Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.

OBJECTIVES: To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram. MATERIALS AND METHODS: A total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods. RESULTS: The validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found. CONCLUSIONS: Automated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.

Multi-functional GdEuxTb1-xO3 (x = 0 to 1) nanoparticles: colour tuning optical properties, water proton spin relaxivities, and X-ray attenuation properties.

Multi-functional nanoparticles are useful for various applications, such as biomedical imaging, detection, and display technologies. Colour-tunable GdEuxTb1-xO3 nanoparticles were synthesized with emission colour ranging from green (545 nm) to red (616 nm) by varying x (x = 0, 0.1, 0.3, 0.5, 0.7, 0.9, and 1). These nanoparticles were surface-grafted with polyacrylic acid and a small quantity of 2,6-pyridinedicarboxylic acid. This modification aimed to ensure long-term colloidal stability (>1 year without precipitation) and high quantum yields (>30%) in aqueous media. Additionally, they exhibited long emission lifetimes (∼1 ms), high longitudinal water proton spin relaxivities (>30 s-1mM-1), and high X-ray attenuation efficiencies (∼10 HU mM-1). These multiple exceptional properties within a single nanoparticle make them highly valuable for applications in biomedical imaging, noise-free signal detection, and colour display.

Factors influencing the development of artificial intelligence in orthodontics.

OBJECTIVES: Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND METHODS: A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. RESULTS: Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. CONCLUSION: The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.

Efficient Radiolabeling of Proteins and Antibodies via Maleamate-Cysteine Bioconjugation.

The study introduces a novel maleamate-based prosthetic group specifically designed for efficient, site-specific radioiodination of biomolecules that contain or are modified with cysteine residues. This strategy is a compelling alternative to the conventional maleimide-based approach, demonstrating outstanding attributes such as high radiochemical yield, rapid reaction kinetics, applicability in aqueous media at neutral pH, and exceptional stability under a competitive environment.

From molecules to medicine: thiol selective bioconjugation in synthesis of diagnostic and therapeutic radiopharmaceuticals.

Radiolabeling of biomolecules and cells with radiolabeled prosthetic groups has significant implications for nuclear medicine, imaging, and radiotherapy. Achieving site-specific and controlled incorporation of radiolabeled prostheses under mild reaction conditions is crucial for minimizing the impact on the bioactivity of the radiolabeled compounds. The targeting of natural and abundant amino acids during radiolabeling of biomolecules often results in nonspecific and uncontrolled modifications. Cysteine is distinguished by its low natural abundance and unique nucleophilicity. It is therefore an optimal target for site-selective and site-specific radiolabeling of biomolecules under controlled parameters. This review extensively discusses thiol-specific radiolabeled prosthetic groups and provides a critical analysis and comprehensive study of the synthesis of these groups, their in vitro and in vivo stability profiles, reaction kinetics, stability of resulting adducts, and overall impact on the targeting ability of radiolabeled biomolecules. The insights presented here aim to facilitate the development of highly efficient radiopharmaceuticals, initially in preclinical settings and ultimately in clinical applications.

64Cu-Labeled Boron-Containing Cyclic RGD Peptides for BNCT and PET Imaging.

The burgeoning interest in developing boron neutron capture therapy (BNCT) tracers and their accompanying diagnostics for the treatment of recalcitrant tumors has prompted this investigation. Our study aims to devise a tumor treatment strategy utilizing BNCT to target the αvß3 integrin. To this end, we propose a pioneering boron-infused cyclic Arg-Gly-Asp (RGD) peptide, cRGD(d-BPA)K, designed as an efficacious BNCT tracer. Additionally, we introduce its diagnostic complement, DOTA-cRGD(d-BPA)K, tailored for positron emission tomography (PET) to visualize αvß3 expressed tumors. Radiolabeling [64Cu]Cu-DOTA-cRGD(d-BPA)K (64Cu-1) resulted in a high radiochemical yield and purity. The radiotracer exhibited exceptional in vitro stability and demonstrated significant uptake in U87MG tumors via PET imaging. Biodistribution analysis using compound 2 showed a 7.0 ppm accumulation of boron in the U87MG tumor 1 h post-intravenous injection. Furthermore, compound 2 displayed superior tumor/blood (2.41) and tumor/muscle (2.46) ratios compared to the clinically approved l-BPA-fructose. Both compound 2 and its diagnostic counterpart 64Cu-1 hold potential for BNCT and cancer diagnosis, respectively, via molecular imaging.

Bioaccumulation and in vivo tracking of radiolabeled 4-nonylphenol in mice.

4-Nonylphenol (4NP) is concerning due to its growing presence and endocrine-disrupting nature, raising concerns about its impact on health. In this study 124I-labeled 4NP was synthesized for in vivo tracing. Positron emission tomography imaging and biodistribution studies showed significant accumulation in various tissues after oral or intraperitoneal administration, emphasizing its intricate distribution and potential long-term effects, crucial for future risk assessments.

East Asian and Southern European craniofacial class III phenotype: two sides of the same coin?

OBJECTIVES: The skeletal class III phenotype is a heterogeneous condition in populations of different ethnicities. This study aimed to analyse the joint and ethnicity-specific clustering of morphological features in skeletal class III patients of Asian and European origins. MATERIALS AND METHODS: This cross-sectional study involved South Korean and Spanish participants who fulfilled the cephalometric, clinical, and ethnic-related selection criteria. Radiographic records were standardised, calibrated, and measured. A total of 54 skeletal variables were selected for varimax factorial analysis (VFA). Subsequently, a cluster analysis (CA) was performed (mixed method: k-means and hierarchical clustering). Method error and precision were assessed using ICC, Student's t-test, and the Dahlberg formula. RESULTS: A total of 285 Korean and Spanish participants with skeletal class III malocclusions were analysed. After performing VFA and CA, the joint sample revealed three global clusters, and ethnicity-specific analysis revealed four Korean and five Spanish clusters. Cluster_1_global was predominantly Spanish (79.2%) and male (83.01%) and was characterised by a predominantly mesobrachycephalic pattern and a larger cranial base, maxilla, and mandible. Cluster_2_global and Cluster_3_global were mainly South Korean (73.9% and 75.6%, respectively) and depicted opposite phenotypes of mandibular projection and craniofacial pattern. CONCLUSIONS: A distinct distribution of Spanish and South Korean participants was observed in the global analysis. Interethnic and interethnic differences were observed, primarily in the cranial base and maxilla size, mandible projection, and craniofacial pattern. CLINICAL RELEVANCE: Accurate phenotyping, reflecting the complexity of skeletal class III phenotype across diverse populations, is critical for improving diagnostic predictability and future personalised treatment protocols.

Ultrasmall cerium oxide nanoparticles as highly sensitive X-ray contrast agents and their antioxidant effect.

Owing to their theranostic properties, cerium oxide (CeO2) nanoparticles have attracted considerable attention for their key applications in nanomedicine. In this study, ultrasmall CeO2 nanoparticles (particle diameter = 1-3 nm) as X-ray contrast agents with an antioxidant effect were investigated for the first time. The nanoparticles were coated with hydrophilic and biocompatible poly(acrylic acid) (PAA) and poly(acrylic acid-co-maleic acid) (PAAMA) to ensure satisfactory colloidal stability in aqueous media and low cellular toxicity. The synthesized nanoparticles were characterized using high-resolution transmission electron microscopy, X-ray diffraction, Fourier transform-infrared spectroscopy, thermogravimetric analysis, dynamic light scattering, cell viability assay, photoluminescence spectroscopy, and X-ray computed tomography (CT). Their potential as X-ray contrast agents was demonstrated by measuring phantom images and in vivo CT images in mice injected intravenously and intraperitoneally. The X-ray attenuation of these nanoparticles was greater than that of the commercial X-ray contrast agent Ultravist and those of larger CeO2 nanoparticles reported previously. In addition, they exhibited an antioxidant effect for the removal of hydrogen peroxide. The results confirmed that the PAA- and PAAMA-coated ultrasmall CeO2 nanoparticles demonstrate potential as highly sensitive radioprotective or theranostic X-ray contrast agents.

Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.

OBJECTIVES: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND METHODS: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared. RESULTS: On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively. CONCLUSIONS: PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.

Porphyrin-Based Brain Tumor-Targeting Agents: [64Cu]Cu-porphyrin and [64Cu]Cu-TDAP.

The aim of this study is to evaluate a radioactive metal complex platform for brain tumor targeting. Herein, we introduce a new porphyrin derivative, 5,10,15,20-(tetra-N,N-dimethyl-4-aminophenyl)porphyrin (TDAP), in which four N,N-dimethyl-4-p-phenylenediamine (DMPD) moieties are conjugated to the porphyrin labeled with the radiometal 64Cu. DMPD affected the pharmacokinetics of porphyrin in terms of retention time in vivo and tumor-targeting ability relative to those of unmodified porphyrin. [64Cu]Cu-TDAP showed stronger enhancement than [64Cu]Cu-porphyrin in U87MG glioblastoma cells, especially in the cytoplasm and nucleus, indicating its tumor-targeting properties and potential use as a therapeutic agent. In the subcutaneous and orthotopic models of brain-tumor-bearing mice, [64Cu]Cu-TDAP was clearly visualized in the tumor site via positron emission tomography imaging and showed a tumor-to-brain ratio as high as 13. [64Cu]Cu-TDAP deserves attention as a new diagnostic agent that is suitable for the early diagnosis and treatment of brain tumors.

Prognostic value of baseline and early treatment response of neutrophil-lymphocyte ratio, C-reactive protein, and lactate dehydrogenase in non-small cell lung cancer patients undergoing immunotherapy.

Background: Not all non-small cell lung cancer (NSCLC) patients will benefit from immune checkpoint therapy and use of these medications carry serious autoimmune adverse effects. Therefore, biomarkers are needed to better identify patients who will benefit from its use. Here, the correlation of overall survival (OS) with baseline and early treatment period serum biomarker responses was evaluated in patients with NSCLC undergoing immunotherapy. Methods: Patients diagnosed with NSCLC undergoing immunotherapy (n=597) at a tertiary academic medical center in South Korea were identified between January 2010 and November 2021. The neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and lactate dehydrogenase (LDH) levels in the survival and non-survival groups were examined at baseline and early treatment periods. Additionally, aberrant laboratory parameters at each period were used to stratify survival curves and examine their correlation with one-year OS. Results: In the non-survival group, the NLR, CRP, and LDH levels at the early treatment period were higher than those at the baseline (P<0.001). The survival curves stratified based on aberrant laboratory findings in each period varied (log-rank test P<0.001). Multivariate Cox regression analysis revealed that having prescribed more than 3rd line of chemotherapy [hazard ratio (HR) =3.19, 95% confidence interval (CI): 1.04-9.82; P=0.043] and early treatment period CRP (HR =3.88; 95% CI: 1.55-9.72; P=0.004) and LDH (HR =4.04; 95% CI: 2.01-8.12; P<0.001) levels were significant predictors of one-year OS. Conclusions: Early treatment period CRP and LDH levels were significant predictors of OS in patients with NSCLC undergoing immunotherapy.

Investigation of the Relationship Between Psychiatry Visit and Suicide After Deliberate Self-harm: Longitudinal National Cohort Study.

BACKGROUND: Deliberate self-harm (DSH) along with old age, physical disability, and low socioeconomic status are well-known contributors to suicide-related deaths. In recent years, South Korea has the highest suicide death rate among all Organization for Economic Co-operation and Development countries. Owing to the difficulty of accessing data of individuals with DSH behavior who died by suicide, the factors associated with suicide death in these high-risk individuals have not been sufficiently explored. There have been conflicting findings with regard to the relationship between previous psychiatric visits and suicidal death. OBJECTIVE: We aimed to address the following 3 questions: Are there considerable differences in demographics, socioeconomic status, and clinical features in individuals who received psychiatric diagnosis (either before DSH or after DSH event) and those who did not? Does receiving a psychiatric diagnosis from the Department of Psychiatry, as opposed to other departments, affect survival? and Which factors related to DSH contribute to deaths by suicide? METHODS: We used the Korean National Health Insurance Service Database to design a cohort of 5640 individuals (3067/5640, 54.38% women) who visited the hospital for DSH (International Classification of Diseases codes X60-X84) between 2002 and 2020. We analyzed whether there were significant differences among subgroups of individuals with DSH behavior based on psychiatric diagnosis status (whether they had received a psychiatric diagnosis, either before or after the DSH event) and the department from which they had received the psychiatric diagnosis. Another main outcome of the study was death by suicide. Cox regression models yielded hazard ratios (HRs) for suicide risk. Patterns were plotted using Kaplan-Meier survival curves. RESULTS: There were significant differences in all factors including demographic, health-related, socioeconomic, and survival variables among the groups that were classified according to psychiatric diagnosis status (P<.001). The group that did not receive a psychiatric diagnosis had the lowest survival rate (867/1064, 81.48%). Analysis drawn using different departments from where the individual had received a psychiatric diagnosis showed statistically significant differences in all features of interest (P<.001). The group that had received psychiatric diagnoses from the Department of Psychiatry had the highest survival rate (888/951, 93.4%). These findings were confirmed using the Kaplan-Meier survival curves (P<.001). The severity of DSH (HR 4.31, 95% CI 3.55-5.26) was the most significant contributor to suicide death, followed by psychiatric diagnosis status (HR 1.84, 95% CI 1.47-2.30). CONCLUSIONS: Receiving psychiatric assessment from a health care professional, especially a psychiatrist, reduces suicide death in individuals who had deliberately harmed themselves before. The key characteristics of individuals with DSH behavior who die by suicide are male sex, middle age, comorbid physical disabilities, and higher socioeconomic status.

Facile Synthesis and X-ray Attenuation Properties of Ultrasmall Platinum Nanoparticles Grafted with Three Types of Hydrophilic Polymers.

Ultrasmall platinum nanoparticles (Pt-NPs) grafted with three types of hydrophilic and biocompatible polymers, i.e., poly(acrylic acid), poly(acrylic acid-co-maleic acid), and poly(methyl vinyl ether-alt-maleic acid) were synthesized using a one-pot polyol method. Their physicochemical and X-ray attenuation properties were characterized. All polymer-coated Pt-NPs had an average particle diameter (davg) of 2.0 nm. Polymers grafted onto Pt-NP surfaces exhibited excellent colloidal stability (i.e., no precipitation after synthesis for >1.5 years) and low cellular toxicity. The X-ray attenuation power of the polymer-coated Pt-NPs in aqueous media was stronger than that of the commercial iodine contrast agent Ultravist at the same atomic concentration and considerably stronger at the same number density, confirming their potential as computed tomography contrast agents.

Measurement of extracellular volume fraction using magnetic resonance-based conductivity tensor imaging.

Conductivity tensor imaging (CTI) using MRI is an advanced method that can non-invasively measure the electrical properties of living tissues. The contrast of CTI is based on underlying hypothesis about the proportionality between the mobility and diffusivity of ions and water molecules inside tissues. The experimental validation of CTI in both in vitro and in vivo settings is required as a reliable tool to assess tissue conditions. The changes in extracellular space can be indicators for disease progression, such as fibrosis, edema, and cell swelling. In this study, we conducted a phantom imaging experiment to test the feasibility of CTI for measuring the extracellular volume fraction in biological tissue. To mimic tissue conditions with different extracellular volume fractions, four chambers of giant vesicle suspension (GVS) with different vesicle densities were included in the phantom. The reconstructed CTI images of the phantom were compared with the separately-measured conductivity spectra of the four chambers using an impedance analyzer. Moreover, the values of the estimated extracellular volume fraction in each chamber were compared with those measured by a spectrophotometer. As the vesicle density increased, we found that the extracellular volume fraction, extracellular diffusion coefficient, and low-frequency conductivity decreased, while the intracellular diffusion coefficient slightly increased. On the other hand, the high-frequency conductivity could not clearly distinguish the four chambers. The extracellular volume fraction measured by the spectrophotometer and CTI method in each chamber were quite comparable, i.e., (1.00, 0.98 ± 0.01), (0.59, 0.63 ± 0.02), (0.40, 0.40 ± 0.05), and (0.16, 0.18 ± 0.02). The prominent factor influencing the low-frequency conductivity at different GVS densities was the extracellular volume fraction. Further studies are needed to validate the CTI method as a tool to measure the extracellular volume fractions in living tissues with different intracellular and extracellular compartments.

VdistCox: Vertically distributed Cox proportional hazards model with hyperparameter optimization.

Vertically partitioned data is distributed data in which information about a patient is distributed across multiple sites. In this study, we propose a novel algorithm (referred to as VdistCox) for the Cox proportional hazards model (Cox model), which is a widely-used survival model, in a vertically distributed setting without data sharing. VdistCox with a single hidden layer feedforward neural network through extreme learning machine can build an efficient vertically distributed Cox model. VdistCox can tune hyperparameters, including the number of hidden nodes, activation function, and regularization parameter, with one communication between the master site, which is the site set to act as the server in this study, and other sites. In addition, we explored the randomness of hidden layer input weights and biases by generating multiple random weights and biases. The experimental results indicate that VdistCox is an efficient distributed Cox model that reflects the characteristics of true centralized vertically partitioned data in the model and enables hyperparameter tuning without sharing information about a patient and additional communication between sites.

Early Prediction of Radiation-Induced Pulmonary Fibrosis Using Gastrin-Releasing Peptide Receptor-Targeted PET Imaging.

Early diagnosis of radiation-induced pulmonary fibrosis (RIPF) in lung cancer patients after radiation therapy is important. A gastrin-releasing peptide receptor (GRPR) mediates the inflammation and fibrosis after irradiation in mice lungs. Previously, our group synthesized a GRPR-targeted positron emission tomography (PET) imaging probe, [64Cu]Cu-NODAGA-galacto-bombesin (BBN), an analogue peptide of GRP. In this study, we evaluated the usefulness of [64Cu]Cu-NODAGA-galacto-BBN for the early prediction of RIPF. We prepared RIPF mice and acquired PET/CT images of [18F]F-FDG and [64Cu]Cu-NODAGA-galacto-BBN at 0, 2, 5, and 11 weeks after irradiation (n = 3-10). We confirmed that [64Cu]Cu-NODAGA-galacto-BBN targets GRPR in irradiated RAW 264.7 cells. In addition, we examined whether [64Cu]Cu-NODAGA-galacto-BBN monitors the therapeutic efficacy in RIPF mice (n = 4). As a result, the lung uptake ratio (irradiated-to-normal) of [64Cu]Cu-NODAGA-galacto-BBN was the highest at 2 weeks, followed by its decrease at 5 and 11 weeks after irradiation, which matched with the expression of GRPR and was more accurately predicted than [18F]F-FDG. These uptake results were also confirmed by the cell uptake assay. Furthermore, [64Cu]Cu-NODAGA-galacto-BBN could monitor the therapeutic efficacy of pirfenidone in RIPF mice. We conclude that [64Cu]Cu-NODAGA-galacto-BBN is a novel PET imaging probe for the early prediction of RIPF-targeting GRPR expressed during the inflammatory response.

Flavonoid-Conjugated Gadolinium Complexes as Anti-Inflammatory Theranostic Agents.

In this study, we designed, synthesized, and evaluated gadolinium compounds conjugated with flavonoids as potential theranostic agents for the treatment of inflammation. These novel theranostic agents combine a molecular imaging agent and one of three flavonoids (galangin, chrysin, and 7-hydroxyflavone) as anti-inflammatory drugs as a single integrated platform. Using these agents, MR imaging showed contrast enhancement (>10 in CNR) at inflamed sites in an animal inflammation model, and subsequent MR imaging used to monitor the therapeutic efficacy of these integrated agents revealed changes in inflamed regions. The anti-inflammatory effects of these agents were demonstrated both in vitro and in vivo. Furthermore, the antioxidant efficacy of the agents was evaluated by measuring their reactive oxygen species scavenging properties. For example, Gd-galangin at 30 µM showed a three-fold higher ROS scavenging of DPPH. Taken together, our findings provide convincing evidence to indicate that flavonoid-conjugated gadolinium compounds can be used as potentially efficient theranostic agents for the treatment of inflammation.