Preventing Donepezil-Induced Adverse Effects Through N-acetylcysteine Co-Administration.

Background: Drug-induced adverse symptoms affect patients' quality of life (QoL) during treatment. Understanding the underlying mechanisms of drug-induced adverse effects could help prevent them. As current drugs have limited effects in halting the progress of Alzheimer's disease (AD), patients are required to take these drugs over a long period. The main obstacles to long-term compliance are drug-elicited side effects that deteriorate patient QoL. Objective: Donepezil, the most popular acetylcholinesterase inhibitor (AChEI) drug for AD, induces various side effects, especially at high doses. This study aimed to identify a drug that can attenuate the side effects of donepezil and investigate the underlying mechanisms. Methods: Five-week-old Sprague-Dawley rats received daily oral donepezil and N-acetylcysteine (NAC) for four weeks. General symptoms following administration were monitored daily to address drug-related adverse effects. Cytosolic calcium influx and generation of reactive oxygen species (ROS) after drug treatment were measured in vitro using C2C12 myotubes. Results: High-dose donepezil induced numerous adverse symptoms in male and female rats, which were markedly attenuated by co-treatment with NAC. NAC significantly reduced both acute and chronic muscle-related symptoms caused by donepezil. Additionally, in vitro studies showed that high-dose donepezil increased ROS and intracellular calcium ([Ca2+]i) levels in muscle cells, contributing to these adverse effects. NAC co-treatment dramatically reduced ROS and [Ca2+]i levels in muscle cells. Conclusions: Combined treatment with NAC effectively diminishes the adverse effects elicited by donepezil by regulating ROS and [Ca2+]i levels in the skeletal muscle, which could contribute to improving donepezil treatment in patients.

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.

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.

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.

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.

Therapeutic Effects of Combination of Nebivolol and Donepezil: Targeting Multifactorial Mechanisms in ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by progressive loss of motor neurons in the spinal cord. Although the disease's pathophysiological mechanism remains poorly understood, multifactorial mechanisms affecting motor neuron loss converge to worsen the disease. Although two FDA-approved drugs, riluzole and edaravone, targeting excitotoxicity and oxidative stress, respectively, are available, their efficacies are limited to extending survival by only a few months. Here, we developed combinatorial drugs targeting multifactorial mechanisms underlying key components in ALS disease progression. Using data analysis based on the genetic information of patients with ALS-derived cells and pharmacogenomic data of the drugs, a combination of nebivolol and donepezil (nebivolol-donepezil) was identified for ALS therapy. Here, nebivolol-donepezil markedly reduced the levels of cytokines in the microglial cell line, inhibited nuclear factor-κB (NF-κB) nucleus translocation in the HeLa cell and substantially protected against excitotoxicity-induced neuronal loss by regulating the PI3K-Akt pathway. Nebivolol-donepezil significantly promoted the differentiation of neural progenitor cells (NPC) into motor neurons. Furthermore, we verified the low dose efficacy of nebivolol-donepezil on multiple indices corresponding to the quality of life of patients with ALS in vivo using SOD1G93A mice. Nebivolol-donepezil delayed motor function deterioration and halted motor neuronal loss in the spinal cord. Drug administration effectively suppressed muscle atrophy by mitigating the proportion of smaller myofibers and substantially reducing phospho-neurofilament heavy chain (pNF-H) levels in the serum, a promising ALS biomarker. High-dose nebivolol-donepezil significantly prolonged survival and delayed disease onset compared with vehicle-treated mice. These results indicate that the combination of nebivolol-donepezil efficiently prevents ALS disease progression, benefiting the patients' quality of life and life expectancy.

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.

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.

Development of Brain-Tumor-Targeted Benzothiazole-Based Boron Complex for Boron Neutron Capture Therapy.

Boron neutron capture therapy (BNCT) is a precision treatment technology that ideally damages only boron-accumulating cells. The effectiveness of BNCT depends on the amount of boron in the tumor cells and the concentration ratio between normal and tumor tissues. Therefore, for successful brain-tumor treatment using BNCT, it is essential to develop a drug with high blood-brain barrier (BBB) permeability and high tumor accumulation. The benzothiazole-based boron complex 4-(benzo[d]thiazol-2-yl)phenylboronic acid (BTPB) is a hydrophobic, low-molecular-weight compound that has shown high BBB permeability and brain accumulation. The highest boron concentration of BTPB is 36.11 ± 2.73 µg/g (at 1 h post-injection) in the brain, and the highest brain/blood ratio is 3.94 ± 0.46 (at 2 h post-injection), which is sufficient for the BNCT drug condition. In addition, BTPB showed good tumor-targeting ability in vivo in a U87MG glioma tumor model. In this study, we conducted a biological evaluation of BTPB compared to boronophenylalanine as a novel drug for BNCT.

Feasibility of Gd-Based prostate cancer targeted magnetic resonance agents using prostate specific membrane antigen.

The aim of this work was to evaluate Gd-FC705, a prostate-specific membrane antigen (PSMA)-targeted MRI contrast agent. The r1 and r2 relaxivities of Gd-FC705 are 5.94 mM-1s-1 and 17.77 mM-1s-1, respectively, in HSA solution (0.67 mM) at 3 T, which are higher than those of Gd-DOTA. Specific targeting efficacy was found with a 3-fold enhancement between PSMA-negative (PSMA-) and PSMA-positive (PSMA+) cells. The in vivo targeting and bio-distribution of Gd-FC705 were further confirmed using nude mice bearing PC3 human prostate cancer xenografts, which showed a 2-fold increase in the contrast-to-noise ratio (CNR) for PSMA+ tumors compared to PSMA- tumors 1 h post injection and a longer circulation time than Gd-DOTA. These results demonstrate that Gd-FC705 has great potential as a diagnostic agent for prostate cancer.

Effect of Structural Fine-Tuning on Chelate Stability and Liver Uptake of Anionic MRI Contrast Agents.

The purpose of this study is to assess the physicochemical properties and MRI diagnostic efficacy of two newly synthesized 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-type Gd chelates, Gd-SucL and Gd-GluL, with an asymmetric α-substituted pendant arm as potential hepatocyte-specific magnetic resonance imaging contrast agents (MRI CAs). Our findings show that fine conformational changes in the chelating arm affect the in vivo pharmacokinetic behavior of the MRI CA, and that a six-membered chelating substituent of Gd-SucL is more advantageous in this system to avoid unwanted interactions with endogenous species. Gd-SucL exhibited a general DOTA-like chelate stability trend, indicating that all chelating arms retain coordination bonding. Finally, the in vivo diagnostic efficacy of highly stable Gd-SucL as a potential hepatocyte-specific MRI CA was evaluated using T1-weighted MR imaging on an orthotopic hepatocarcinoma model.

Time series analysis.

INTRODUCTION: This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R. METHODS: Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected. Time series analysis was performed by applying Language R software. The data sets and codes were provided for tutorial and illustrative purposes. RESULTS: Using time series decomposition, the trend component and the seasonal variation were separated and visualized graphically. CONCLUSIONS: Time series analysis may be helpful to clinicians by providing a simple tool to evaluate patient characteristics and manage the practice.

Assessment of reliability in orthodontic literature.

OBJECTIVES: To map the statistical methods applied to assess reliability in orthodontic publications and to identify possible trends over time. MATERIALS AND METHODS: Original research articles published in 2009 and 2019 in a subset of orthodontic journals were downloaded. Publication characteristics, including publication year, number of authors, single vs multicenter study, geographic origin of the study, statistician involvement, study category, subject category, types of reliability assessment, and statistical methods applied to assess reliability, were recorded. Descriptive statistics, Chi-square tests, and logistic regression analyses were performed to investigate associations between reliability analysis and study characteristics. RESULTS: A total of 768 original research articles were analyzed. The most prevalent study category was observational (69%) with a statistician involved in 16% of studies. Overall, reliability was assessed in 47% of studies, and the most frequent methods applied to assess reliability were intraclass correlation coefficients or kappa statistics (60.4%). The odds of applying appropriate methods were greater in 2019 than in 2009 (odds ratio [OR]: 2.43; 95% confidence interval [CI]: 1.75, 3.37; P < .001). Involvement of a statistician resulted in greater odds of applying appropriate methods compared to no statistician involvement (OR: 1.88; 95% CI: 1.23, 2.87; P < .01). CONCLUSIONS: Over the past decade (2009 vs 2019), reliability assessment became more common in the orthodontic literature, and studies applying correct statistical methods to assess reliability significantly increased. This trend was more apparent in studies that involved a statistician, which may highlight the role of the statistician.

Contemporary gendered pathways into adulthood in South Korea.

The transition to adulthood has become increasingly uncertain and variable. Among South Koreans, this transition has become more de-standardized since 1990, reflecting the effects of long-term economic stagnation and persistent, traditional gender norms, but little is known about the variability in pathways to adulthood among recent cohorts. This study employs sequence analysis to examine early life course trajectories between the ages of 19 and 35 and assess gender and cohort differences for South Koreans born between 1970 and 1985 (N = 8647), using the Korean Labor and Income Panel Study (KLIPS, Wave 1-23). The main results show that pathways into adulthood have become more varied in the current socioeconomic and cultural contexts in South Korea, particularly for women compared to men. At the same time, new gender-specific pathways into adulthood have appeared, while the traditional, distinctly gendered breadwinner-homemaker trajectory has declined.

Image-Based Evaluation of Irradiation Effects in Brain Tissues by Measuring Absolute Electrical Conductivity Using MRI.

Radiation-induced injury is damage to normal tissues caused by unintentional exposure to ionizing radiation. Image-based evaluation of tissue damage by irradiation has an advantage for the early assessment of therapeutic effects by providing sensitive information on minute tissue responses in situ. Recent magnetic resonance (MR)-based electrical conductivity imaging has shown potential as an effective early imaging biomarker for treatment response and radiation-induced injury. However, to be a tool for evaluating therapeutic effects, validation of its reliability and sensitivity according to various irradiation conditions is required. We performed MR-based electrical conductivity imaging on designed phantoms to confirm the effect of ionizing radiation at different doses and on in vivo mouse brains to distinguish tissue response depending on different doses and the elapsed time after irradiation. To quantify the irradiation effects, we measured the absolute conductivity of brain tissues and calculated relative conductivity changes based on the value of pre-irradiation. The conductivity of the phantoms with the distilled water and saline solution increased linearly with the irradiation doses. The conductivity of in vivo mouse brains showed different time-course variations and residual contrast depending on the irradiation doses. Future studies will focus on validation at long-term time points, including early and late delayed response and evaluation of irradiation effects in various tissue types.

Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI.

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.

Porphyrin-Based Tumor-Targeting Theranostic Agent: Gd-TDAP.

The aim of this work was to evaluate a tumor-targeting porphyrin-based gadolinium complex (Gd-TDAP) for use as an MR/optical imaging agent and potential therapeutic agent. Gd-TDAP had higher longitudinal relaxivity (11.8 mM-1 s-1) than a commercial MRI contrast agent (Omniscan; 3.7 mM-1 s-1) in HSA solution (0.67 mM) at 3 T. The tumor-targeting characteristics were confirmed by T1-weighted MR imaging and optical imaging using an orthotopic brain tumor mouse model, which showed 1.3-fold higher uptake in tumor compared to normal brain tissues. The cell fraction data using U87MG glioblastoma cells indicated the potential for gadolinium neutron capture therapy (Gd-NCT), which requires gadolinium to be inside the cell nucleus. In addition, porphyrin derivatives can be used for photodynamic therapy (PDT), and the results demonstrated that Gd-TDAP has great potential not only as a bimodal imaging agent but also for treatment.