Ultrathin Serpentine Insulation Layer Architecture for Ultralow Power Gas Sensor.

Toxic gases have surreptitiously influenced the health and environment of contemporary society with their odorless/colorless characteristics. As a result, a pressing need for reliable and portable gas-sensing devices has continuously increased. However, with their negligence to efficiently microstructure their bulky supportive layer on which the sensing and heating materials are located, previous semiconductor metal-oxide gas sensors have been unable to fully enhance their power efficiency, a critical factor in power-stringent portable devices. Herein, an ultrathin insulation layer with a unique serpentine architecture is proposed for the development of a power-efficient gas sensor, consuming only 2.3 mW with an operating temperature of 300 °C (≈6% of the leading commercial product). Utilizing a mechanically robust serpentine design, this work presents a fully suspended standalone device with a supportive layer thickness of only ≈50 nm. The developed gas sensor shows excellent mechanical durability, operating over 10 000 on/off cycles and ≈2 years of life expectancy under continuous operation. The gas sensor detected carbon monoxide concentrations from 30 to 1 ppm with an average response time of ≈15 s and distinguishable sensitivity to 1 ppm (ΔR/R0 = 5%). The mass-producible fabrication and heating efficiency presented here provide an exemplary platform for diverse power-efficient-related devices.

Pulmonary Foreign Body Granulomatosis after Cosmetic Injection.

INTRODUCTION: We occasionally encounter irregular marginated masses discovered incidentally in young individuals. In most cases, further investigations are conducted to assess the presence of a primary malignancy, as these masses often raise suspicions of malignancy. However, rare exceptional cases leave us perplexed. Granulomas arising from common lung infections and those induced by foreign substances can often pose challenge in distinguishing them from lung cancer. Therefore, we aimed to present a case of multiple pulmonary granulomatosis following cosmetic procedure. CASE PRESENTATION: A 55-year-old woman visited the hospital after an incidental discovery of an abnormal chest radiograph during a routine health check-up. Subsequent computed tomography (CT) scans showed worrisome lung nodules, leading to biopsies and positron emission tomography CT scans. Histological examination of the biopsied specimens revealed a chronic inflammatory reaction surrounded by multinucleated foreign body giant cells. Upon sharing the biopsy results with the patient and conducting additional history-taking, she had undergone various cosmetic procedures (botox injection, dermal filler treatments, and thread lifts) around the face and neck, approximately 5-6 months ago. It was hypothesized that these cosmetic materials might have led to the observed pulmonary granulomatosis. After 3 months of conservative care, a follow-up CT showed no change in the lesions. CONCLUSION: We present this case to underscore the importance of considering pulmonary foreign body granulomatosis as a potential differential diagnosis, especially when it closely resembles lung cancer, particularly following cosmetic injections.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Impact of interstitial lung abnormalities on postoperative pulmonary complications and survival of lung cancer.

BACKGROUND: Interstitial lung abnormalities (ILAs) are associated with the risk of lung cancer and its mortality. However, the impact of ILA on treatment-related complications and survival in patients who underwent curative surgery is still unknown. RESEARCH QUESTION: This study aimed to evaluate the significance of the presence of computed tomography-diagnosed ILA and histopathologically matched interstitial abnormalities on postoperative pulmonary complications (PPCs) and the long-term survival of patients who underwent surgical treatment for lung cancer. STUDY DESIGN AND METHODS: A matched case-control study was designed to compare PPCs and mortality among 50 patients with ILA, 50 patients with idiopathic pulmonary fibrosis (IPF) and 200 controls. Cases and controls were matched by sex, age, smoking history, tumour location, the extent of surgery, tumour histology and pathological TNM stage. RESULTS: Compared with the control group, the OR of the prevalence of PPCs increased to 9.56 (95% CI 2.85 to 32.1, p<0.001) in the ILA group and 56.50 (95% CI 17.92 to 178.1, p<0.001) in the IPF group. The 5-year overall survival (OS) rates of the control, ILA and IPF groups were 76% (95% CI 71% to 83%), 52% (95% CI 37% to 74%) and 32% (95% CI 19% to 53%), respectively (log-rank p<0.001). Patients with ILA had better 5-year OS than those with IPF (log-rank p=0.046) but had worse 5-year OS than those in the control group (log-rank p=0.002). CONCLUSIONS: The presence of radiological and pathological features of ILA in patients with lung cancer undergoing curative surgery was associated with frequent complications and decreased survival.

Comparison of Clinical Outcomes and Imaging Features in Hospitalized Patients with SARS-CoV-2 Omicron Subvariants.

Background Differences in the clinical and radiological characteristics of SARS-CoV-2 Omicron subvariants have not been well studied. Purpose To compare clinical disease severity and radiologically severe pneumonia in patients with COVID-19 hospitalized during a period of either Omicron BA.1/BA.2 or Omicron BA.5 subvariant predominance. Materials and Methods This multicenter retrospective study, included patients registered in the Korean Imaging Cohort of COVID-19 database who were hospitalized for COVID-19 between January and December 2022. Publicly available relative variant genome frequency data were used to determine the dominant periods of Omicron BA.1/BA.2 subvariants (January 17 to June 20, 2022) and the Omicron BA.5 subvariant (July 4 to December 5, 2022). Clinical outcomes and imaging pneumonia outcomes based on chest radiography and CT were compared among predominant subvariants using multivariable analyses adjusted for covariates. Results Of 1916 confirmed patients with COVID-19 (mean age, 72 years ± 16 [SD]; 1019 males), 1269 were registered during the Omicron BA.1/BA.2 subvariant dominant period and 647 during the Omicron BA.5 subvariant dominant period. Patients in the BA.5 group showed lower odds of high-flow O2 requirement (adjusted odds ratio [OR], 0.75 [95% CI: 0.57, 0.99]; P = .04), mechanical ventilation (adjusted OR, 0.49 [95% CI: 0.34, 0.72]; P < .001]), and death (adjusted OR, 0.47 [95% CI: 0.33, 0.68]; P <.001) than those in the BA.1/BA.2 group. Additionally, the BA.5 group had lower odds of severe pneumonia on chest radiographs (adjusted OR, 0.68 [95% CI: 0.53, 0.88]; P = .004) and higher odds of atypical pattern pneumonia on CT images (adjusted OR, 1.81 [95% CI: 1.26, 2.58]; P = .001) than the BA.1/BA.2 group. Conclusions Patients hospitalized during the period of Omicron BA.5 subvariant predominance had lower odds of clinical and pneumonia severity than those hospitalized during the period of Omicron BA.1/BA.2 predominance, even after adjusting for covariates. See also the editorial by Hammer in this issue.

SARS-CoV-2 Variants Infection in Relationship to Imaging-based Pneumonia and Clinical Outcomes.

Background Few reports have evaluated the effect of the SARS-CoV-2 variant and vaccination on the clinical and imaging features of COVID-19. Purpose To evaluate and compare the effect of vaccination and variant prevalence on the clinical and imaging features of infections by the SARS-CoV-2. Materials and Methods Consecutive adults hospitalized for confirmed COVID-19 at three centers (two academic medical centers and one community hospital) and registered in a nationwide open data repository for COVID-19 between August 2021 and March 2022 were retrospectively included. All patients had available chest radiographs or CT images. Patients were divided into two groups according to predominant variant type over the study period. Differences between clinical and imaging features were analyzed with use of the Pearson χ2 test, Fisher exact test, or the independent t test. Multivariable logistic regression analyses were used to evaluate the effect of variant predominance and vaccination status on imaging features of pneumonia and clinical severity. Results Of the 2180 patients (mean age, 57 years ± 21; 1171 women), 1022 patients (47%) were treated during the Delta variant predominant period and 1158 (53%) during the Omicron period. The Omicron variant prevalence was associated with lower pneumonia severity based on CT scores (odds ratio [OR], 0.71 [95% CI: 0.51, 0.99; P = .04]) and lower clinical severity based on intensive care unit (ICU) admission or in-hospital death (OR, 0.43 [95% CI: 0.24, 0.77; P = .004]) than the Delta variant prevalence. Vaccination was associated with the lowest odds of severe pneumonia based on CT scores (OR, 0.05 [95% CI: 0.03, 0.13; P < .001]) and clinical severity based on ICU admission or in-hospital death (OR, 0.15 [95% CI: 0.07, 0.31; P < .001]) relative to no vaccination. Conclusion The SARS-CoV-2 Omicron variant prevalence and vaccination were associated with better clinical outcomes and lower severe pneumonia risk relative to Delta variant prevalence. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Little in this issue.

Disrupted social memory ensembles in the ventral hippocampus underlie social amnesia in autism-associated Shank3 mutant mice.

The ability to remember conspecifics is critical for adaptive cognitive functioning and social communication, and impairments of this ability are hallmarks of autism spectrum disorders (ASDs). Although hippocampal ventral CA1 (vCA1) neurons are known to store social memories, how their activities are coordinated remains unclear. Here we show that vCA1 social memory neurons, characterized by enhanced activity in response to memorized individuals, were preferentially reactivated during sharp-wave ripples (SPW-Rs). Spike sequences of these social replays reflected the temporal orders of neuronal activities within theta cycles during social experiences. In ASD model Shank3 knockout mice, the proportion of social memory neurons was reduced, and neuronal ensemble spike sequences during SPW-Rs were disrupted, which correlated with impaired discriminatory social behavior. These results suggest that SPW-R-mediated sequential reactivation of neuronal ensembles is a canonical mechanism for coordinating hippocampus-dependent social memories and its disruption underlie the pathophysiology of social memory defects associated with ASD.

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Erratum: Correction of Affiliations in the Article "Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019".

This corrects the article on p. e413 in vol. 35, PMID: 33258333.

Anti-Cancer Roles of Probiotic-Derived P8 Protein in Colorectal Cancer Cell Line DLD-1.

A novel probiotics-derived protein, P8, suppresses the growth of colorectal cancer (CRC). P8 can penetrate the cell membrane via endocytosis and cause cell cycle arrest in DLD-1 cells through down-regulation of CDK1/Cyclin B1. However, neither the protein involved in the endocytosis of P8 nor the cell cycle arrest targets of P8 are known. We identified two P8-interacting target proteins [importin subunit alpha-4 (KPNA3) and glycogen synthase kinase-3 beta (GSK3ß)] using P8 as a bait in pull-down assays of DLD-1 cell lysates. Endocytosed P8 in the cytosol was found to bind specifically to GSK3ß, preventing its inactivation by protein kinases AKT/CK1ε/PKA. The subsequent activation of GSK3ß led to strong phosphorylation (S33,37/T41) of ß-catenin, resulting in its subsequent degradation. P8 in the cytosol was also found to be translocated into the nucleus by KPNA3 and importin. In the nucleus, after its release, P8 binds directly to the intron regions of the GSK3ß gene, leading to dysregulation of GSK3ß transcription. GSK3ß is a key protein kinase in Wnt signaling, which controls cell proliferation during CRC development. P8 can result in a cell cycle arrest morphology in CRC cells, even when they are in the Wnt ON signaling state.

Imaging and Clinical Features of COVID-19 Breakthrough Infections: A Multicenter Study.

Background Since vaccines against COVID-19 became available, rare breakthrough infections have been reported despite their high efficacies. Purpose To evaluate the clinical and imaging characteristics of patients with COVID-19 breakthrough infections and compare them with those of unvaccinated patients with COVID-19. Materials and Methods In this retrospective multicenter cohort study, the authors analyzed patient (aged ≥18 years) data from three centers that were registered in an open data repository for COVID-19 between June and August 2021. Hospitalized patients with baseline chest radiographs were divided into three groups according to their vaccination status. Differences between clinical and imaging features were analyzed using the Pearson χ2 test, Fisher exact test, and analysis of variance. Univariable and multivariable logistic regression analyses were used to evaluate associations between clinical factors, including vaccination status and clinical outcomes. Results Of the 761 hospitalized patients with COVID-19, the mean age was 47 years and 385 (51%) were women; 47 patients (6%) were fully vaccinated (breakthrough infection), 127 (17%) were partially vaccinated, and 587 (77%) were unvaccinated. Of the 761 patients, 412 (54%) underwent chest CT during hospitalization. Among the patients who underwent CT, the proportions without pneumonia were 22% of unvaccinated patients (71 of 326), 30% of partially vaccinated patients (19 of 64), and 59% of fully vaccinated patients (13 of 22) (P < .001). Fully vaccinated status was associated with a lower risk of requiring supplemental oxygen (odds ratio [OR], 0.24 [95% CI: 0.09, 0.64; P = .005]) and lower risk of intensive care unit admission (OR, 0.08 [95% CI: 0.09, 0.78; P = .02]) compared with unvaccinated status. Conclusion Patients with COVID-19 breakthrough infections had a significantly higher proportion of CT scans without pneumonia compared with unvaccinated patients. Vaccinated patients with breakthrough infections had a lower likelihood of requiring supplemental oxygen and intensive care unit admission. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Schiebler and Bluemke in this issue.

Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movements of the tympanic membrane and part of the ossicles, analyzing VPO images was expected to be useful in predicting the presence of middle ear transmission problems. Using a convolutional neural network (CNN), a deep neural network implementing computer vision, this preliminary study aimed to create a deep learning model that detects the presence of an air-bone gap, conductive component of hearing loss, by analyzing VPO findings. DESIGN: The medical records of adult patients who underwent VPO tests and pure-tone audiometry (PTA) on the same day were reviewed for enrollment. Conductive hearing loss was defined as an average air-bone gap of more than 10 dB at 0.5, 1, 2, and 4 kHz on PTA. Two significant images from the original VPO videos, at the most medial position on positive pressure and the most laterally displaced position on negative pressure, were used for the analysis. Applying multi-column CNN architectures with individual backbones of pretrained CNN versions, the performance of each model was evaluated and compared for Inception-v3, VGG-16 or ResNet-50. The diagnostic accuracy predicting the presence of conductive component of hearing loss of the selected deep learning algorithm used was compared with experienced otologists. RESULTS: The conductive hearing loss group consisted of 57 cases (mean air-bone gap = 25 ± 8 dB): 21 ears with effusion, 14 ears with malleus-incus fixation, 15 ears with stapes fixation including otosclerosis, one ear with a loose incus-stapes joint, 3 cases with adhesive otitis media, and 3 ears with middle ear masses including congenital cholesteatoma. The control group consisted of 76 cases with normal hearing thresholds without air-bone gaps. A total of 1130 original images including repeated measurements were obtained for the analysis. Of the various network architectures designed, the best was to feed each of the images into the individual backbones of Inception-v3 (three-column architecture) and concatenate the feature maps after the last convolutional layer from each column. In the selected model, the average performance of 10-fold cross-validation in predicting conductive hearing loss was 0.972 mean areas under the curve (mAUC), 91.6% sensitivity, 96.0% specificity, 94.4% positive predictive value, 93.9% negative predictive value, and 94.1% accuracy, which was superior to that of experienced otologists, whose performance had 0.773 mAUC and 79.0% accuracy on average. The algorithm detected over 85% of cases with stapes fixations or ossicular chain problems other than malleus-incus fixations. Visualization of the region of interest in the deep learning model revealed that the algorithm made decisions generally based on findings in the malleus and nearby tympanic membrane. CONCLUSIONS: In this preliminary study, the deep learning algorithm created to analyze VPO images successfully detected the presence of conductive hearing losses caused by middle ear effusion, ossicular fixation, otosclerosis, and adhesive otitis media. Interpretation of VPO using the deep learning algorithm showed promise as a diagnostic tool to differentiate conductive hearing loss from sensorineural hearing loss, which would be especially useful for patients with poor cooperation.

Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort.

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients' initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696−0.788), 0.794 (0.745−0.843) and 0.770 (0.724−0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820−0.889) than that of all other models (p < 0.001, using DeLong's test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.

A modified Tet-ON system minimizing leaky expression for cell-type specific gene induction in medaka fish.

The Tet-ON system is an important molecular tool for temporally and spatially-controlled inducible gene expression. Here, we developed a Tet-ON system to induce transgene expression specifically in the rod photoreceptors of medaka fish. Our modified reverse tetracycline-controlled transcriptional transactivator (rtTAm) with 5 amino acid substitutions dramatically improved the leakiness of the transgene in medaka fish. We generated a transgenic line carrying a self-reporting vector with the rtTAm gene driven by the Xenopus rhodopsin promoter and a tetracycline response element (TRE) followed by the green fluorescent protein (GFP) gene. We demonstrated that GFP fluorescence was restricted to the rod photoreceptors in the presence of doxycycline in larval fish (9 days post-fertilization). The GFP fluorescence intensity was enhanced with longer durations of doxycycline treatment up to 72 h and in a dose-dependent manner (5-45 µg/ml). These findings demonstrate that the Tet-ON system using rtTAm allows for spatiotemporal control of transgene expression, at least in the rod photoreceptors, in medaka fish.

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study.

BACKGROUND: Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorporated into the medical field. In our previous studies, an automated algorithm for EH analysis was developed by using a convolutional neural network. However, several limitations existed, and further studies were conducted to compensate for these limitations. OBJECTIVE: The aim of this study is to develop a fully automated analytic system for measuring EH ratios that enhances EH analysis accuracy and clinical usability when studying Ménière disease via MRI. METHODS: We proposed the 3into3Inception and 3intoUNet networks. Their network architectures were based on those of the Inception-v3 and U-Net networks, respectively. The developed networks were trained for inner ear segmentation by using the magnetic resonance images of 124 people and were embedded in a new, automated EH analysis system-inner-ear hydrops estimation via artificial intelligence (INHEARIT)-version 2 (INHEARIT-v2). After fivefold cross-validation, an additional test was performed by using 60 new, unseen magnetic resonance images to evaluate the performance of our system. The INHEARIT-v2 system has a new function that automatically selects representative images from a full MRI stack. RESULTS: The average segmentation performance of the fivefold cross-validation was measured via the intersection of union method, resulting in performance values of 0.743 (SD 0.030) for the 3into3Inception network and 0.811 (SD 0.032) for the 3intoUNet network. The representative magnetic resonance slices (ie, from a data set of unseen magnetic resonance images) that were automatically selected by the INHEARIT-v2 system only differed from a maximum of 2 expert-selected slices. After comparing the ratios calculated by experienced physicians and those calculated by the INHEARIT-v2 system, we found that the average intraclass correlation coefficient for all cases was 0.941; the average intraclass correlation coefficient of the vestibules was 0.968, and that of the cochleae was 0.914. The time required for the fully automated system to accurately analyze EH ratios based on a patient's MRI stack was approximately 3.5 seconds. CONCLUSIONS: In this study, a fully automated full-stack magnetic resonance analysis system for measuring EH ratios was developed (named INHEARIT-v2), and the results showed that there was a high correlation between the expert-calculated EH ratio values and those calculated by the INHEARIT-v2 system. The system is an upgraded version of the INHEARIT system; it has higher segmentation performance and automatically selects representative images from an MRI stack. The new model can help clinicians by providing objective analysis results and reducing the workload for interpreting magnetic resonance images.

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.

Diverse sensory cues for individual recognition.

To be social, the ability to recognize and discriminate conspecific individuals is indispensable in social animals, including primates, rodents, birds, fish, and social insects which live in societies or groups. Recent studies using molecular biology, genetics, in vivo and in vitro physiology, and behavioral neuroscientific approaches have provided detailed insights into how animals process and recognize the information of individuals. Here, we review the most distinct sensory modalities for individual recognition in animals, namely, olfaction and vision. In the case of rodents, two polymorphic gene complexes have been identified in their urine as the key and essential pheromonal components for individual recognition: the major histocompatibility complex (MHC) and the major urinary protein (MUP). Animals flexibly utilize MHC and/or MUP, which are detected by the main olfactory epithelium (MOE) and/or the vomeronasal organ (VNO) for various types of social recognition, such as strain recognition, kin recognition, and individual recognition. In contrast, primates, including humans, primarily use facial appearance to identify others. Face recognition in humans and other animals is naturally unique from genetic, cognitive, developmental, and functional points of view. Importantly note that nurture effects during growth phase such as social experience and environment can also shape and tune this special cognitive ability, in order to distinguish subtle differences between individuals. In this review, we address such unique nature and nurture mechanisms for individual recognition.

Exogenous 8-hydroxydeoxyguanosine ameliorates liver fibrosis through the inhibition of Rac1-NADPH oxidase signaling.

BACKGROUND AND AIM: Exogenous 8-hydroxydeoxyguanosine (8-OHdG) was suggested as an inhibitor of Rac1 and NADPH oxidase (NOX). The aim of this study was to evaluate the effects of the exogenous 8-OHdG on hepatic fibrogenesis in vitro and in vivo model of liver fibrosis. METHODS: Adult Sprague-Dawley rats were allocated to sham-operated rats (n = 7), rats that underwent bile duct ligation (BDL) (n = 6), and BDL rats treated with 8-OHdG (60 mg/kg/day by gavage, n = 6). All rats were sacrificed on day 21. Double immunofluorescence staining between either NOX1 or NOX2 and α-smooth muscle actin (SMA) in liver was performed. Hepatic fibrotic contents were assessed by hydroxyproline assay and quantified by Sirius red staining. In vitro, hepatic stellate cell (HSC) line LX-2 and HHSteC cells were stimulated by angiotensin II (10 µM). The reactive oxygen species (ROS) production was measured by confocal microscopy. The expressions of NOX1, NOX2, α-SMA, transforming growth factor (TGF)-ß1, and collagen Iα were analyzed by quantitative real-time polymerase chain reaction or immunoblotting. RESULTS: The 8-OHdG treatment in BDL rats reduced the NOX1 and NOX2 protein expression, which overlapped with α-SMA compared with BDL rats. The 8-OHdG treatment in BDL rats significantly decreased the mRNA expression of NOX1, NOX2, α-SMA, TGF-ß1, and collagen Iα, and fibrotic contents. Increases of ROS production, Rac1 activation, NOX1, NOX2, and fibronectin expression induced by angiotensin II in HSCs were attenuated by 8-OHdG. CONCLUSIONS: Rac1 activation and NOX-derived ROS are implicated to liver fibrosis. The 8-OHdG ameliorates liver fibrosis through the inhibition of Rac1 activation and NOX-derived ROS.

Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography.

BACKGROUND: It is necessary to consider myopic optic disc tilt as it seriously impacts normal ocular parameters. However, ophthalmologic measurements are within inter-observer variability and time-consuming to get. This study aimed to develop and evaluate deep learning models that automatically recognize a myopic tilted optic disc in fundus photography. METHODS: This study used 937 fundus photographs of patients with normal or myopic tilted disc, collected from Samsung Medical Center between April 2016 and December 2018. We developed an automated computer-aided recognition system for optic disc tilt on color fundus photographs via a deep learning algorithm. We preprocessed all images with two image resizing techniques. GoogleNet Inception-v3 architecture was implemented. The performances of the models were compared with the human examiner's results. Activation map visualization was qualitatively analyzed using the generalized visualization technique based on gradient-weighted class activation mapping (Grad-CAM++). RESULTS: Nine hundred thirty-seven fundus images were collected and annotated from 509 subjects. In total, 397 images from eyes with tilted optic discs and 540 images from eyes with non-tilted optic discs were analyzed. We included both eye data of most included patients and analyzed them separately in this study. For comparison, we conducted training using two aspect ratios: the simple resized dataset and the original aspect ratio (AR) preserving dataset, and the impacts of the augmentations for both datasets were evaluated. The constructed deep learning models for myopic optic disc tilt achieved the best results when simple image-resizing and augmentation were used. The results were associated with an area under the receiver operating characteristic curve (AUC) of 0.978 ± 0.008, an accuracy of 0.960 ± 0.010, sensitivity of 0.937 ± 0.023, and specificity of 0.963 ± 0.015. The heatmaps revealed that the model could effectively identify the locations of the optic discs, the superior retinal vascular arcades, and the retinal maculae. CONCLUSIONS: We developed an automated deep learning-based system to detect optic disc tilt. The model demonstrated excellent agreement with the previous clinical criteria, and the results are promising for developing future programs to adjust and identify the effect of optic disc tilt on ophthalmic measurements.