Screening of identification algorithm for rodent-induced bare patches based on the drone imagery.

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.

Accelerated proton dissociation in an excited state induces superacidic microenvironments around graphene quantum dots.

Investigating proton transport at the interface in an excited state facilitates the mechanistic investigation and utilization of nanomaterials. However, there is a lack of suitable tools for in-situ and interfacial analysis. Here we addresses this gap by in-situ observing the proton transport of graphene quantum dots (GQDs) in an excited state through reduction of magnetic resonance relaxation time. Experimental results, utilizing 0.1 mT ultra-low-field nuclear magnetic resonance relaxometry compatible with a light source, reveal the light-induced proton dissociation and acidity of GQDs' microenvironment in the excited state (Hammett acidity function: -13.40). Theoretical calculations demonstrate significant acidity enhancement in -OH functionalized GQDs with light induction ( p K a * = -4.62, stronger than that of H2SO4). Simulations highlight the contributions of edge and phenolic -OH groups to proton dissociation. The light-induced superacidic microenvironment of GQDs benefits functionalization and improves the catalytic performances of GQDs. Importantly, this work advances the understanding of interfacial properties of light-induced sp2-sp3 carbon nanostructure and provides a valuable tool for exploring catalyst interfaces in photocatalysis.

Nitrogen-Doped 3D-Graphene Advances Near-Infrared Photodetector for Logic Circuits and Image Sensors Overcoming 2D Limitations.

The limitations of two-dimensional (2D) graphene in broadband photodetector are overcome by integrating nitrogen (N) doping into three-dimensional (3D) structures within silicon (Si) via plasma-assisted chemical vapor deposition (PACVD) technology. This contributes to the construction of vertical Schottky heterojunction broad-spectrum photodetectors and applications in logic devices and image sensors. The natural nanoscale resonant cavity structure of 3D-graphene enhances photon capture efficiency, thereby increasing photocarrier generation. N-doping can fine-tune the electronic structure, advancing the Schottky barrier height and reducing dark current. The as-fabricated photodetector exhibits exceptional self-driven photoresponse, especially at 1550 nm, with an excellent photoresponsivity (79.6 A/W), specific detectivity (1013 Jones), and rapid response of 130 µs. Moreover, it enables logic circuits, high-resolution pattern image recognition, and broadband spectra recording across the visible to near-infrared range (400-1550 nm). This research will provide new views and technical support for the development and widespread application of high-performance semiconductor-based graphene broadband detectors.

Hybrid ant colony-based inter-cluster routing protocol for FANET.

This study addresses the challenges in large-scale unmanned aerial vehicle (UAV) clusters, specifically the scalability issues and limitations of using reactive routing protocols for inter-cluster routing. These traditional methods place an excessive burden on cluster heads and struggle to adapt to frequently changing topologies, leading to decreased network performance. To solve these problems, we propose an innovative inter-cluster routing protocol (ICRP), which is based on a hybrid ant colony algorithm. During the route establishment phase, ICRP uses this algorithm to identify the optimal relay node. This approach is inspired by the foraging behavior of Physarum polycephalum, combining factors such as the number of hops from the source node, the load condition of the node, and its weight in the pheromone calculation. In the route maintenance phase, ICRP uses a predictive repair and contraction mechanism to dynamically maintain routes, accommodating the high mobility of UAVs. Comparative simulations in OMNeT + + showed that this protocol surpasses ad-hoc on-demand distance vector (AODV), fuzzy-logic-assisted-AODV, and Enhanced-Ant-AODV routing protocols in packet delivery rate and end-to-end transmission delay. Furthermore, it showed superior adaptation to network environments with high-speed node mobility.

The Balloon Catheter Method and the End-hole Catheter Method in the Measurement of Hepatic Venous Pressure Gradient: a Comparative Study.

OBJECTIVE: This study aims to evaluate the differences between The balloon catheter method and End-hole Catheter Method in measuring hepatic venous pressure gradient (HVPG) among cirrhosis patients. METHODS: From October 2017 to January 2024, patients who underwent HVPG measurements using both methods were consecutively included. HVPGs obtained from both methods were compared with the portal vein pressure gradient (PPG) obtained via transjugular intrahepatic portosystemic shunt (TIPS) using paired comparisons. Additionally, the consistency and predictive ability for bleeding risk of the two methods, as well as the impact of intrahepatic veno-venous shunt (IHVS), were analyzed. RESULTS: The study enrolled 145 patients, each of whom had HVPG measured by both methods. PPG was measured in 61 patients. There was a statistically significant difference between the PPGs and HVPGs measured by both the balloon catheter method and the end-hole catheter method (P < 0.001), with the HVPG mean values obtained by the end-hole catheter method being closer to the PPGs. In the non-IHVS group, no significant statistical difference was found between the two methods (P = 0.071). In contrast, the IHVS group showed a significant difference (P < 0.001), with a mean difference of 2.98 ± 4.03 mmHg. When IHVS was absent, the measurement results from the end-hole catheter method and the balloon catheter method were found to be highly correlated. The end-hole catheter method has a higher screening capability for patients at risk of bleeding compared to the balloon catheter method (75.90% vs. 72.86%). CONCLUSION: HVPG measurements using either the balloon catheter method or end-hole catheter method showed significant difference with the PPG. The end-hole catheter method has a higher screening capability for patients at risk of bleeding, and IHVS could lead to lower HVPG measurements with The balloon catheter method.

A Tape-Wrapping Strategy towards Electrochemical Fabrication of Water-Dispersible Graphene.

Graphene has achieved mass production via various preparative routes and demonstrated its uniqueness in many application fields for its intrinsically high electron mobility and thermal conductivity. However, graphene faces limitations in assembling macroscopic structures because of its hydrophobic property. Therefore, balancing high crystal quality and good aqueous dispersibility is of great importance in practical applications. Herein, we propose a tape-wrapping strategy to electrochemically fabricate water-dispersible graphene (w-Gr) with both excellent dispersibility (~4.5 mg/mL, stable over 2 months), and well-preserved crystalline structure. A large production rate (4.5 mg/min, six times faster than previous electrochemical methods), high yield (65.4% ≤5 atomic layers) and good processability are demonstrated. A mechanism investigation indicates that the rational design of anode configuration to ensure proper oxidation, deep exfoliation and unobstructed mass transfer is responsible for the high efficiency of this strategy. This simple yet efficient electrochemical method is expected to promote the scalable preparation and applications of graphene.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT.

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.

PURPOSE: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm. METHODS: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI. RESULTS: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727). CONCLUSION: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach.

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Rationale for immune checkpoint inhibitors plus targeted therapy for advanced renal cell carcinoma.

Renal cell carcinoma (RCC) is a frequent urological malignancy characterized by a high rate of metastasis and lethality. The treatment strategy for advanced RCC has moved through multiple iterations over the past three decades. Initially, cytokine treatment was the only systemic treatment option for patients with RCC. With the development of medicine, antiangiogenic agents targeting vascular endothelial growth factor and mammalian target of rapamycin and immunotherapy, immune checkpoint inhibitors (ICIs) have emerged and received several achievements in the therapeutics of advanced RCC. However, ICIs have still not brought completely satisfactory results due to drug resistance and undesirable side effects. For the past years, the interests form researchers have been attracted by the combination of ICIs and targeted therapy for advanced RCC and the angiogenesis and immunogenic tumor microenvironmental variations in RCC. Therefore, we emphasize the potential principle and the clinical progress of ICIs combined with targeted treatment of advanced RCC, and summarize the future direction.

The Synergistic Effect on the Mimetic Optical Structure of Feline Eyes toward Household Health Monitoring of Acute and Chronic Diseases.

A breakthrough in the performance of bionic optical structures will only be achieved if we can obtain an in-depth understanding of the synergy mechanisms operating in natural optical structures and find ways to imitate them. In this work, inspired by feline eyes, an optical substrate that takes advantage of a synergistic effect that occurs between resonant and reflective structures was designed. The synergistic effect between the reflective and resonant components leads to a Raman enhancement factor (EF) of 1.16 × 107, which is much greater than that achieved using the reflective/resonant cavities on their own. Finite-difference time-domain (FDTD) simulations and experimental results together confirm that the mechanism of this synergistic effect is achieved by realizing multiple reflections and repeated absorptions of light, generating a strong local electric field. Thus, a 2-3 order of magnitude increase in sensitivity could be achieved. More importantly, with the homemade centrifugal device, above optical substrates were further used to develop a rapidly highly sensitive household health monitoring system (detection time <3 min). It can thus be used to give early warning of acute diseases with high risk (e.g., acute myocardial infarction (AMI) and cerebral peduncle). Due to the good reusability and storability (9% and 8% reduction in EF after washing 30 times and 9 months of storage, respectively) of the substrates, the substrates thus reduce detection costs (to ∼$1), making them much cheaper to use than the current gold-standard methods (e.g., ∼$16 for gout detection).

Symmetry-Triggered Tunable Phosphorescence Lifetime of Graphene Quantum Dots in a Solid State.

Studying the phosphorescent mechanisms of carbon nanostructures synthesized by the "bottom-up" approach is key to understanding the structure modulation and the interfacial properties of carbon nanostructures. In this work, the relationships among symmetry of precursors in the "bottom-up" synthesis, structures of products, and phosphorescence lifetimes of graphene quantum dots (GQDs) are studied. The symmetry matching of precursors in the formation of a D6h graphene-like framework is considered the key factor in controlling the separability of sp2 domains in GQDs. As the separability of sp2 domains in GQDs increases, the phosphorescence lifetimes (14.8-125.5 ms) of GQDs in the solid state can be tuned. Machine learning is used to define the degree of disorder (S) of the GQD structure, which quantitatively describes the different space groups of precursors. The negative correlation between S and the oscillator strength of GQDs is uncovered. Therefore, S can be recognized as reflective of oscillator strength in the GQD structure. Finally, based on the correlations found between the structures and phosphorescence lifetimes of GQDs, GQDs with an ultralong phosphorescence lifetime (28.5 s) are obtained. Moreover, GQDs with visible phosphorescence emission (435-618 nm) are synthesized.

Fast proton transport enables the magnetic relaxation response of graphene quantum dots for monitoring the oxidative environment in vivo.

A magnetic relaxation switch (MRS) that targets small molecules such as H2O2 is difficult to realize because of the small size of the targets, which cannot gather enough MRS probes to form aggregates and generate a difference in magnetic relaxation times. Therefore, the development of small molecule-targeted MRS is strongly dependent on changes in the interfacial structure of the probe, which modulates the proton transport behavior near the probe. Herein, functionalized graphene quantum dots (GQDs) consisting of GQDs with disulfide bonds, polyethylene glycol (PEG), and paramagnetic Gd3+ were used as the MRS probe to sense H2O2. The structure of GQDs changed after reacting with H2O2. The PEG assembled a tube for transmitting changes in GQDs via proton transport and thus enabled the magnetic relaxation response of the probe towards H2O2. Pentaethylene glycol was experimentally and theoretically proven to have the strongest ability to transport protons. Such a probe can be applied in the differentiation of healthy and senescent cells/tissues using in vitro fluorescent imaging and in vivo magnetic resonance imaging. This work provides a reliable solution for building a proton transport route, which not only enables the response of the MRS probe towards the targets but also demonstrates the design of carbon nanostructures with proton transport behaviors.

Correlation between computed tomography-based body composition parameters and hepatic venous pressure gradient in patients with cirrhosis: a systematic review and meta-analysis.

PURPOSE: Computed tomography (CT)-based body composition parameters and the hepatic venous pressure gradient (HVPG) are key characteristics in patients with liver cirrhosis. The present study aims to explore the correlation between CT-based body composition parameters and HVPG, as well as the difference in HVPG between patients with and patients without sarcopenia. METHODS: A literature search for studies reporting the correlation between HVPG and CT-based body composition parameters published in English up to August 2023 in four databases, Embase, MEDLINE (via PubMed), Web of Science, and Cochrane Library, was conducted. The correlation coefficient between HVPG and CT-based body composition parameters was the primary outcome, and the difference in the HVPG value between the sarcopenia and non-sarcopenia groups was the secondary outcome. A meta-analysis was conducted using a random-effects models. The methodologic quality was assessed using the Quality Assessment of Diagnostic Studies instrument. RESULTS: A total of 652 articles were identified, of which nine studies (n = 1,569) met the eligibility criteria. Among them, seven studies reported the primary outcome via the muscle index, five via the skeletal muscle index (SMI), two via the psoas-muscle-related index (PRI), and three via two adipose tissue indexes. A total of five studies reported the secondary outcome: four via SMI and one via PRI. No evidence of a significant correlation was determined between the various body composition parameters and the HVPG value, either in the muscle index or the adipose tissue index. Higher HVPG values were observed in patients with sarcopenia than in patients without sarcopenia [pooled standardized mean difference (SMD): 0.628 (-0.350, 1.606), P < 0.001; I2 = 92.8%; P < 0.001] when an Asian sarcopenia definition was adopted. In contrast, when a Western cut-off value was applied, the HVPG value was higher in patients without sarcopenia than in patients with sarcopenia [pooled SMD: -0.201 (-0.366, -0.037), P = 0.016; I2 = 0.00%; P = 0.785]. CONCLUSION: No sufficient evidence regarding a correlation between the CT-based body composition and HVPG value was discovered. The difference in the HVPG value between the sarcopenia and non-sarcopenia groups was likely dependent on the sarcopenic cut-off value.

Glasgow prognostic score and its derived scores predicts contrast-associated acute kidney injury in patients undergoing coronary angiography.

Background: Glasgow prognostic score (GPS) is a reliable scoring system reflecting both nutritional and inflammatory factors. The association of inflammation and nutrition with contrast-associated acute kidney injury (CA-AKI) has been validated. This study set out to determine the impact of GPS and its derived scores on CA-AKI incidence. Methods: Populations treated with coronary angiography with/without percutaneous coronary intervention were screened retrospectively. According to C-reactive protein and albumin, three kinds of GPSs were involved: GPS, modified GPS (mGPS), and the cutoff-based GPS (cGPS) which was derived by calculating the optimal cutoff values of two parameters. Primary endpoint was CA-AKI. Pearson' r correlation, linear/logistic regression, receiver operating characteristic curve as well as subgroup analyses were conducted. Results: Totally, 3150 patients were valid for analysis, and the mean age was 67.5 years old, with 66.4 % male. Of these, 610 patients suffered CA-AKI. All three kinds of GPSs were independently associated with the SCr elevation proportion (GPS: ß = 4.850, 95%CI [3.700 to 8.722], P < 0.001; mGPS: ß = 3.450, 95%CI [1.896 to 6.888], P = 0.001; cGPS: ß = 3.992, 95%CI [2.368 to 6.940], P < 0.001). GPS, mGPS and cGPS were proved to be the independent risk factors for CA-AKI risk (all P for trend <0.05). Compared with GPS and mGPS, cGPS was of greater prognostic value for predicting CA-AKI incidence (cGPS: AUC = 0.633; mGPS: AUC = 0.567; GPS: AUC = 0.611). Main findings were also consistent in all subgroup analysis. Conclusion: Preprocedural GPS and its derived scores (mGPS and cGPS), especially cGPS, were correlated with the incidence of CA-AKI, which might assist in clinical decision making in treating CA-AKI.

Comparison of quantitative volumetric analysis and linear measurement for predicting the survival of Barcelona Clinic Liver Cancer 0- and A stage hepatocellular carcinoma after radiofrequency ablation.

PURPOSE: The prognostic role of the tumor volume in patients with hepatocellular carcinoma (HCC) at the Barcelona Clinic Liver Cancer (BCLC) 0 and A stages remains unclear. This study aims to compare the volumetric measurement with linear measurement in early HCC burden profile and clarify the optimal cut-off value of the tumor volume. METHODS: The consecutive patients diagnosed with HCC who underwent initial and curative-intent radiofrequency ablation (RFA) were included retrospectively. The segmentation was performed semi-automatically, and enhanced tumor volume (ETV) as well as total tumor volume (TTV) were obtained. The patients were categorized into high- and low-tumor burden groups according to various cutoff values derived from commonly used diameter values, X-tile software, and decision-tree analysis. The inter- and intra-reviewer agreements were measured using the intra-class correlation coefficient. Univariate and multivariate time-to-event Cox regression analyses were performed to identify the prognostic factors of overall survival. RESULTS: A total of 73 patients with 81 lesions were analyzed in the whole cohort with a median follow-up of 31.0 (interquartile range: 16.0­36.3). In tumor segmentation, excellent consistency was observed in intra- and inter-reviewer assessments. There was a strong correlation between diameter-derived spherical volume and ETV as well as ETV and TTV. As opposed to all linear candidates and 4,188 mm3 (sphere equivalent to 2 cm in diameter), ETV >14,137 mm3 (sphere equivalent to 3 cm in diameter) or 23,000 mm3 (sphere equivalent to 3.5 cm in diameter) was identified as an independent risk factor of survival. Considering the value of hazard ratio and convenience to use, when ETV was at 23,000 mm3, it was regarded as the optimal volumetric cut-off value in differentiating survival risk. CONCLUSION: The volumetric measurement outperforms linear measurement on tumor burden evaluation for survival stratification in patients at BCLC 0 and A stages HCC after RFA.

Fabrication of Carbon-Based Quantum Dots via a "Bottom-Up" Approach: Topology, Chirality, and Free Radical Processes in "Building Blocks".

The discovery of carbon-based quantum dots (CQDs) has allowed opportunities for fluorescence bioimaging, tumor diagnosis and treatment, and photo-/electro-catalysis. Nevertheless, in the existing reviews related to the "bottom-up" approaches, attention is mainly paid to the applications of CQDs but not the formation mechanism of CQDs, which mainly derived from the high complexities during the synthesis of CQDs. Among the various synthetic methods, using small molecules as "building blocks", the development of a "bottom-up" approach has promoted the structural design, modulation of the photoluminescence properties, and control of the interfacial properties of CQDs. On the other hand, many works have demonstrated the "building blocks"-dependent properties of CQDs. In this review, from one of the most important variables, the relationships among intrinsic properties of "building blocks" and photoluminescence properties of CQDs are summarized. The topology, chirality, and free radical process are selected as descriptors for the intrinsic properties of "building blocks". This review focuses on the induction and summary of recent research results from the "bottom-up" process. Moreover, several empirical rules pertaining thereto are also proposed.

Raman spectral classification algorithm of cephalosporin based on VGGNeXt.

In recent years, deep learning has been widely used in the field of Raman spectral classification. However, the majority of the training and test sets are generated by the same device (generally a portable Raman spectrometer), with little difference between them, and the trained model may not be directly applicable to other devices. In this study, we established a database of six cephalosporin Raman spectra and proposed a classification algorithm VGGNeXt for cephalosporin Raman spectra. VGGNeXt takes inspiration from ConvNeXt, borrows some tricks from Swin-T, and re-improves VGG. Training data were high-resolution spectra from a benchtop Raman spectrometer, and test data were low-resolution spectra from a portable Raman spectrometer. The impact of preprocessing and dataset size on algorithm accuracy was explored. The results show that our network outperforms other comparative algorithms in all cases. After preprocessing, the VGGNeXt model achieves 100% accuracy on both full and halved data sets, and 99.9% accuracy when there are only 10 data for each cephalosporin class. The results show that the experimental ideas and processing methods in this paper solve the problems of model transfer and instrument standardization to a certain extent, and the model has good robustness.

Mechanism of the Fe(iii)-catalyzed synthesis of hexahydropyrimidine with α-phenylstyrene: a DFT study.

It is very important to develop multiple C-H substitution reactions of simple alkenes to obtain complex unsaturated components. The present study focuses on a theoretical investigation of the plausible mechanism in the Fe(OTf)3-catalyzed tandem amidomethylative reactions of α-phenylstyrene. Bis(tosylamido)methane is activated by Fe(OTf)3 to form tosylformaldimine and its Fe(OTf)3-adduct. The Fe(OTf)3-adduct undergoes an intermolecular aza-Prins reaction with α-phenylstyrene to form allylamide. The DFT data support the formation of the hexahydropyrimidine derivative from allylamide, and "condensation/iminium homologation/intramolecular aza-Prins" is the optimal reaction path. At the same time, a possible reaction pathway for the conversion of the hydrolysate 1,3-diamide derivative to the hexahydropyrimidine (HHP) derivative is given. This work is thus instructive for understanding Fe(iii)-based tandem catalysis for the amidomethylative multiple-substitution reactions of alkenes.

Synergistic Effect of Oxygen- and Nitrogen-Containing Groups in Graphene Quantum Dots: Red Emitted Dual-Mode Magnetic Resonance Imaging Contrast Agents with High Relaxivity.

Contrast agents (CAs) in magnetic resonance imaging generally involve the dissociative Gd3+. Because of the limited ligancy of Gd3+, the balance between Gd3+ coordination stability (reducing the concentration of dissociative Gd3+) and increases in the number of coordination water molecules (enhancing the relaxivity) becomes crucial. Herein, the key factor of the synergistic effect between the O- and N-containing groups of graphene quantum dots for the structural design of CAs with both high relaxivity and low toxicity was obtained. The nitrogen-doped graphene quantum dots (NGQDs) with an O/N ratio of 0.4 were selected to construct high-relaxivity magnetic resonance imaging (MRI)-fluorescence dual-mode CAs. The coordination stability of Gd3+ can be increased through the synergetic coordination of O- and N-containing groups. The synergetic coordination of O- and N-containing groups can result in the short residency time of the water ligand and achieve high relaxivity. The resulting CAs (called NGQDs-Gd) exhibit a high relaxivity of 32.04 mM-1 s-1 at 114 µT. Meanwhile, the NGQDs-Gd also emit red fluorescence (614 nm), which can enable the MRI-fluorescence dual-mode imaging as the CAs. Moreover, the bio-toxicity and tumor-targeting behavior of NGQDs-Gd were also evaluated, and NGQDs-Gd show potential in MRI-fluorescence imaging in vivo.