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Abstract Background Secondary carnitine deficiency in patients with anorexia nervosa has been rarely reported. This study aimed to investigate the occurrence of carnitine deficiency in severely malnourished patients with eating disorders during refeeding and assess its potential adverse effects on treatment outcomes. Method In a cohort study of 56 female inpatients with eating disorders at a single hospital from March 2010 to December 2020, we measured plasma free carnitine (FC) levels and compared to those of a healthy control group (n = 35). The patients were categorized into three groups based on FC levels: FC deficiency (FC< 20 µmol/L), FC pre-deficiency (20 µmol/L ≤ FC< 36 µmol/L), and FC normal (36 µmol/L ≤ FC). Results Upon admission, the patients had a median age of 26 years (interquartile range [IQR]: 21–35) and a median body mass index (BMI) of 13.8 kg/m2 (IQR: 12.8–14.8). Carnitine deficiency or pre-deficiency was identified in 57% of the patients. Hypocarnitinemia was associated with a decline in hemoglobin levels during refeeding (odds ratio [OR]: 0.445; 95% confidence interval [CI]: 0.214–0.926, p = 0.03), BMI at admission (OR: 0.478; 95% CI: 0.217–0.874, p = 0.014), and moderate or greater hepatic impairment at admission (OR: 6.385; 95% CI: 1.170–40.833, p = 0.032). Conclusions Hypocarnitinemia, particularly in cases of severe undernutrition (BMI< 13 kg/m2 at admission) was observed in severely malnourished patients with eating disorders during refeeding, a critical metabolic transition phase. Moderate or severe hepatic impairment at admission was considered a potential indicator of hypocarnitinemia. Although hypocarnitinemia was not associated with any apparent adverse events other than anemia during refeeding, the possibility that carnitine deficiency may be a risk factor for more serious complications during sudden increases in energy requirements associated with changes in physical status cannot be denied. Further research on the clinical significance of hypocarnitinemia in severely malnourished patients with eating disorders is warranted.

The CHA2DS2 -VASc score is a vital clinical tool for evaluating thromboembolic risk in patients with atrial fibrillation (AF). This study investigated the efficacy of the CHA2DS2 -VASc score in a cohort of 737 heterogeneous patients (mean age: 63 years) receiving care in cardiac intensive care units (CICUs), with a creatinine-based estimated glomerular filtration rate (eGFR) of ≥60 mL/min/1.73 m2 upon admission and discharge. Incident chronic kidney disease (CKD) was defined as the emergence of a new-onset eGFR5 mL/min/1.73 m2 compared to that at discharge. The primary endpoint was the incidence of CKD, and the secondary endpoints included all-cause mortality, cardiovascular events, and progression to end-stage kidney disease. In this cohort, 210 (28 %) patients developed CKD. Multivariate analyses revealed that CHA2DS2 -VASc score was a significant independent predictor of incident CKD, regardless of the presence of AF. Integration of CHA2DS2 -VASc scores with eGFR enhanced the predictive accuracy of incident CKD, as evidenced by the improved C-index, net reclassification improvement, and integrated discrimination improvement values (all p

Abstract The renal angina index (RAI) is a validated scoring tool for predicting acute kidney injury (AKI). We investigated the efficacy of the RAI in 2436 heterogeneous patients (mean age, 70 years) treated in cardiac intensive care units (CICUs). The RAI was calculated from creatinine and patient condition scores. AKI was diagnosed by the Kidney Disease: Improving Global Outcome criteria. The primary and secondary endpoints were the development of severe AKI and all-cause mortality, respectively. Four hundred thirty-three patients developed AKI, 87 of them severe. In multivariate analyses, the RAI was a significant independent predictor of severe AKI. During the 12-month follow-up period, 210 patients suffered all-cause death. Elevated RAI was independently associated with all-cause mortality, as was NT-proBNP (p

Abstract The sixth Japanese meeting on host–transposon interactions, titled “Biological Function and Evolution through Interactions between Hosts and Transposable Elements,” was held on August 24th and 25th, 2023, at the National Institute of Genetics as well as online. This meeting was supported by the National Institute of Genetics and aimed to bring together researchers studying the diverse roles of TEs in genome function and evolution, as well as host defense systems against TE mobility, TE bursts during evolution, and intron mobility in mammals, insects, land plants, yeast, protozoa, and bacteria. Here, we have presented the highlights of the discussion. Organizers: Kenji Ichiyanagi, Yoko Ikeda, and Kuniaki Saito.

Preexisting cardiovascular disease (CVD) is a pivotal risk factor for severe coronavirus disease 2019 (COVID-19). We investigated the longitudinal (over 1 year and 9 months) humoral and cellular responses to primary series and booster doses of mRNA COVID-19 vaccines in patients with CVD. Twenty-six patients with CVD who received monovalent mRNA COVID-19 vaccines were enrolled in this study. Peripheral blood samples were serially drawn nine times from each patient. IgG against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) was measured using an enzyme-linked immunosorbent assay. The numbers of interferon-γ-releasing cells in response to SARS-CoV-2 peptides were measured using an enzyme-linked immunospot assay. The RBD-IgG titers increased 2 weeks after the primary series and booster vaccination and waned 6 months after vaccination. The S1-specific T cell responses in patients aged < 75 years were favorable before and after booster doses; however, the Omicron BA.1-specific T cell responses were poor. These results suggest that regular vaccination is useful to maintain long-term antibody levels and has implications for booster dose strategies in patients with CVD. Additional booster doses, including Omicron variant-adapted mRNA vaccines, may be recommended for patients with CVD, regardless of age.

Purpose The increased global burden of non-communicable diseases and mental disorders is an urgent health challenge for countries around the entire world, especially those experiencing super-ageing societies, where over 21% of the population is age 65 years or older. Japan is the world’s most rapidly ageing society, and as a result, medical costs are also rising dramatically. With the aims of establishing a foundational framework for future research efforts, primarily focusing on the development of a personal health record (PHR) system, and creating a long-term repository for bioresources integrated with PHRs, this study investigated potential health risks and future healthcare burdens based on a longitudinal analysis of health records.Participants The Resource Center for Health Science (RECHS) project is a long-term, prospective biobank project, population and health check-up-based cohort that primarily investigates the associations between lifestyle and environmental factors and some surrogate markers of non-communicable diseases, such as diabetes, hypertension, cardiovascular disease and cancer. Starting in 2010, we initiated an annual cohort study among voluntary participants recruited from health check-up programmes and collected data from the following sources: a self-administered baseline questionnaire that included items on dietary habits and stress, a Brief Self-Administered Diet History Questionnaire, the Centre for Epidemiologic Studies Depression Scale and the General Health Questionnaire-28.Findings to date For this prospective cohort study, we planned to enrol approximately 10 000 participants. We collected and stored serum samples from all participants for future analyses. The study participants who still were able to participate in these health check-ups and their outcomes were then obtained from the measurements and questionnaire responses.Future plans Insights emerging from the RECHS study can provide researchers and public health policy administrators with evidence to aid in the prevention of non-communicable diseases and clarify the most malleable status to implement preventive measures.

Objective: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage. Methods: The ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN). Results: The prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction. Conclusion: These results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients. Clinical impact: The proposed method could be used to determine the treatment strategy and choose the intensity of treatment after admission to the CCU.

Abstract Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative adversarial networks (GANs). However, applying a GAN as a data augmentation technique has not been explored, owing to the quality and diversity of the generated images. To promote such applications by generating diverse images, this study aims to generate free-form lesion images from tumor sketches using a pix2pix-based model, which is an image-to-image translation model derived from GAN. As pix2pix, which assumes one-to-one image generation, is unsuitable for data augmentation, we propose StylePix2pix, which is independently improved to allow one-to-many image generation. The proposed model introduces a mapping network and style blocks from StyleGAN. Image generation results based on 20 tumor sketches created by a physician demonstrated that the proposed method can reproduce tumors with complex shapes. Additionally, the one-to-many image generation of StylePix2pix suggests effectiveness in data-augmentation applications.

Abstract Background Coronavirus disease 2019 (COVID‐19) is known to cause not only respiratory but also neuropsychiatric symptoms, which are assumed to be derived from a cytokine storm and its effects on the central nervous systems. Patients with COVID‐19 who develop severe respiratory symptoms are known to show severe neuropsychiatric symptoms such as cerebrovascular disease and encephalopathy. However, the detailed clinical courses of patients with neuropsychiatric symptoms caused by mild or asymptomatic COVID‐19 remain poorly understood. Here, we present a case of COVID‐19 who presented with severe and prolonged neuropsychiatric symptoms subsequent to mild respiratory symptoms. Case presentation A 55‐year‐old female with COVID‐19 accompanied by mild respiratory symptoms showed delusion, psychomotor excitement, and poor communication ability during quarantine outside the hospital. Considering her diminished respiratory symptoms, her neuropsychiatric symptoms were initially regarded as psychogenic reactions. However, as she showed progressive disturbance of consciousness accompanied by an abnormal electroencephalogram, she was diagnosed with post‐COVID‐19 encephalopathy. Although her impaired consciousness and elevated cytokine level improved after steroid pulse therapy, several neuropsychiatric symptoms, including a loss of concentration, unsteadiness while walking, and fatigue, remained. Conclusions This case suggests the importance of both recognizing that even apparently mild COVID‐19‐related respiratory symptoms can lead to severe and persistent neuropsychiatric symptoms, and elucidating the mechanisms, treatment, and long‐term course of COVID‐19‐related neuropsychiatric symptoms in the future.

Abstract The fifth Japanese meeting on host–transposon interactions, titled “Biological Function and Evolution through Interactions between Hosts and Transposable Elements (TEs),” was held online on August 26–27, 2021. The meeting was supported by National Institute of Genetics and aimed to bring together researchers studying the diverse roles of TEs in genome function and evolution, as well as host defense systems against TE mobility by chromatin and RNA modifications and protein-protein interactions. Here, we present the highlights of the talks.

PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden on radiologists during diagnosis. Thus, the development of computer-aided diagnosis (CAD) and technologies assisting in diagnosis has been requested. However, because FDG accumulation in PET images differs for each organ, recognizing organ regions is essential for developing lesion detection and analysis algorithms for PET/CT images. Therefore, we developed a method for automatically extracting organ regions from PET/CT images using U-Net or DenseUNet, which are deep-learning-based segmentation networks. The proposed method is a hybrid approach combining morphological and functional information obtained from LDCT and PET images. Moreover, pre-training using ImageNet and RadImageNet was performed and compared. The best extraction accuracy was obtained by pre-training ImageNet with Dice indices of 94.1, 93.9, 91.3, and 75.1% for the liver, kidney, spleen, and pancreas, respectively. This method obtained better extraction accuracy for low-quality PET/CT images than did existing studies on PET/CT images and was comparable to existing studies on diagnostic contrast-enhanced CT images using the hybrid method and pre-training.

Abstract In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.

Aim: Chronic inflammation is closely associated with tryptophan (TRP)-kynurenine (KYN) metabolic pathway. However, TRP-KYN pathway has not been fully elucidated in psoriasis, a systemic inflammatory disease with skin lesions and extracutaneous manifestations. Herein, we studied comprehensively serum profiles of TRP-KYN pathway metabolites in psoriatic patients (PSOs) to clarify the involvement of this pathway in the pathophysiology of psoriasis and to evaluate serum biomarkers reflecting systemic inflammation in PSOs. Methods: The concentrations of main TRP metabolites, TRP, KYN, 3-hydroxykynurenine (3HK), kynurenic acid (KYNA), 3-hydroxyanthranilic acid (3HAA), and anthranilic acid (AA), were determined by high-performance liquid chromatography in the sera from 65 PSOs and 35 healthy controls (HCs). The levels of these metabolites and the ratios of metabolites were compared between these subjects. The correlations between these values and the psoriasis area severity index (PASI) scores were analyzed. Skin samples from PSOs and HCs were subjected to immunohistochemical staining for kynureninase, catabolic enzyme from KYN or 3HK to downstream. Cytokine concentrations were comprehensively measured in the same samples and the correlations between the cytokine levels and TRP-KYN pathway metabolite levels were examined. Results: Serum TRP, KYN, and KYNA concentrations were lower and the 3HAA concentrations were higher in PSOs than in HCs. The ratios of 3HK/KYN, 3HAA/3HK, and 3HK/AA were higher in PSOs than in HCs. The AA levels and the ratio of AA/KYN were weakly positively correlated, and TRP, KYNA, and 3HK levels and the ratios of KYNA/KYN and 3HAA/AA were weakly negatively correlated with the PASI scores. The AA, KYN, and KYNA levels were positively correlated with the interferon gamma-induced protein 10 (IP-10) concentrations. Kynureninase expression was enhanced in the epidermis, both involved and uninvolved skin. Conclusions: Serum profiles of TRP-KYN pathway metabolites differed between PSOs and HCs. TRP-KYN pathway-associated processes, including kynureninase activation, may be involved in the pathogenesis of psoriasis, and thus serve as targets for psoriasis therapy.

Abstract Dopamine is a key neurotransmitter that regulates attention through dopamine D1 and D2-receptors in the prefrontal cortex (PFC). We previously developed an object-based attention test (OBAT) to evaluate attention in mice. Disruption of the dopaminergic neuronal system in the PFC induced attentional impairment in the OBAT. However, previous studies have not systematically examined which specific brain regions are associated with the blockade of PFC dopamine D1 and D2-receptors in the OBAT. In this study, we investigated the association of dopamine D1 and D2-receptors in the PFC with attention and neuronal activity in diverse brain regions. We found that both dopamine D1 and D2-receptor antagonists induced attentional impairment in the OBAT by bilateral microinjection into the PFC of mice, suggesting that both dopamine D1 and D2-receptors were associated with attention in the OBAT. Our analysis of the neuronal activity as indicated by c-Fos expression in 11 different brain regions showed that based on the antagonist types, there was selective activation of several brain regions. Overall, this study suggests that both dopamine D1 and D2-receptors play a role in attention through different neuronal circuits in the PFC of mice.

Abstract Disturbances of attention are a common behavioral feature associated with neuropsychiatric disorders with largely unknown underlying causes. We previously developed an object-based attention test (OBAT) as a simple and practical method for evaluating attention in mice. Since its establishment, the test has become a popular method for assessing attention and related underlying mechanisms in various mouse models. However, the underlying neuronal network involved in this test has yet to be studied. The purpose of this study was to identify the principal brain regions activated in the OBAT. Accordingly, C57BL/6J mice were subjected to the OBAT and thereafter prepared for immunohistochemical quantification of c-Fos, an immediate early gene that is frequently used as a marker of neuronal activity, in 13 different brain regions. The number of c-Fos-positive cells was significantly higher in the prefrontal cortex (PFC), dorsomedial striatum (DMS), and dentate gyrus (DG) in the test group as compared to the control group. The neuronal activation of these brain regions during the OBAT indicates that these brain regions are necessary for the regulation of attention in this test. This was supported by excitotoxic lesioning of these brain regions, leading to impaired attention without causing locomotor dysfunction. This study is one of the first attempts to analyze the brain regions that regulate attention in the OBAT. These findings provide an initial insight into the role of these brain regions and ideas for studying the underlying neural and molecular mechanisms.

ABSTRACT To fight severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), mass vaccination has begun in many countries. To investigate the usefulness of a serological assay to predict vaccine efficacy, we analyzed the levels of IgG, IgM, and IgA against the receptor-binding domain (RBD) of SARS-CoV-2 in the sera from BNT162b2 vaccinated individuals in Japan. This study included 219 individuals who received two doses of BNT162b2. The levels of IgG, IgM, and IgA against RBD were measured by enzyme-linked immunosorbent assay before and after the first and second vaccination, respectively. The relationship between antibody levels and several factors, including age, gender, and hypertension were analyzed. Virus-neutralizing activity in sera was measured to determine the correlation with the levels of antibodies. A chemiluminescent enzyme immunoassay (CLEIA) method to measure IgG against RBD was developed and validated for the clinical setting. The levels of all antibody isotypes were increased after vaccination. Among them, RBD-IgG was dramatically increased after the second vaccination. The IgG levels in females were significantly higher than in males. There was a negative correlation between age and IgG levels in males. The IgG levels significantly correlated with the neutralizing activity. The CLEIA assay measuring IgG against RBD showed a reliable performance and a high correlation with neutralizing activity. Monitoring of IgG against RBD is a powerful tool to predict the efficacy of SARS-CoV-2 vaccination and provides useful information in considering a personalized vaccination strategy for COVID-19. IMPORTANCE Mass vaccination campaigns using mRNA vaccines against SARS-CoV-2 have begun in many countries. Serological assays to detect antibody production may be a useful tool to monitor the efficacy of SARS-CoV-2 vaccination in individuals. Here, we reported the induction of antibody isotype responses after the first and second dose of the BNT162b2 vaccine in a well-defined cohort of employees in Japan. We also reported that age, gender, and hypertension are associated with differences in antibody response after vaccination. This study not only provides valuable information with respect to antibody responses after BNT162b2 vaccination in the Japanese population but also the usefulness of serological assays for monitoring vaccine efficacy in clinical laboratories to determine a personalized vaccination strategy for COVID-19.

PIWI-clade Argonautes bind PIWI-interacting RNAs (piRNAs) to silence transposons and maintain genome integrity in animal gonads. Here the authors present the crystal structure of a Drosophila Piwi–piRNA complex isolated from cultured fly ovarian somatic cells and find that it contains a non-canonical DVDK tetrad, and lacks slicer activity in vitro.

Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.

In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We developed an electronic cleansing method using CycleGAN, a deep learning technique, to assist diagnosis in CTC. In this method, an original computed tomography (CT) image taken during a CTC examination and a manually cleansed image in which the barium area was manually removed from the original CT image were prepared and converted to an image in which the barium was removed from the original CT image using CycleGAN. In the experiment, the electric cleansing images obtained using the conventional method were compared with those obtained using the proposed method. The average barium cleansing rates obtained by the conventional and proposed methods were 72.3% and 96.3%, respectively. A visual evaluation of the images showed that it was possible to remove only barium without removing the intestinal tract. Furthermore, the extraction of colorectal polyps and early stage cancerous lesions in the colon was performed as in the conventional method. These results indicate that the proposed method using CycleGAN may be useful for accurately visualizing the colon without barium.

Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion.

Abstract The fourth Japanese meeting entitled “Biological Function and Evolution through Interactions between Hosts and Transposable Elements (TEs)” was held on August 20–21, 2018 at the National Institute of Genetics (NIG), Mishima, Japan. The meeting was supported by NIG, and its objective was to bring together researchers who study the diverse roles of TEs in genome evolution, as well as host defense systems against TE mobility, such as chromatin modifications, small RNAs, and others. Here, we present the highlights of the talks given by 14 invited speakers. Organizers: Kenji Ichiyanagi (chief), Kuniaki Saito, and Tetsuji Kakutani.

Objective(s): Positron emission tomography/computed tomography (PET/CT) examination is commonly used for the evaluation of pulmonary nodules since it provides both anatomical and functional information. However, given the dependence of this evaluation on physician’s subjective judgment, the results could be variable. The purpose of this study was to develop an automated scheme for the classification of pulmonary nodules using early and delayed phase PET/ CT and conventional CT images.Methods: We analysed 36 early and delayed phase PET/CT images in patients who underwent both PET/CT scan and lung biopsy, following bronchoscopy. In addition, conventional CT images at maximal inspiration were analysed. The images consisted of 18 malignant and 18 benign nodules. For the classification scheme, 25 types of shape and functional features were first calculated from the images. The random forest algorithm, which is a machine learning technique, was used for classification.Results: The evaluation of the characteristic features and classification accuracy was accomplished using collected images. There was a significant difference between the characteristic features of benign and malignant nodules with regard to standardised uptake value and texture. In terms of classification performance, 94.4% of the malignant nodules were identified correctly assuming that 72.2% of the benign nodules were diagnosed accurately. The accuracy rate of benign nodule detection by means of CT plus two-phase PET images was 44.4% and 11.1% higher than those obtained by CT images alone and CT plus early phase PET images, respectively.Conclusion: Based on the findings, the proposed method may be useful to improve the accuracy of malignancy analysis.

Objective: Papanicolaou and Giemsa stains used in cytology have different characteristics and complementary roles. In this study, we focused on cycle-consistent generative adversarial network (CycleGAN), which is an image translation technique using deep learning, and we conducted mutual stain conversion between Giemsa and Papanicolaou in cytological images using CycleGAN. Methods: A total of 191 Giemsa-stained images and 209 Papanicolaou-stained images were collected from 63 patients with lung cancer. From those images, 67 images from nine cases were used for testing and the remaining images were used for training. For data augmentation, the number of training images was increased by rotation and inversion, and the images were pipelined to CycleGAN to train the mutual conversion process involving Giemsa- and Papanicolaou-stained images. Three pathologists and three cytotechnologists performed visual evaluations of the authenticity of cell nuclei, cytoplasm, and cell layouts of the test images translated using CycleGAN. Results: As a result of converting Giemsa-stained images into Papanicolaou-stained images, the background red blood cell patterns present in Giemsa-stained images disappeared, and cell patterns that reproduced the shape and staining of the cell nuclei and cytoplasm peculiar to Papanicolaou staining were obtained. Regarding the reverse-translated results, nuclei became larger, and red blood cells that were not evident in Papanicolaou-stained images appeared. After visual evaluation, although actual images exhibited better results than converted images, the results were promising for various applications. Discussion: The stain translation technique investigated in this paper can complement specimens under conditions where only single stained specimens are available; it also has potential applications in the massive training of artificial intelligence systems for cell classification, and can also be used for training cytotechnologist and pathologists.

Progranulin is an autocrine growth factor that promotes proliferation, migration, invasion, and chemoresistance of various cancer cells. These mechanisms mainly depend on the protein kinase B (Akt)/mechanistic target of rapamycin (mTOR) pathway. Recent studies have shown that patients with hematopoietic cancer have elevated serum progranulin levels. Thus, the current study aimed to investigate the role of progranulin in hematopoietic cancer cells and how it modulates their proliferation. Both knockdown of progranulin and progranulin neutralizing antibody treatment inhibited proliferation in several human hematopoietic cancer cell lines. Moreover, progranulin depletion not only decreases the phosphorylation level of the Akt/mTOR pathway but also, surprisingly, increases the expression of transforming growth factor-beta (TGF-β) and phosphorylation of mothers against decapentaplegic homolog 2 (SMAD2) in Kasumi-1 cell. Furthermore, LY2109761, an inhibitor of TGF-β receptor type I/II kinase, and TGF-β neutralizing antibody blocked the inhibition of proliferation induced by progranulin depletion. These data provide new insights that progranulin alters cell proliferation via the TGF-β axis and progranulin could be a new therapeutic target for hematopoietic cancers.

Since recognizing the location and extent of infarction is essential for diagnosis and treatment, many methods using deep learning have been reported. Generally, deep learning requires a large amount of training data. To overcome this problem, we generated pseudo patient images using CycleGAN, which performed image transformation without paired images. Then, we aimed to improve the extraction accuracy by using the generated images for the extraction of cerebral infarction regions. First, we used CycleGAN for data augmentation. Pseudo-cerebral infarction images were generated from healthy images using CycleGAN. Finally, U-Net was used to segment the cerebral infarction region using CycleGAN-generated images. Regarding the extraction accuracy, the Dice index was 0.553 for U-Net with CycleGAN, which was an improvement over U-Net without CycleGAN. Furthermore, the number of false positives per case was 3.75 for U-Net without CycleGAN and 1.23 for U-Net with CycleGAN, respectively. The number of false positives was reduced by approximately 67% by introducing the CycleGAN-generated images to training cases. These results indicate that utilizing CycleGAN-generated images was effective and facilitated the accurate extraction of the infarcted regions while maintaining the detection rate.

Background: Several immunochromatographic serological test kits have been developed to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibodies, but their relative performance and potential clinical utility is unclear. Methods: Three commercially available serological test kits were evaluated using 99 serum samples collected from 29 patients diagnosed with coronavirus disease 2019 (COVID-19) and 100 serum samples collected from 100 healthy volunteers in 2017 as negative controls. Results: The specificity of the IgM and IgG antibodies showed comparable results among the three immunochromatographic serological test kits. The specificity for IgM antibody was 98.0%, 98.0%, and 97.0%, and the specificity for IgG antibody was identical among the three kits (99.0%). The IgM antibody-positive rates of the three test kits for samples taken at the early stage of the disease (0–4 days after onset) were consistent with all three kits (18.2%); however, the IgM antibody-positive rates thereafter showed considerable differences among the kits, making it difficult to interpret the kinetics of IgM response against SARS-CoV-2. The IgG antibody-positive rates for samples taken after 13 days of onset were 100.0%, 97.6%, and 97.6%, respectively. Conclusion: There were large differences among the results of the three test kits. Only few cases showed positive results for IgM, suggesting that at least 2 of these kits used in this study were unsuitable for diagnosis of COVID-19. The IgG antibody was positive in almost all samples after 13 days of onset, suggesting that it may be useful for determining infections in the recent past.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. To further improve the DCNN's performance, it is necessary to train the network using more images. However, it is difficult to acquire cell images which contain a various cytological features with the use of many manual operations with a microscope. Therefore, in this study, we aim to improve the classification accuracy of a DCNN with the use of actual and synthesized cytological images with a generative adversarial network (GAN). Based on the proposed method, patch images were obtained from a microscopy image. Accordingly, these generated many additional similar images using a GAN. In this study, we introduce progressive growing of GANs (PGGAN), which enables the generation of high-resolution images. The use of these images allowed us to pretrain a DCNN. The DCNN was then fine-tuned using actual patch images. To confirm the effectiveness of the proposed method, we first evaluated the quality of the images which were generated by PGGAN and by a conventional deep convolutional GAN. We then evaluated the classification performance of benign and malignant cells, and confirmed that the generated images had characteristics similar to those of the actual images. Accordingly, we determined that the overall classification accuracy of lung cells was 85.3% which was improved by approximately 4.3% compared to a previously conducted study without pretraining using GAN-generated images. Based on these results, we confirmed that our proposed method will be effective for the classification of cytological images in cases at which only limited data are acquired.

Objectives: Andrographis paniculata (A. paniculata) is a major medicinal herb, and andrographolide (Andro) is the main component of A. paniculata. Here we evaluated the anti-proliferative activity and anti-inflammatory effects of Andro on THP-1human monocytic leukemia cell line. Methods: THP-1 cells were cultured in the presence or absence of Andro, Ara-C or vincristine. The anti-cancer activities of Andro were assessed by cell morphological changes, DNA fragmentation, MTT assay, annexin V positive rate and caspase-3/7 activities. The inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) were measured after addition of Andro. Results: MTT assays showed that Andro inhibited cell proliferation of THP-1 cells. Further, annexin V and caspase-3/7 positive rates were increased by Andro compared with untreated cells, indicating induction of apoptosis. IL-6, TNF-α and nuclear factor-kappa B (NF-κB) were increased by LPS, but the addition of Andro blocked the induction of cytokines by LPS. Conclusions: These results suggest that Andro has anti-proliferative effects and anti-inflammatory activities via induction of apoptosis and the suppression of NF-κB, respectively.

Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regarding the reduction in false positives in the detected results. In this study, we proposed a novel object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing a local analysis of the images. U-Net was introduced as a semantic segmentation method to detect early candidates for gastric cancer. These candidates were classified as gastric cancer cases or false positives based on box classification using a convolutional neural network. In the experiments, the detection performance was evaluated via the 5-fold cross-validation method using 1208 images of healthy subjects and 533 images of gastric cancer patients. When DenseNet169 was used as the convolutional neural network for box classification, the detection sensitivity and the number of false positives evaluated on a lesion basis were 98% and 0.01 per image, respectively, which improved the detection performance compared to the previous method. These results indicate that the proposed method will be useful for the automated detection of early gastric cancer from endoscopic images.

Concern has been raised about the effectiveness of the coronavirus disease 2019 (COVID-19) vaccine in the population of patients with various comorbidities such as heart disease. We investigated the humoral response to the BNT162b2 mRNA COVID-19 vaccine in patients with cardiovascular disease (CVD). We measured IgG against severe acute respiratory syndrome coronavirus 2 spike receptor-binding domain (RBD-IgG) in 85 CVD patients and 179 healthcare workers (HCWs). Blood samples were collected from patients and HCWs three times: (1) before the first dose of vaccination, (2) two weeks after the first dose of vaccination, and (3) two weeks after the second dose of vaccination. Patients with CVD showed a significantly inferior serological response to the BNT162b2 mRNA COVID-19 vaccine at 14 days after the prime dose compared to HCWs (21% vs. 95%, p < 0.001). Median RBD-IgG titers of patients with CVD at 14 days after the second dose were significantly lower than those of HCWs (137.2 U/mL (80.6–200.4 U/mL) vs. 176.2 U/mL (123.9–260.0 U/mL), p < 0.001). In multivariable analyses, CVD is significantly associated with seropositivity after first vaccination and RBD-IgG titers after second vaccination. CVD patients may have a poor humoral response to the BNT162b2 mRNA COVID-19 vaccine, need to be closely monitored, and require earlier revaccination to ensure stronger immunity and protection against infection.

Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA and our original classification model. In this method, the brain tumor region was extracted using the Clara segmentation model, and the volume of interest (VOI) created using this extracted region was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and our original 3D CNN.

Objectives: Andrographis paniculata (A. paniculata) is a widely used herb that has potential medical properties. Andrographolide (Andro) is the major component of A. paniculata. We evaluated the anti-tumor activity of Andro using leukemic cell line cells. Methods: Leukemic cell lines U937, HL60 or H929 cells were cultured in the presence or absence of Andro and compared with the effects of Ara-C or vincristine. The anti-tumor activity was assessed by morphological observations of the cells, DNA fragmentation, MTT assay, Annexin V positive rate, caspase-3/7 activity, and cell cycle analysis. Results: After addition of Andro, the morphology of cells changed to characteristic shapes with apoptotic bodies. Furthermore, the Annexin V positive rate and caspase-3/7 activities were increased compared with untreated cells. The G1 phase of cell cycle was also similarly increased compared with cells treated with Ara-C. Conclusions: Our results show that Andro has an anti-tumor activity against leukemic cell lines, very possibly by inducing apoptosis.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is increasing worldwide as one of the leading causes of chronic liver disease. Sake lees (SL) are secondary products of sake manufacturing and are considered to have beneficial effects on human health. To investigate these effects, we used high fat diet (HFD)-fed mice treated with or without the SL extract. Method Mice were the HFD ad libitum for 8 weeks and were administered 500 μL of distilled water with or without the SL extract (350 mg/mL) by a feeding needle daily for the last 4 weeks. Food intake, body weight, and liver weight were measured. Triacylglycerol content and the mRNA and protein expression levels of various lipid and glucose metabolism-related genes were determined in liver tissues. The levels of triglyceride, free fatty acids, glucose, insulin, and liver cell damage markers were determined in serum. Fatty acid-induced lipid accumulation in HepG2 cells was assessed in the presence or absence of the SL extract. Results Mice fed a HFD and treated with the SL extract demonstrated a significant reduction in hepatic lipid accumulation and mRNA and protein levels of peroxidome proliferator-activated receptor γ (PPARγ), PPARα, CD36, and phosphoenolpyruvate carboxykinase 1 in the liver, while the SL extract did not affect body weight and food intake. Moreover, insulin resistance and hepatic inflammation in HFD-fed mice improved after administration of the SL extract. In HepG2 cells, the SL extract suppressed fatty acid-induced intracellular lipid accumulation. Conclusions These findings suggest that treatment with the SL extract could potentially reduce the risk of NAFLD development, and that the SL extract may be clinically useful for the treatment of NAFLD.

Background: Lung cancer is a leading cause of death worldwide, and its early detection is usually performed with low-dose computed tomography. For lesions suspected of abnormality by CT examination, a cytological diagnosis of lung cells collected by biopsy is first performed. However, atypical cells are the major challenge in malignant lung cell classification for cytotechnologists and cytopathologists. In this study, we aimed to automatize the classification of malignant lung cells from microscopic images using a deep convolutional neural network (DCNN). Method: Cytological specimens were prepared with a liquid-based cytology system and stained using the Papanicolaou technique. Images were acquired with a digital still camera attached to a microscope with a 40× objective lens. The original microscopic images were first cropped to obtain image patches with resolution of 224 × 224 pixels. We obtained 306 benign and 315 malignant image patches. To avoid overfitting, 60,000 patch images were generated using data augmentation by applying rotation, flipping, filtering, and color adjustment. DCNN classification was conducted based on a fine-tuned VGG-16 model. We performed patch-based segmentation of malignant regions in the images and evaluated classification performance using threefold cross-validation. Results: The classification sensitivity and specificity were 89.3 and 83.3%, respectively, reaching a performance comparable to that of a cytopathologist. Using the gradient-weighted class activation mapping, we visualized the DCNN identification performance while the network searched for typical benign and malignant cells in images for classification. Conclusions: The proposed method can be useful for accurate and automatic classification of lung cells from pulmonary cytological images. Keywords: Pathology, Cytology, Deep learning, Deep convolutional neural network, Lung cancer, Malignancy analysis

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

Indoleamine 2,3-dioxygenase 1 (IDO1) is an enzyme known to suppress immune responses, and several reports have showed that it is associated with psoriasis. IDO2 is an isoform of IDO1, recently identified as a catalytic enzyme in the tryptophan-kynurenine pathway, which is expressed in dendritic cells and monocytes. The expression of IDO2 in immune cells suggests that IDO2 may contribute to immune functions. However, the role of IDO2 in the pathogenesis of psoriasis remains unclear. In this study, to elucidate the role of IDO2 in psoriasis, we assessed imiquimod (IMQ)-induced psoriasis-like dermatitis in IDO2 knockout (KO) mice. Skin inflammation, evaluated by scoring erythema, scaling, and ear thickness, was significantly worse in the IDO2 KO mice than in the wild-type (WT) mice. The mRNA expression levels of TNF-α, IL-23p19, and IL-17A, key cytokines involved in the development of psoriasis, were also increased in the IDO2 KO mice. Furthermore, immunohistochemistry revealed that the number of Ki67-positive cells in the epidermis and CD4-, CD8-, and IL-17-positive lymphocytes infiltrating the dermis were significantly increased in the IDO2 KO mice. These results suggest that IDO2 might decrease IL-17 expression, thereby resulting in the suppression of skin inflammation in IMQ-induced psoriasis-like dermatitis.

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

Gastrointestinal endoscopy is widely conducted for the early detection of gastric cancer. However, it is often difficult to detect early gastric cancer lesions and accurately evaluate the invasive regions. Our study aimed to develop a detection and segmentation method for early gastric cancer regions from gastrointestinal endoscopic images. In this method, we first collected 1208 healthy and 533 cancer images. The gastric cancer region was detected and segmented from endoscopic images using Mask R-CNN, an instance segmentation method. An endoscopic image was provided to the Mask R-CNN, and a bounding box and a label image of the gastric cancer region were obtained. As a performance evaluation via five-fold cross-validation, sensitivity and false positives (FPs) per image were 96.0% and 0.10 FP/image, respectively. In the evaluation of segmentation of the gastric cancer region, the average Dice index was 71%. These results indicate that our proposed scheme may be useful for the detection of gastric cancer and evaluation of the invasive region in gastrointestinal endoscopy.

PIWI-interacting RNAs (piRNAs) direct Piwi to repress transposons and maintain genome integrity in Drosophila ovarian somatic cells. piRNA maturation and association with Piwi occur at perinuclear Yb bodies, the centers of piRNA biogenesis. Here, we show that piRNA intermediates arising from the piRNA cluster flamenco (flam) localize to perinuclear foci adjacent to Yb bodies, termed Flam bodies. RNAi-based screening of piRNA factors revealed that Flam body formation depends on Yb, the core component of Yb bodies, while Piwi and another Yb body component, Armitage, are dispensable for formation. Abolishing the RNA-binding activity of Yb disrupts both Flam bodies and Yb bodies. Yb directly binds flam, but not transcripts from neighboring protein-coding genes. Thus, Yb integrates piRNA intermediates and piRNA processing factors selectively into Flam bodies and Yb bodies, respectively. We suggest that Yb is a key upstream factor in the cytoplasmic phase of the piRNA pathway in ovarian somatic cells.

Multiple studies on the dynamics of inflammatory cytokines in patients with anorexia nervosa (AN) have been published, although results are not consistent among reports. Thus the pathophysiologic roles of these cytokines are not clear. We performed an exploratory analysis that included (1) comparisons of plasma interleukin-18 (IL-18) concentrations between patients with AN (n = 21) and healthy controls (n = 39), and (2) correlations between body mass index (BMI) and IL-18 concentrations in both groups, exploring the relationship between malnourishment and IL-18. Plasma IL-18 levels were significantly decreased in patients with AN compared with controls. Plasma IL-18 levels correlated to BMI in controls, but not in patients with AN. These results suggest that a decline in plasma IL-18 levels in patients with AN is not only due to malnourishment, but other pathophysiologic changes as well. IL-18 has a role in the brain’s reaction to sadness and chronic stress. Therefore, decreased levels of IL-18 may commonly occur in patients with chronic AN.

In the present study, we examined the role of indoleamine 2,3-dioxygenase (IDO) in the development of CCl4-induced hepatic fibrosis. The liver fibrosis induced by repetitive administration with CCl4 was aggravated in IDO-KO mice compared to WT mice. In IDO-KO mice treated with CCl4, the number of several inflammatory cells and the expression of pro-inflammatory cytokines increased in the liver. In the results, activated hepatic stellate cells (HSCs) and fibrogenic factors on HSCs increased after repetitive CCl4 administration in IDO-KO mice compared to WT mice. Moreover, the treatment with l-tryptophan aggravated the CCl4-induced hepatic fibrosis in WT mice. Our findings demonstrated that the IDO deficiency enhanced the inflammation in the liver and aggravated liver fibrosis in repetitive CCl4-treated mice.

Indoleamine 2,3-dioxygenase (IDO), a tryptophan-catabolizing intracellular enzyme of the L-kynurenine pathway, causes preneoplastic cells and tumor cells to escape the immune system by inducing immune tolerance; this mechanism might be associated with the development and progression of human malignancies. In the present study, we investigated the role of IDO in diethylnitrosamine (DEN)-induced hepatocarcinogenesis by using IDO-knockout (KO) mice. To induce hepatocellular carcinoma (HCC), hepatic adenoma, and preneoplastic hepatocellular lesions termed foci of cellular alteration (FCA), male IDO-wild-type (WT) and IDO-KO mice with a C57BL/6J background received a single intraperitoneal injection of DEN at 2 weeks of age. The mice were sacrificed to evaluate the development of FCA and hepatocellular neoplasms. HCC overexpressed IDO and L-kynurenine compared to surrounding normal tissue in the DEN-treated IDO-WT mice. The number and cell proliferative activity of FCAs, and the incidence and multiplicity of HCC were significantly greater in the IDO-WT than in the IDO-KO mice. The expression levels of the IDO protein, of L-kynurenine, and of IFN-γ, COX-2, TNF-α, and Foxp3 mRNA were also significantly increased in the DEN-induced hepatic tumors that developed in the IDO-WT mice. The mRNA expression levels of CD8, perforin and granzyme B were markedly increased in hepatic tumors developed in IDO-KO mice. Moreover, Foxp3-positive inflammatory cells had infiltrated into the livers of DEN-treated IDO-WT mice, whereas fewer cells had infiltrated into the livers of IDO-KO mice. Induction of IDO and elevation of L-kynurenine might play a critical role in both the early and late phase of liver carcinogenesis. Our findings suggest that inhibition of IDO might offer a promising strategy for the prevention of liver cancer.

Indoleamine 2,3-dioxygenase (IDO) 1, that catalyzes the first and rate-limiting step in the degradation of L-tryptophan, has an important immunomodulatory function. The activity of IDO1 increases in various inflammatory diseases, including tumors, autoimmune diseases, and different kinds of inflammation. We evaluated the suppressive effect of plant extracts or phytochemicals on IDO1 induction and activity; sixteen kinds of plants extracts and fourteen kinds of phytochemicals were examined. As a result, the methanol extracts of Myoga flower buds, which are traditional Japanese foods, and labdane-type diterpene galanal derived from Myoga flowers significantly suppressed IDO1 activity. The Lineweaver-Burk plot analysis indicated that galanal is a competitive inhibitor. Galanal attenuated L-kynurenine formation with an IC₅₀ value of 7.7 µM in the assay system using recombinant human IDO1, and an IC₅₀ value of 45 nM in the cell-based assay. Further, mechanistic analysis revealed that galanal interfered with the transcriptional function of the nuclear factor-κB and the interferon-γ signaling pathway. These effects of galanal are important for immune response. Because the inhibitory effect of galanal on IDO1 activity was stronger than that of 1-methyl tryptophan, a tryptophan analog, galanal may have great potential as the novel drug for various immune-related diseases.

Hepatic immune regulation is associated with the progression from simple steatosis to non-alcoholic steatohepatitis, a severe condition of inflamed fatty liver. Indoleamine 2,3-dioxygenase (IDO), an intracellular enzyme that mediates the catabolism of L-tryptophan to L-kynurenine, plays an important role in hepatic immune regulation. In the present study, we examined the effects of IDO gene silencing on high-fat diet (HFD)-induced liver inflammation and fibrosis in mice. After being fed a HFD for 26 weeks, the IDO-knockout (KO) mice showed a marked infiltration of inflammatory cells, especially macrophages and T lymphocytes, in the liver. The expression levels of F4/80, IFNγ, IL-1β, and IL-6 mRNA in the liver and the expression levels of F4/80 and TNF-α mRNA in the white adipose tissue were significantly increased in IDO-KO mice, although hepatic steatosis, the accumulation of intrahepatic triglycerides, and the amount of oxidative stress were lower than those in IDO-wild-type mice. IDO-KO mice also developed marked pericellular fibrosis in the liver, accumulated hepatic hydroxyproline, and exhibited increased expression levels of hepatic TGF-β1 mRNA. These findings suggest that IDO-KO renders the mice more susceptible to HFD-induced hepatic inflammation and fibrosis. Therefore, IDO may have a protective effect against hepatic fibrosis, at least in this HFD-induced liver injury model.

Indoleamine 2,3-dioxygenase 1 (IDO1), the L-tryptophan-degrading enzyme, plays a key role in the immunomodulatory effects on several types of immune cells. Originally known for its regulatory function during pregnancy and chronic inflammation in tumorigenesis, the activity of IDO1 seems to modify the inflammatory state of infectious diseases. The pathophysiologic activity of L-tryptophan metabolites, kynurenines, is well recognized. Therefore, an understanding of the regulation of IDO1 and the subsequent biochemical reactions is essential for the design of therapeutic strategies in certain immune diseases. In this paper, current knowledge about the role of IDO1 and its metabolites during various infectious diseases is presented. Particularly, the regulation of type I interferons (IFNs) production via IDO1 in virus infection is discussed. This paper offers insights into new therapeutic strategies in the modulation of viral infection and several immune-related disorders.

Activation-induced cytidine deminase (Aid), a unique enzyme that deaminates cytosine in DNA, shuttles between the nucleus and the cytoplasm. A recent study proposed a novel function of Aid in active DNA demethylation via deamination of 5-hydroxymethylcytosine, which is converted from 5-methylcytosine by the Ten-eleven translocation (Tet) family of enzymes. In this study, we examined the effect of simultaneous expression of Aid and Tet family proteins on the subcellular localization of each protein. We found that overexpressed Aid is mainly localized in the cytoplasm, whereas Tet1 and Tet2 are localized in the nucleus, and Tet3 is localized in both the cytoplasm and the nucleus. However, nuclear Tet proteins were gradually translocated to the cytoplasm when co-expressed with Aid. We also show that Aid-mediated translocation of Tet proteins is associated with Aid shuttling. Here we propose a possible role for Aid as a regulator of the subcellular localization of Tet family proteins.