafted by the National Clinical Research Center for Child Health, the National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine to further standardize the protocol for diagnosis and treatment of respiratory infection in children caused by 2019-nCoV.
Purpose
Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) ...and deep learning based on CT images to predict MVI preoperatively.
Methods
In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.
Results
Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (
p
= 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (
p
= 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months,
p
< 0.001; 3D-CNN Model: 64.06 vs. 31.05 months,
p
= 0.027).
Conclusion
The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.
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•Core-double-shell architecture was designed as efficient HER and OER electrocatalysts.•Cobalt phosphide and NiFe-LDH were used as representative HER and OER catalysts.•The ...architecture consist of core–shell porous carbon fiber (CFC@EC) and TMCs.•Lattice distortions were created in the TMCs by CFC@EC, facilitating the exposure of active sites.•Enhanced performance is due to the strong electronic interaction between the hybrids.
Different transition metal compounds (TMCs) nanostructures grown on conductive substrates have been considered as promising self-supportive non-precious electrocatalysts for electrochemical water splitting, but extremely challenging to develop facile and generalized approaches for rational design and enhancing their catalytic properties. Herein, we develop a general strategy to boost the hydrogen and oxygen evolution reactions (HER and OER) performance of TMCs by designing monolith electrocatalyst architectures. The monoliths comprises of TMCs integrated on carbon fiber cloth core–shell (CFC@EC) structure. The CFC@EC allows the creation of numerous lattice distortions and strong electronic interactions between CFC@EC and metal cations of the TMCs. Such lattice distortions exposes more active sites in CFC@EC/TMCs compared to the pristine CFC coated TMCs (CFC/TMC). Cobalt phosphide (CoP) nanowires and NiFe-LDH coated on CFC@EC exhibits the optimized HER and OER activities. Overall water splitting device assembled based on the optimized HER and OER electrodes also achieve low overall potential of 1.53 V at 10 mA cm−2. More importantly, we further experimentally verify that the integration of Ni3N and Ni3S2, CoS2, NiCo-LDH, NiMn-LDH with CFC@EC also reveal similar improved performance, providing a general and valuable strategy into the design of other self-supporting electrocatalysts for water splitting and beyond.
The low n-doping efficiency of conjugated polymers with the molecular dopants limits their availability in electrical conductivity, thermoelectrics, and other electric applications. Recently, ...considerable efforts have focused on improving the ionization of dopants by modifying the structures of host polymers or n-dopants; however, the effect of ionized dopants on the electrical conductivity and thermoelectric performance of the polymers is still a puzzle. Herein, we try to reveal the role of molecular dopant cations on carrier transport through the systematic comparison of two n-dopants, TAM and N-DMBI-H. These two n-dopants exhibit various doping features with the polymer due to their different chemical structure characteristics. For instance, while doping, TAM negligibly perturbs the polymer backbone conformation and microstructural ordering; then after ionization, TAM cations possess weak π-backbone affinity but strong intrinsic affinity with side chains, which enables the doped system to screen the Coulomb potential spatially. Such doping features lead to high carrierization capabilities for TAM-doped polymers and further result in an excellent conductivity of up to 22 ± 2.5 S cm–1 and a power factor of over 80 μW m–1 K–2, which are significantly higher than the state of the art values of the common n-dopant N-DMBI-H. More importantly, this strategy has also proven to be widely applicable in other doped polymers. Our investigations indicate the vital role of dopant counterions in high electrical and thermoelectric performance polymers and also suggest that, without sacrificing Seebeck coefficients, high conductivities can be realized with precise regulation of the interaction between the cations and the host.
Abstract
Electroreduction of carbon dioxide (CO
2
) into multicarbon products provides possibility of large-scale chemicals production and is therefore of significant research and commercial ...interest. However, the production efficiency for ethanol (EtOH), a significant chemical feedstock, is impractically low because of limited selectivity, especially under high current operation. Here we report a new silver–modified copper–oxide catalyst (dCu
2
O/Ag
2.3%
) that exhibits a significant Faradaic efficiency of 40.8% and energy efficiency of 22.3% for boosted EtOH production. Importantly, it achieves CO
2
–to–ethanol conversion under high current operation with partial current density of 326.4 mA cm
−2
at −0.87 V vs reversible hydrogen electrode to rank highly significantly amongst reported Cu–based catalysts. Based on in situ spectra studies we show that significantly boosted production results from tailored introduction of Ag to optimize the coordinated number and oxide state of surface Cu sites, in which the
*
CO adsorption is steered as both atop and bridge configuration to trigger asymmetric C–C coupling for stablization of EtOH intermediates.
By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication ...for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.
Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains ...within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas.
The blood-brain barrier (BBB) plays a vital role in the protection and maintenance of homeostasis in the brain. In this way, it is an interesting target as an interface for various types of drug ...delivery, specifically in the context of the treatment of several neuropathological conditions where the therapeutic agents cannot cross the BBB. Drug toxicity and on-target specificity are among some of the limitations associated with current neurotherapeutics. In recent years, advances in nanodrug delivery have enabled the carrier system containing the active therapeutic drug to target the signaling pathways and pathophysiology that are closely linked to central nervous system (CNS) disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), multiple sclerosis (MS), brain tumor, epilepsy, ischemic stroke, and neurodegeneration. At present, among the nano formulations, solid lipid nanoparticles (SLNs) have emerged as a putative drug carrier system that can deliver the active therapeutics (drug-loaded SLNs) across the BBB at the target site of the brain, offering a novel approach with controlled drug delivery, longer circulation time, target specificity, and higher efficacy, and more importantly, reducing toxicity in a biomimetic way. This paper highlights the synthesis and application of SLNs as a novel nontoxic formulation strategy to carry CNS drugs across the BBB to improve the use of therapeutics agents in treating major neurological disorders in future clinics.
A metal-free and efficient visible-light-induced spirocyclization of indolyl-ynones with diselenides at room temperature under air atmosphere to prepare 3-selenospiroindolenines in moderate to good ...yields has been developed. The resulting products were tested for in vitro anticancer activity by MTT assay, and compounds 3 c and 3 e showed potent cancer cell-growth inhibition activities.
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