Single-molecule observation of G-quadruplex and R-loop formation induced by transcription.

Potential G-quadruplex forming sequences (PQS) are enriched in cancer-related genes and immunoglobulin class-switch recombination. They are prevalent in the 5'UTR of transcriptionally active genes, thereby contributing to the regulation of gene expression. We and others previously demonstrated that the PQS located in the non-template strand leads to an R-loop formation followed by a G-quadruplex (G4) formation during transcription. These structural changes increase mRNA production. Here, we present how single-molecule technique was used to observe cotranscriptional G4 and R-loop formation and to examine the impact on transcription, particularly for the initiation and elongation stages.

Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury.

Drug-induced liver injury (DILI) presents significant diagnostic challenges, and recently artificial intelligence-based deep learning technology has been used to predict various hepatic findings. In this study, we trained a set of Mask R-CNN-based deep algorithms to learn and quantify typical toxicant induced-histopathological lesions, portal area, and connective tissue in Sprague Dawley rats. We compared a set of single-finding models (SFMs) and a combined multiple-finding model (MFM) for their ability to simultaneously detect, classify, and quantify multiple hepatic findings on rat liver slide images. All of the SFMs yielded mean average precision (mAP) values above 85%, suggesting that the models had been successfully established. The MFM showed better performance than the SFMs, with a total mAP value of 92.46%. We compared the model predictions for slide images with ground-truth annotations generated by an accredited pathologist. For the MFM, the overall and individual finding predictions were highly correlated with the annotated areas, with R-squared values of 0.852, 0.952, 0.999, 0.990, and 0.958 being obtained for portal area, infiltration, necrosis, vacuolation, and connective tissue (including fibrosis), respectively. Our results indicate that the proposed MFM could be a useful tool for detecting and predicting multiple hepatic findings in basic non-clinical study settings.

A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.

Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s43188-023-00173-5.

Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat.

BACKGROUND: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3+, and SSD, to detect hepatic fibrosis. RESULTS: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3+ also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). CONCLUSIONS: We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies.

Preparing pathological data to develop an artificial intelligence model in the nonclinical study.

Artificial intelligence (AI)-based analysis has recently been adopted in the examination of histological slides via the digitization of glass slides using a digital scanner. In this study, we examined the effect of varying the staining color tone and magnification level of a dataset on the result of AI model prediction in hematoxylin and eosin stained whole slide images (WSIs). The WSIs of liver tissues with fibrosis were used as an example, and three different datasets (N20, B20, and B10) were prepared with different color tones and magnifications. Using these datasets, we built five models trained Mask R-CNN algorithm by a single or mixed dataset of N20, B20, and B10. We evaluated their model performance using the test dataset of three datasets. It was found that the models that were trained with mixed datasets (models B20/N20 and B10/B20), which consist of different color tones or magnifications, performed better than the single dataset trained models. Consequently, superior performance of the mixed models was obtained from the actual prediction results of the test images. We suggest that training the algorithm with various staining color tones and multi-scaled image datasets would be more optimized for consistent remarkable performance in predicting pathological lesions of interest.

Application of convolutional neural network for analyzing hepatic fibrosis in mice.

Recently, with the development of computer vision using artificial intelligence (AI), clinical research on diagnosis and prediction using medical image data has increased. In this study, we applied AI methods to analyze hepatic fibrosis in mice to determine whether an AI algorithm can be used to analyze lesions. Whole slide image (WSI) Sirius Red staining was used to examine hepatic fibrosis. The Xception network, an AI algorithm, was used to train normal and fibrotic lesion identification. We compared the results from two analyses, that is, pathologists' grades and researchers' annotations, to observe whether the automated algorithm can support toxicological pathologists efficiently as a new apparatus. The accuracies of the trained model computed from the training and validation datasets were greater than 99%, and that obtained by testing the model was 100%. In the comparison between analyses, all analyses showed significant differences in the results for each group. Furthermore, both normalized fibrosis grades inferred from the trained model annotated the fibrosis area, and the grades assigned by the pathologists showed significant correlations. Notably, the deep learning algorithm derived the highest correlation with the pathologists' average grade. Owing to the correlation outcomes, we conclude that the trained model might produce results comparable to those of the pathologists' grading of the Sirius Red-stained WSI fibrosis. This study illustrates that the deep learning algorithm can potentially be used for analyzing fibrotic lesions in combination with Sirius Red-stained WSIs as a second opinion tool in non-clinical research.

Cytosine methylation regulates DNA bendability depending on the curvature.

Cytosine methylation plays an essential role in many biological processes, such as nucleosome inactivation and regulation of gene expression. The modulation of DNA mechanics may be one of the regulatory mechanisms influenced by cytosine methylation. However, it remains unclear how methylation influences DNA mechanics. Here, we show that methylation has contrasting effects on the bending property of dsDNA depending on DNA curvature. We directly applied bending force on 30 base pairs of dsDNA using a D-shaped DNA nanostructure and measured the degree of bending using single-molecule fluorescence resonance energy transfer without surface immobilization. When dsDNA is weakly bent, methylation increases the stiffness of dsDNA. The stiffness of dsDNA increased by approximately 8% with a single methylation site for 30 bp dsDNA. When dsDNA is highly bent by a strong force, it forms a kink, i.e., a sharp bending of dsDNA. Under strong bending, methylation destabilizes the non-kink form compared with the kink form, which makes dsDNA near the kink region apparently more bendable. However, if the kink region is methylated, the kink form is destabilized, and dsDNA becomes stiffer. As a result, methylation increases the stiffness of weakly bent dsDNA and concurrently can promote kink formation, which may stabilize the nucleosome structure. Our results provide new insight into the effect of methylation, showing that cytosine methylation has opposite effects on DNA mechanics depending on its curvature and methylation location.

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats.

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model's predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats.

Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we applied Mask Regions with Convolution Neural Network (Mask R-CNN), a deep learning model that uses instance segmentation, to detect hepatic fibrosis induced by N-nitrosodimethylamine (NDMA) in Sprague-Dawley rats. From 51 WSIs, we collected 2011 cropped images with hepatic fibrosis annotations. Training and detection of hepatic fibrosis via artificial intelligence methods was performed using Tensorflow 2.1.0, powered by an NVIDIA 2080 Ti GPU. From the test process using tile images, 95% of model accuracy was verified. In addition, we validated the model to determine whether the predictions by the trained model can reflect the scoring system by the pathologists at the WSI level. The validation was conducted by comparing the model predictions in 18 WSIs at 20× and 10× magnifications with ground truth annotations and board-certified pathologists. Predictions at 20× showed a high correlation with ground truth (R2 = 0.9660) and a good correlation with the average fibrosis rank by pathologists (R2 = 0.8887). Therefore, the Mask R-CNN algorithm is a useful tool for detecting and quantifying pathological findings in nonclinical studies.

Origin of a pair of red-crowned cranes (Grus japonensis) found in Sarobetsu Wetland, northwestern Hokkaido, Japan: a possible crossbreeding between the island and the mainland population.

Red-crowned cranes Grus japonensis, which are an endangered species, have two separate populations, a mainland population in the Eurasian continent and an island population in eastern Hokkaido, Japan. Island cranes showed three haplotypes (Gj1, Gj2 and Gj13), whereas ten haplotypes (Gj3-Gj12) were confirmed in captive cranes and stray cranes. We found Gj5 haplotype in feathers of two cranes as well as four new haplotypes in seven wild crane feathers collected in South Korea. We also found feathers in the nest in Sarobetsu Wetland in northwestern Hokkaido. While the haplotype of female-derived feathers was Gj2, that of male-derived feathers was Gj5. The results suggest that there has been crossbreeding between cranes in the island population and cranes in the mainland population.

The Effect of Sustainability-Related Information on the Sensory Evaluation and Purchase Behavior towards Salami Products.

Consumer's interest in sustainable livestock farming methods has grown in response to concerns for the environment and animal welfare. The purpose of this study is to examine the different influences of sustainability product information on sensory characteristics and purchase behaviors. To accomplish this aim, the study used salami, which is an Italian-style sausage processed by fermentation and drying. Three different types of information were provided: salami made from the pork of an antibiotic-free pig (SMAFP), of an animal welfare pig (SMAWP), and of a grazing pig (SMGP). This study was conducted as an off-line experiment with Korean participants (n=140). As a result, there were sensory differences according to the sustainability information. For the SMAFP, it had a significant difference in, sourness (p<0.05). With the SMAWP, there was a difference in gumminess (p<0.10), and the SMGP had significant differences in sourness (p<0.01), sweetness (p<0.01), andmoisture (p<0.05). Moreover, the purchase intention and willingness to pay were significantly higher when the sustainability information was given. Especially, among the three types of salamis, participants were willing to pay the most for the SMAWP. This is one of the first consumer studies to investigate sensory evaluation and purchase behavior for various types of sustainable livestock production. These results contribute by helping sustainable meat producers and marketers become aware of the kind of sustainable information to which consumers are sensitive.

The AHR1-ARNT1 dimerization pair is a major regulator of the response to natural ligands, but not to TCDD, in the chicken.

The activation of the aryl hydrocarbon receptor (AHR) occurs through the binding of dioxin-like compounds (DLCs) or natural ligands. In this pathway, the AHR-ARNT (AHR nuclear translocator) heterodimer serves to regulate critical physiological functions, such as immune responses and the metabolism of xenobiotics. Birds have three AHR isoforms (AHR1, AHR1ß, and AHR2) and two ARNT isoforms (ARNT1 and ARNT2). However, how AHR and ARNT dimerization pair in birds regulates the AHR signaling pathway in an isoform-specific manner remains unknown. In this study, we initially sought to clarify the major chicken AHR-ARNT (ckAHR-ckARNT) pairs by estimating the mRNA tissue distributions of various ckAHR and ckARNT isoforms. Our results indicated that the ckAHR1-ckARNT1 represented the major dimerization pair in most tissues except the brain. We then measured the transactivation potencies of various ckAHR-ckARNT pairs by natural ligands and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in in vitro reporter gene assays using COS-7 and LMH cell lines. Our results from the in vitro assays demonstrated that the ckAHR1-ckARNT1 pair was strongly activated by the five natural ligands, namely, 6-formylindolo [3,2-b]carbazole, L-kynurenin, kynurenic acid, indoxyl-3-sulfate, and 1,3,7-tribromodibenzo-p-dioxin, but not by TCDD. In in silico ligand docking simulations with ckAHR1 homology models, all the natural ligands showed a interaction pattern that was distinct from that observed with anthropogenic DLCs, including TCDD. In conclusion, our findings indicate that the ckAHR1-ckARNT1 may be the most important dimerization pair in most tissues for regulating the physiological functions driven by natural ligands, although it was less reactive to TCDD.

Single-molecule observation of ATP-independent SSB displacement by RecO in Deinococcus radiodurans.

Deinococcus radiodurans (DR) survives in the presence of hundreds of double-stranded DNA (dsDNA) breaks by efficiently repairing such breaks. RecO, a protein that is essential for the extreme radioresistance of DR, is one of the major recombination mediator proteins in the RecA-loading process in the RecFOR pathway. However, how RecO participates in the RecA-loading process is still unclear. In this work, we investigated the function of drRecO using single-molecule techniques. We found that drRecO competes with the ssDNA-binding protein (drSSB) for binding to the freely exposed ssDNA, and efficiently displaces drSSB from ssDNA without consuming ATP. drRecO replaces drSSB and dissociates it completely from ssDNA even though drSSB binds to ssDNA approximately 300 times more strongly than drRecO does. We suggest that drRecO facilitates the loading of RecA onto drSSB-coated ssDNA by utilizing a small drSSB-free space on ssDNA that is generated by the fast diffusion of drSSB on ssDNA.

Assessment of Risks of Dioxins for Aryl Hydrocarbon Receptor-Mediated Effects in Polar Bear (Ursus maritimus) by in Vitro and in Silico Approaches.

Polar bear (Ursus maritimus) populations accumulate dioxins and related compounds (DRCs) at levels that are of health concern. The toxicities of DRCs are primarily mediated via aryl hydrocarbon receptor (AHR) signaling pathway. To evaluate the sensitivity and responses to DRCs in polar bears, we assessed the activation potencies of polar bear-specific AHR (pbAHR) by DRCs through in vitro and in silico approaches. In vitro assays showed that the pbAHR was as sensitive to DRCs as C3H/lpr mouse AHR, which is well-known to be highly sensitive to DRCs. Comparison of pbAHR transactivation potencies indicated that TCDF, 2,3,4,7,8-PeCDF, and BaP exhibited high induction equivalency factors (IEFs). Considering the accumulation levels of DRCs in polar bears, PCB126 was found to be the most active inducer of pbAHR. The in vitro transactivation potencies of ligands of pbAHR showed a significant relationship with in silico ligand docking energies in a pbAHR homology model. The protein ligand interaction fingerprint (PLIF) analysis showed different interaction patterns depending on the ligands. Several amino acids which are highly conserved among mammals may be involved in species-specific responses via backbone interactions with neighboring amino acid residues which are specific to pbAHR. We document high susceptibility of polar bears to DRCs, through a mechanistic approach, for the first time.

General and facile purification of dye-labeled oligonucleotides by pH-controlled extraction.

Previously, we reported a method for facile purification of oligonucleotides labeled with hydrophobic dyes, based on the solubility difference between the hydrophilic DNA and unreacted dye. Here, we present a new purification method applicable to any dye regardless of its hydrophobicity. We exploited the population shift of a fluorescent dye in a low-pH aqueous solution from its anionic form toward its neutral form. When the pH of an aqueous solution containing dye-labeled DNA and unreacted free dye is lowered, and the solution is mixed with a hydrophobic organic solvent (butanol), the neutral free dye is preferentially dissolved in the organic phase, leaving behind the hydrophilic dye-labeled DNA in the aqueous phase. We experimentally verified that our new method results in high yields of dye-labeled oligonucleotides and the efficient removal of free dye.

Ecological factors drive natural selection pressure of avian aryl hydrocarbon receptor 1 genotypes.

The aryl hydrocarbon receptor (AHR) mediates dioxin toxicities. Several studies have suggested that two amino acid residues corresponding to the 324(th) and 380(th) positions in the ligand binding domain (LBD) of the chicken AHR1 (Ile_Ser as high sensitivity, Ile_Ala as moderate sensitivity, and Val_Ala as low sensitivity), could be an important factor determining dioxin sensitivity in avian species. Here, we analyzed the association between ecological factors and AHR1 LBD genotypes of 113 avian species. Cluster analyses showed that 2 major clusters and sub-clusters of the cluster 3 were associated with specific AHR1 genotypes depending on the food, habitat, and migration of the animal. The majority of the species with Ile_Ala type were the Passeriformes, which are omnivorous or herbivorous feeders in the terrestrial environment. The species with Val_Ala type was primarily composed of raptors and waterbirds, which have been exposed to naturally occurring dioxins. An in vitro reporter gene assay revealed that the sensitivity to a natural dioxin, 1,3,7-tribromodibenzo-p-dioxin was in the order of Ile_Ser > Ile_Ala > Val_Ala. These results suggest that ecological factors related to the exposure of natural dioxins contribute to natural selection of the avian AHR1 genotype, which consequently leads to different sensitivity to man-made dioxins.

In vitro and in silico evaluation of transactivation potencies of avian AHR1 and AHR2 by endogenous ligands: Implications for the physiological role of avian AHR2.

Aryl hydrocarbon receptor (AHR) is well conserved from invertebrates to vertebrates, and it mediates the toxic effects of exogenous ligands, including dioxins. Recent studies reported that AHRs activated by endogenous ligands play critical roles in mammalian physiological homeostasis. Avian species possess at least two AHR isoforms (AHR1 and AHR2), which exhibit species- and isoform-specific transactivation potencies to exogenous ligands, whereas mammals possess a single AHR. To delineate the profiles and roles of endogenous ligands for avian AHR isoforms, we investigated in vitro transactivation potencies of avian AHRs (AHR1 and AHR2 from the jungle crow, Corvus macrorhynchos; common cormorant, Phalacrocorax carbo; and black-footed albatross, Phoebastria nigripes) treated with the endogenous tryptophan metabolites 6-formylindolo [3,2-b] carbazole (FICZ), l-kynurenine (l-Kyn), kynurenic acid (KYNA), and indoxyl sulfate (IS). Furthermore, we analyzed the binding mode of these ligands to each avian AHR isoform by in silico docking simulations. The EC50 of FICZ (0.009-0.032nM) was similar regardless of the species or isoform of AHR. The estimated in silico binding mode of FICZ to AHRs was well conserved in both isoforms. The transactivation potencies of avian AHRs to other tryptophan metabolites were 10(5)-10(7) fold lower than those for FICZ, and EC50 values varied in a species- and isoform-specific manner. This was consistent with poor conservation of the binding mode of l-Kyn, KYNA, and IS predicted in in silico docking simulations. Our results suggest that in avian species, FICZ is the most potent endogenous AHR ligand, and that AHR1 and AHR2 are physiologically functional.

RESOLFT nanoscopy with photoswitchable organic fluorophores.

Far-field optical nanoscopy has been widely used to image small objects with sub-diffraction-limit spatial resolution. Particularly, reversible saturable optical fluorescence transition (RESOLFT) nanoscopy with photoswitchable fluorescent proteins is a powerful method for super-resolution imaging of living cells with low light intensity. Here we demonstrate for the first time the implementation of RESOLFT nanoscopy for a biological system using organic fluorophores, which are smaller in size and easier to be chemically modified. With a covalently-linked dye pair of Cy3 and Alexa647 to label subcellular structures in fixed cells and by optimizing the imaging buffer and optical parameters, our RESOLFT nanoscopy achieved a spatial resolution of ~74 nm in the focal plane. This method provides a powerful alternative for low light intensity RESOLFT nanoscopy, which enables biological imaging with small organic probes at nanoscale resolution.

Excision versus trichloroacetic acid (TCA) chemocauterization for branchial sinus of the pyriform fossa.

PURPOSE: We analyzed the outcomes of open surgical excision and endoscopic trichloroacetic acid (TCA) chemocauterization for the treatment of branchial sinus of the pyriform fossa (BSPF). METHOD: We retrospectively reviewed the records of 27 patients (16 males and 11 females) who were treated for BSPF at the Asan Medical Center between 1996 and 2013. RESULTS: The median age of the 27 patients was 4.5years (range, 0 to 15years). Before definitive surgery, 19 (70.3%) of the patients had histories of neck infection, and 16 (59.2%) patients had neck abscesses that were drained. The lesions were predominantly located on the left side (26 of 27; 96.2%). Excisions were performed for 14 (48.1%) patients. TCA chemocauterizations were performed for 13 patients. After a median follow-up period of 5.5years, 11 patients developed recurrence. The recurrence rates were not significantly different between the excision and chemocauterization groups (35.7% vs 46.1%, respectively, p=0.704). All of the recurred patients were successfully treated with repeated chemocauterization or reexcision. Analyses of the risk factors for recurrence revealed that a previous infection history tended to increase the rate of recurrence (90.9% vs 56.2%, p=0.090). CONCLUSION: Our experience suggests that the outcomes of excision and TCA chemocauterization are not significantly different. Additional studies are needed to reach a consensus regarding the best treatment strategy for BSPF.

Comparison of Arbekacin and Vancomycin in Treatment of Chronic Suppurative Otitis Media by Methicillin Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of ear infections. We attempted to evaluate the clinical usefulness of arbekacin in treating chronic suppurative otitis media (CSOM) by comparing its clinical efficacy and toxicity with those of vancomycin. Efficacy was classified according to bacterial elimination or bacteriologic failure and improved or failed clinical efficacy response. Ninety-five subjects were diagnosed with CSOM caused by MRSA. Twenty of these subjects were treated with arbekacin, and 36 with vancomycin. The bacteriological efficacy (bacterial elimination, arbekacin vs. vancomycin: 85.0% vs. 97.2%) and improved clinical efficacy (arbekacin vs. vancomycin; 90.0% vs. 97.2%) were not different between the two groups. However, the rate of complications was higher in the vancomycin group (33.3%) than in the arbekacin group (5.0%) (P=0.020). In addition, a total of 12 adverse reactions were observed in the vancomycin group; two for hepatotoxicity, one for nephrotoxicity, eight for leukopenia, two for skin rash, and one for drug fever. It is suggested that arbekacin be a good alternative drug to vancomycin in treatment of CSOM caused by MRSA.