PANCDR: precise medicine prediction using an adversarial network for cancer drug response.

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

Changes in the intrinsic severity of severe acute respiratory syndrome coronavirus 2 according to the emerging variant: a nationwide study from February 2020 to June 2022, including comparison with vaccinated populations.

BACKGROUND: As the population acquires immunity through vaccination and natural infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), understanding the intrinsic severity of coronavirus disease (COVID-19) is becoming challenging. We aimed to evaluate the intrinsic severity regarding circulating variants of SARS-CoV-2 and to compare this between vaccinated and unvaccinated individuals. METHODS: With unvaccinated and initially infected confirmed cases of COVID-19, we estimated the case severity rate (CSR); case fatality rate (CFR); and mortality rate (MR), including severe/critical cases and deaths, stratified by age and compared by vaccination status according to the period regarding the variants of COVID-19 and vaccination. The overall rate was directly standardized with age. RESULTS: The age-standardized CSRs (aCSRs) of the unvaccinated group were 2.12%, 5.51%, and 0.94% in the pre-delta, delta, and omicron period, respectively, and the age-standardized CFRs (aCFRs) were 0.60%, 2.49%, and 0.63% in each period, respectively. The complete vaccination group had lower severity than the unvaccinated group over the entire period showing under 1% for the aCSR and 0.5% for the aCFR. The age-standardized MR of the unvaccinated group was 448 per million people per month people in the omicron period, which was 11 times higher than that of the vaccinated group. In terms of age groups, the CSR and CFR sharply increased with age from the 60 s and showed lower risk reduction in the 80 s when the period changed to the omicron period. CONCLUSIONS: The intrinsic severity of COVID-19 was the highest in the delta period, with over 5% for the aCSR, whereas the completely vaccinated group maintained below 1%. This implies that when the population is vaccinated, the impact of COVID-19 will be limited, even if a new mutation appears. Moreover, considering the decreasing intrinsic severity, the response to COVID-19 should prioritize older individuals at a higher risk of severe disease.

Establishing a Framework for Evaluating the Effectiveness of Vaccines Targeting National Vaccination Programs.

BACKGROUND: The increasing number of vaccines and the complexity of immunization programs, along with continuous changes in the epidemiology of infectious diseases, necessitate a systematic approach to vaccine effectiveness (VE) evaluation. This study presents a preliminary survey to establish a VE evaluation framework in Korea, focusing on the National Immunization Program. METHODS: Experts' opinions were collected through a two-round online survey targeting key stakeholders. The first round consisted of two multiple-choice questions and two open-ended questions. The second round was a quantitative survey with 17 questionnaires based on five domains derived by analyzing the results of the first-round survey. RESULTS: The results emphasize the necessity and urgency of a government-led VE evaluation system and the establishment of a multidisciplinary evaluation organization. Key considerations include personnel, budget, data integration, legal standards, and surveillance system enhancements. CONCLUSION: These findings provide valuable insights for policymakers, emphasizing the need for collaboration, financial support, and robust data management in developing evidence-based vaccination policies.

MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.

MOTIVATION: Accurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting the prognosis of complex diseases are required. RESULTS: Here, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various classification tasks demonstrated the superior performance of this approach. The utility of MOMA was also verified using a comparison experiment with an attention mechanism that was turned on or off and biological analysis. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/dmcb-gist/MOMA. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Characterization of Kikuchi-Fujimoto Disease in Children and Risk Factors Associated with Its Course.

OBJECTIVE: To outline the characteristics of Kikuchi-Fujimoto disease (KFD) in children and analyze factors associated with severe and recurring courses. METHODS: Electronic medical records of children histopathologically diagnosed with KFD at Seoul National University Bundang Hospital from March 2015 to April 2021 were retrospectively reviewed. RESULTS: A total of 114 cases (62 males) were identified. The mean patient age was 12.0 ± 3.5 years. Most patients came to medical attention with cervical lymph node enlargement (97.4%) and fever (85%); 62% had a high-grade fever (≥39°C). Prolonged fever (≥14 days) was seen in 44.3% and was associated with a high-grade fever (P = .004). Splenomegaly, oral ulcer, or rash was present in 10.5%, 9.6%, and 15.8%, respectively. Laboratory findings showed leukopenia, anemia, and thrombocytopenia in 74.1%, 49%, and 24%, respectively. Sixty percent of cases had a self-limited course. Antibiotics were initially prescribed in 20%. A corticosteroid was prescribed in 40% of patients and was associated with oral ulcer (P = .045) and anemia (P = .025). Twelve patients (10.5%) had a recurrence with a median interval of 19 months. No risk factor for recurrence was identified in multivariable analysis. Clinical characteristics of KFD were similar between our current and previous studies. However, antibiotics use decreased (P < .001); nonsteroidal anti-inflammatory drugs use increased (P < .001), and, although statistically not significant, corticosteroid treatment also increased. CONCLUSIONS: Over a span of 18 years, the clinical characteristics of KFD did not change. Patients presenting with high-grade fever, oral ulcer, or anemia may benefit from corticosteroid intervention. All patients should be monitored for recurrence.

Inhibition of CDK4/6 regulates AD pathology, neuroinflammation and cognitive function through DYRK1A/STAT3 signaling.

Repurposing approved drugs is an emerging therapeutic development strategy for Alzheimer's disease (AD). The CDK4/6 inhibitor abemaciclib mesylate is an FDA-approved drug for breast cancer treatment. However, whether abemaciclib mesylate affects Aß/tau pathology, neuroinflammation, and Aß/LPS-mediated cognitive impairment is unknown. In this study, we investigated the effects of abemaciclib mesylate on cognitive function and Aß/tau pathology and found that abemaciclib mesylate improved spatial and recognition memory by regulating the dendritic spine number and neuroinflammatory responses in 5xFAD mice, an Aß-overexpressing model of AD. Abemaciclib mesylate also inhibited Aß accumulation by enhancing the activity and protein levels of the Aß-degrading enzyme neprilysin and the α-secretase ADAM17 and decreasing the protein level of the γ-secretase PS-1 in young and aged 5xFAD mice. Importantly, abemaciclib mesylate suppressed tau phosphorylation in 5xFAD mice and tau-overexpressing PS19 mice by reducing DYRK1A and/or p-GSK3ß levels. In wild-type (WT) mice injected with lipopolysaccharide (LPS), abemaciclib mesylate rescued spatial and recognition memory and restored dendritic spine number. In addition, abemaciclib mesylate downregulated LPS-induced microglial/astrocytic activation and proinflammatory cytokine levels in WT mice. In BV2 microglial cells and primary astrocytes, abemaciclib mesylate suppressed LPS-mediated proinflammatory cytokine levels by downregulating AKT/STAT3 signaling. Taken together, our results support repurposing the anticancer drug, CDK4/6 inhibitor abemaciclib mesylate as a multitarget therapeutic for AD pathologies.

Development of diagnostic tests for pathogen identification and detection of antimicrobial resistance on WHO global priority pathogens using modular real-time nucleic acid amplification test.

BACKGROUND: Concerns regarding antimicrobial resistance (AMR) have resulted in the World Health Organization (WHO) designating so-called global priority pathogens (GPPs). However, little discussion has focused on the diagnosis of GPPs. To enable the simultaneous identification of pathogens and AMR, we developed a modular real-time nucleic acid amplification test (MRT-NAAT). METHODS: Sequence-specific primers for each modular unit for MRT-NAAT pathogen identification and AMR sets were designed. The composition of the reaction mixture and the real-time PCR program were unified irrespective of primer type so to give MRT-NAAT modularity. Standard strains and clinical isolates were used to evaluate the performance of MRT-NAAT by real-time PCR and melting curve analysis. Probit analysis for the MRT-NAAT pathogen identification set was used to assess the limit of detection (LoD). RESULTS: The MRT-NAAT pathogen identification set was made up of 15 modular units 109-199 bp in product size and with a Tms of 75.5-87.5 °C. The LoD was < 15.548 fg/µL, and nine modular units successfully detected the target pathogens. The MRT-NAAT AMR set included 24 modular units 65-785 bp in product size with a Tms of 75.5-87.5 °C; it showed high performance for detecting GPP target genes and variants. CONCLUSIONS: MRT-NAAT enables pathogen identification and AMR gene detection and is time-effective. By unifying the reaction settings of each modular unit, the modularity where combinations of primers can be used according to need could be achieved. This would greatly help in reflecting the researcher's need and the AMR status of a certain region while successfully detecting pathogens and AMR genes.

Detecting amyloid-ß positivity using regions of interest from structural magnetic resonance imaging.

BACKGROUND AND PURPOSE: Alzheimer disease (AD) is the most common type of dementia. Amyloid-ß (Aß) positivity is the main diagnostic marker for AD. Aß positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of Aß, and the accessibility of these methods is thus relatively limited compared with structural magnetic resonance imaging (sMRI). METHODS: We investigated whether regions of interest (ROIs) in sMRIs can be used to predict Aß positivity for samples with normal cognition (NC), mild cognitive impairment (MCI), and dementia. We obtained 846 Aß negative (Aß-) and 865 Aß positive (Aß+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are Aß+, we built five machine learning models using ROIs and apolipoprotein E (APOE) genotypes as features. To test the performance of the machine learning models, we constructed a new cohort containing 97 Aß- and 81 Aß+ samples. RESULTS: The best performing machine learning model combining ROIs and APOE had an accuracy of 0.798, indicating that it can help predict Aß+. Furthermore, we searched ROIs that could aid our prediction and discovered that an average left entorhinal cortical region (L-ERC) thickness is an important feature. We also noted significant differences in L-ERC thickness between the Aß- and Aß+ samples even in the same diagnosis of NC, MCI, and dementia. CONCLUSIONS: Our findings indicate that ROIs from sMRIs along with APOE can be used as an initial screening tool in the early diagnosis of AD.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.

BACKGROUND: Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. OBJECTIVE: This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. METHODS: We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. RESULTS: A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. CONCLUSIONS: Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Risk Factors for Sudden Death Within 2 Days After Diagnosis of COVID-19 in Korea.

BACKGROUND: We aimed to analyze the risk factors for sudden death after diagnosis of coronavirus disease 2019 (COVID-19) in South Korea and to provide evidence for informing prevention and control interventions for patients at risk of sudden death. METHODS: We included 30,302 COVID-19 related deaths registered in the patient management information system (Central Disease Control Headquarters) between January 1, 2021, and December 15, 2022. We collected their epidemiological data recorded by the reporting city, province, or country. We performed multivariate logistic regression analysis to identify risk factors for sudden death after diagnosis of COVID-19. RESULTS: Among the 30,302 deaths, there were 7,258 (24.0%) and 23,044 (76.0%) sudden and non-sudden deaths, respectively. Sudden death means a person who died within 2 days of diagnosis and who did not receive inpatient treatment. Underlying condition, vaccination status, and place of death were significantly associated with the survival period in all age groups. Moreover, region, sex, and prescription were significantly associated with the survival period only in certain age groups. However, reinfection was not significantly associated with the survival period in any age group. CONCLUSION: To our knowledge, this is the first study on the risk factors for sudden death after a diagnosis of COVID-19, which included age, underlying condition, vaccination status, and place of death. Additionally, individuals aged < 60 years without an underlying condition were at high risk for sudden death. However, this group has relatively low interest in health, as can be seen from the high non-vaccination rate (16.1% of the general population vs. 61.6% of the corresponding group). Therefore, there is a possibility for the presence of an uncontrolled underlying disease in this population. In addition, many sudden deaths occurred due to delayed hospital visits to continue economic activities even after the onset of COVID-19 symptoms (7 days overall vs. 10 days average for the group). In conclusion, 'continued interest in health' is a key factor in avoiding sudden death in the economically active group (under 60 years of age).

Contact Investigations With a Single Tuberculin Skin Test on Infants Exposed to Tuberculosis in a Postpartum Care Center During the Neonatal Period.

BACKGROUND: Tuberculosis (TB) exposure in congregate settings related to neonates is a serious medical and social issue. TB exposure happens during the neonatal period, but contact investigations for exposed infants are usually conducted after the neonatal period. Generally, recommendations for screening and managing close contact are different for neonates and children. Thus, there are challenges in contact investigations. We aimed to report contact investigations with a single tuberculin skin test (TST) on infants exposed to infectious TB in a postpartum care center. METHODS: The index case was a healthcare worker with active pulmonary TB: sputum acid-fast bacilli smear negative, culture positive, and no cavitary lesion. All exposed infants underwent medical examinations and chest X-ray. After TB disease was ruled out, contacts received window period prophylaxis with isoniazid (INH) until three months after the last exposure. TST was performed only once after completing the prophylaxis. RESULTS: A total of 288 infants were selected as high-priority contacts. At the initial contact investigation, the age of infants ranged from 8 to 114 days. None of these exposed infants had TB disease. The prevalence of latent TB infection (LTBI) was 25.3% (73/288; 95% confidence interval [CI], 20.7-30.7). There were no serious adverse events related to the window period prophylaxis or LTBI treatment with INH. During the 1-year follow-up period, no infants progressed to overt TB disease. The size of TST induration in infants vaccinated with percutaneous Bacillus Calmette-Guérin (BCG) vaccine was significantly larger than that of infants vaccinated with intradermal BCG vaccine (median, 8 mm vs. 5 mm; P = 0.002). In multiple logistic regression analysis, independent factors associated with TST positivity (≥ 10 mm induration) were male (adjusted odds ratio [aOR], 2.98; 95% CI, 1.6-5.64), percutaneous BCG vaccination (aOR, 3.30; 95% CI, 1.75-6.48), TST reading between 60 and 72 hours after injecting purified protein derivative (aOR, 2.87; 95% CI, 1.53-5.49), and INH prophylaxis more than four weeks (aOR, 0.49; 95% CI, 0.25-0.94). CONCLUSION: A single TST at three months after the last TB exposure with INH prophylaxis could be used as a main protocol in contact investigations for infants exposed to infectious TB during the neonatal period in congregate settings in Korea.

Pediatric Deaths Associated With Coronavirus Disease 2019 (COVID-19) in Korea.

As of September 3, 2022, 5,388,338 coronavirus disease 2019 (COVID-19) cases and 46 deaths (3 in 2021 and 43 in 2022) were reported in children ≤ 18 years in Korea. Cumulative confirmed cases accounted for 67.3% of the population aged ≤ 18 years and case fatality rate was 0.85/100,000. Among 46 fatal cases, 58.7% were male and median age was 7 years. Underlying diseases were present in 47.8%; neurologic diseases (63.6%) and malignancy (13.6%) most common. Only four had history of COVID-19 immunization. COVID-19 associated deaths occurred at median 2 days from diagnosis (range: -1 to 21). Among COVID-19 deaths, 41.3% occurred before admission; 2 before hospital arrival and 17 in the emergency department. Among children whose cause was documented, myocarditis, respiratory and multiorgan failure were most common. COVID-19 associated death was seen early after diagnosis in children and public health policies to provide access to medical care for children with COVID-19 are essential during the pandemic.

Etiology and Clinical Characteristics of Community-Acquired Pneumonia in Korean Children During the Pre-COVID-19 Period, 2015-2020.

BACKGROUND: There have been many epidemiologic studies on community-acquired pneumonia (CAP) among children, most of which had substantial limitations. This study investigated the etiologic distribution and clinical characteristics of CAP in Korean children for 5 years before the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A retrospective analysis of children hospitalized for CAP at 4 referral hospitals during 2015-2020 was performed. Cases in which bronchiolitis was suspected or pulmonary infiltration was not evident on chest radiography (CXR) were excluded. Viruses and atypical bacteria were defined as detected when positive in the polymerase chain reaction test performed for respiratory specimens. Serologic testing result for Mycoplasma pneumoniae was incorporated with strict interpretation. Pyogenic bacteria were included only when cultured in blood, pleural fluid, or bronchoalveolar lavage, but those cultured in endotracheal aspirate or sputum when the case was clinically evident bacterial pneumonia were also included. RESULTS: A total of 2,864 cases of suspected pneumonia were selected by diagnosis code and CXR findings. Medical chart and CXR review excluded nosocomial pneumonia and cases without evident infiltration, resulting in 517 (18.1%) CAP cases among 489 children. Regarding clinical symptoms, high fever was present in 59.4% and dyspnea in 19.9% of cases. Respiratory support was required for 29.2% of patients, including mechanical ventilation for 3.9%. Pathogens were detected in 49.9% of cases, with viruses in 32.3%, atypical bacteria in 17.8%, and pyogenic bacteria in 2.3% of cases. As single pathogens, M. pneumoniae (16.8%) and respiratory syncytial virus (RSV, 13.7%) were the most common. Parenteral ß-lactam and macrolide antibiotics were administered in 81.6% and 50.7% of cases, respectively. A total of 12 (2.3%) cases resulted in poor outcomes, including 3 deaths. CONCLUSION: M. pneumoniae and RSV were the most commonly detected pathogens of pediatric CAP, which was selected by strict clinical and radiologic criteria. It is necessary to carefully decide whether to use parenteral antibiotics based on the epidemiology and clinical features of CAP in children.

Twenty-Five Year Trend Change in the Etiology of Pediatric Invasive Bacterial Infections in Korea, 1996-2020.

BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic has contributed to the change in the epidemiology of many infectious diseases. This study aimed to establish the pre-pandemic epidemiology of pediatric invasive bacterial infection (IBI). METHODS: A retrospective multicenter-based surveillance for pediatric IBIs has been maintained from 1996 to 2020 in Korea. IBIs caused by eight bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, Staphylococcus aureus, Streptococcus agalactiae, Streptococcus pyogenes, Listeria monocytogenes, and Salmonella species) in immunocompetent children > 3 months of age were collected at 29 centers. The annual trend in the proportion of IBIs by each pathogen was analyzed. RESULTS: A total of 2,195 episodes were identified during the 25-year period between 1996 and 2020. S. pneumoniae (42.4%), S. aureus (22.1%), and Salmonella species (21.0%) were common in children 3 to 59 months of age. In children ≥ 5 years of age, S. aureus (58.1%), followed by Salmonella species (14.8%) and S. pneumoniae (12.2%) were common. Excluding the year 2020, there was a trend toward a decrease in the relative proportions of S. pneumoniae (rs = -0.430, P = 0.036), H. influenzae (rs = -0.922, P < 0.001), while trend toward an increase in the relative proportion of S. aureus (rs = 0.850, P < 0.001), S. agalactiae (rs = 0.615, P = 0.001), and S. pyogenes (rs = 0.554, P = 0.005). CONCLUSION: In the proportion of IBIs over a 24-year period between 1996 and 2019, we observed a decreasing trend for S. pneumoniae and H. influenzae and an increasing trend for S. aureus, S. agalactiae, and S. pyogenes in children > 3 months of age. These findings can be used as the baseline data to navigate the trend in the epidemiology of pediatric IBI in the post COVID-19 era.

A Study on Optimization of Noise Reduction of Powered Vehicle Seat Movement Using Brushless Direct-Current Motor.

In this paper, an optimal design model was developed to reduce noise and secure the torque performance of a brushless direct-current motor used in the seat of an autonomous vehicle. An acoustic model using finite elements was developed and verified through the noise test of the brushless direct-current motor. In order to reduce noise in the brushless direct-current motor and obtain a reliable optimization geometry of noiseless seat motion, parametric analysis was performed through the design of experiments and Monte Carlo statistical analysis. The slot depth, stator tooth width, slot opening, radial depth, and undercut angle of the brushless direct-current motor were selected as design parameters for design parameter analysis. Then, a non-linear prediction model was used to determine the optimal slot depth and stator tooth width to maintain the drive torque and minimize the sound pressure level at 23.26 dB or lower. The Monte Carlo statistical method was used to minimize the deviation of the sound pressure level caused by the production deviation of the design parameters. The result is that the SPL was 23.00-23.50 dB with a confidence level of approximately 99.76% when the level of production quality control was set at 3σ.

VDUP1 Deficiency Promotes the Severity of DSS-Induced Colitis in Mice by Inducing Macrophage Infiltration.

The loss of vitamin D3 upregulated protein 1 (VDUP1) has been implicated in the pathogenesis of various inflammation-related diseases. Notably, reduced expression of VDUP1 has been observed in clinical specimens of ulcerative colitis (UC). However, the role of VDUP1 deficiency in colitis remains unclear. In this study, we investigated the role of VDUP1 in dextran sulfate sodium (DSS)-induced experimental colitis in mice. VDUP1-deficient mice were more susceptible to DSS-induced colitis than their wild-type (WT) littermates after 2% DSS administration. VDUP1-deficient mice exhibited an increased disease activity index (DAI) and histological scores, as well as significant colonic goblet cell loss and an increase in apoptotic cells. These changes were accompanied by a significant decrease in MUC2 mRNA expression and a marked increase in proinflammatory cytokines and chemokines within damaged tissues. Furthermore, phosphorylated NF-κB p65 expression was significantly upregulated in damaged tissues in the context of VDUP1 deficiency. VDUP1 deficiency also led to significant infiltration of macrophages into the site of ulceration. An in vitro chemotaxis assay confirmed that VDUP1 deficiency enhanced bone marrow-derived macrophage (BMDM) chemotaxis induced by CCL2. Overall, this study highlights VDUP1 as a regulator of UC pathogenesis and a potential target for the future development of therapeutic strategies.

Dapagliflozin attenuates diabetes-induced diastolic dysfunction and cardiac fibrosis by regulating SGK1 signaling.

BACKGROUND: Recent studies have reported improved diastolic function in patients administered sodium-glucose cotransporter 2 inhibitors (SGLT2i). We aimed to investigate the effect of dapagliflozin on left ventricular (LV) diastolic function in a diabetic animal model and to determine the molecular and cellular mechanisms underlying its function. METHODS: A total of 30 male New Zealand white rabbits were randomized into control, diabetes, or diabetes+dapagliflozin groups (n = 10/per each group). Diabetes was induced by intravenous alloxan. Cardiac function was evaluated using echocardiography. Myocardial samples were obtained for histologic and molecular evaluation. For cellular evaluation, fibrosis-induced cardiomyoblast (H9C2) cells were obtained, and transfection was performed for mechanism analysis (serum and glucocorticoid-regulated kinase 1 (SGK1) signaling analysis). RESULTS: The diabetes+dapagliflozin group showed attenuation of diastolic dysfunction compared with the diabetes group. Dapagliflozin inhibited myocardial fibrosis via inhibition of SGK1 and epithelial sodium channel (ENaC) protein, which was observed both in myocardial tissue and H9C2 cells. In addition, dapagliflozin showed an anti-inflammatory effect and ameliorated mitochondrial disruption. Inhibition of SGK1 expression by siRNA decreased and ENaC and Na+/H+ exchanger isoform 1 (NHE1) expression was confirmed as significantly reduced as siSGK1 in the diabetes+dapagliflozin group. CONCLUSIONS: Dapagliflozin attenuated left ventricular diastolic dysfunction and cardiac fibrosis via regulation of SGK1 signaling. Dapagliflozin also reduced macrophages and inflammatory proteins and ameliorated mitochondrial disruption.

Nilotinib modulates LPS-induced cognitive impairment and neuroinflammatory responses by regulating P38/STAT3 signaling.

BACKGROUND: In chronic myelogenous leukemia, reciprocal translocation between chromosome 9 and chromosome 22 generates a chimeric protein, Bcr-Abl, that leads to hyperactivity of tyrosine kinase-linked signaling transduction. The therapeutic agent nilotinib inhibits Bcr-Abl/DDR1 and can cross the blood-brain barrier, but its potential impact on neuroinflammatory responses and cognitive function has not been studied in detail. METHODS: The effects of nilotinib in vitro and in vivo were assessed by a combination of RT-PCR, real-time PCR, western blotting, ELISA, immunostaining, and/or subcellular fractionation. In the in vitro experiments, the effects of 200 ng/mL LPS or PBS on BV2 microglial cells, primary microglia or primary astrocytes pre- or post-treated with 5 µM nilotinib or vehicle were evaluated. The in vivo experiments involved wild-type mice administered a 7-day course of daily injections with 20 mg/kg nilotinib (i.p.) or vehicle before injection with 10 mg/kg LPS (i.p.) or PBS. RESULTS: In BV2 microglial cells, pre- and post-treatment with nilotinib altered LPS-induced proinflammatory/anti-inflammatory cytokine mRNA levels by suppressing AKT/P38/SOD2 signaling. Nilotinib treatment also significantly downregulated LPS-stimulated proinflammatory cytokine levels in primary microglia and primary astrocytes by altering P38/STAT3 signaling. Experiments in wild-type mice showed that nilotinib administration affected LPS-mediated microglial/astroglial activation in a brain region-specific manner in vivo. In addition, nilotinib significantly reduced proinflammatory cytokine IL-1ß, IL-6 and COX-2 levels and P38/STAT3 signaling in the brain in LPS-treated wild-type mice. Importantly, nilotinib treatment rescued LPS-mediated spatial working memory impairment and cortical dendritic spine number in wild-type mice. CONCLUSIONS: Our results indicate that nilotinib can modulate neuroinflammatory responses and cognitive function in LPS-stimulated wild-type mice.

Regulation of habenular G-protein gamma 8 on learning and memory via modulation of the central acetylcholine system.

Guanine nucleotide binding protein (G protein) gamma 8 (Gng8) is a subunit of G proteins and expressed in the medial habenula (MHb) and interpeduncular nucleus (IPN). Recent studies have demonstrated that Gng8 is involved in brain development; however, the roles of Gng8 on cognitive function have not yet been addressed. In the present study, we investigated the expression of Gng8 in the brain and found that Gng8 was predominantly expressed in the MHb-IPN circuit of the mouse brain. We generated Gng8 knockout (KO) mice by CRISPR/Cas9 system in order to assess the role of Gng8 on cognitive function. Gng8 KO mice exhibited deficiency in learning and memory in passive avoidance and Morris water maze tests. In addition, Gng8 KO mice significantly reduced long-term potentiation (LTP) in the hippocampus compared to that of wild-type (WT) mice. Furthermore, we observed that levels of acetylcholine (ACh) and choline acetyltransferase (ChAT) in the MHb and IPN of Gng8 KO mice were significantly decreased, compared to WT mice. The administration of nAChR α4ß2 agonist A85380 rescued memory impairment in the Gng8 KO mice, suggesting that Gng8 regulates cognitive function via modulation of cholinergic activity. Taken together, Gng8 is a potential therapeutic target for memory-related diseases and/or neurodevelopmental diseases.

Artificial intelligence system for identification of false-negative interpretations in chest radiographs.

OBJECTIVES: To investigate the efficacy of an artificial intelligence (AI) system for the identification of false negatives in chest radiographs that were interpreted as normal by radiologists. METHODS: We consecutively collected chest radiographs that were read as normal during 1 month (March 2020) in a single institution. A commercialized AI system was retrospectively applied to these radiographs. Radiographs with abnormal AI results were then re-interpreted by the radiologist who initially read the radiograph ("AI as the advisor" scenario). The reference standards for the true presence of relevant abnormalities in radiographs were defined by majority voting of three thoracic radiologists. The efficacy of the AI system was evaluated by detection yield (proportion of true-positive identification among the entire examination) and false-referral rate (FRR, proportion of false-positive identification among all examinations). Decision curve analyses were performed to evaluate the net benefits of applying the AI system. RESULTS: A total of 4208 radiographs from 3778 patients (M:F = 1542:2236; median age, 56 years) were included. The AI system identified initially overlooked relevant abnormalities with a detection yield and an FRR of 2.4% and 14.0%, respectively. In the "AI as the advisor" scenario, radiologists detected initially overlooked relevant abnormalities with a detection yield and FRR of 1.2% and 0.97%, respectively. In a decision curve analysis, AI as an advisor scenario exhibited a positive net benefit when the cost-to-benefit ratio was below 1:0.8. CONCLUSION: An AI system could identify relevant abnormalities overlooked by radiologists and could enable radiologists to correct their false-negative interpretations by providing feedback to radiologists. KEY POINTS: • In consecutive chest radiographs with normal interpretations, an artificial intelligence system could identify relevant abnormalities that were initially overlooked by radiologists. • The artificial intelligence system could enable radiologists to correct their initial false-negative interpretations by providing feedback to radiologists when overlooked abnormalities were present.