Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity.

Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing health concern in pediatric populations due to its association with obesity and metabolic syndrome. Bioelectrical impedance analysis (BIA) offers a non-invasive and potentially effective alternative for identifying MASLD risk in youth with overweight or obesity. Therefore, this study aimed to assess the utility of BIA for screening for MASLD in the youth. Method: This retrospective, cross-sectional study included 206 children and adolescents aged <20 years who were overweight and obese. The correlations between anthropometric measurements and BIA parameters and alanine aminotransferase (ALT) levels were assessed using Pearson's correlation analysis. Logistic regression analysis was performed to examine the associations between these parameters and ALT level elevation and MASLD score. Receiver operating characteristic (ROC) curves were generated to assess the predictive ability of the parameters for MASLD. Results: Pearson's correlation analysis revealed that waist-to-hip ratio (WHR), percentage body fat (PBF), and BIA parameters combined with anthropometric measurements were correlated with ALT level. Logistic regression revealed that WHR, skeletal muscle mass/WHR, PBF-WHR, fat-free mass/WHR, and appendicular skeletal muscle mass/WHR were correlated with ALT level elevation after adjusting for age, sex, and puberty. WHR, PBF-WHR, and visceral fat area (VFA)-WHR were positively correlated with the MASLD score in the total population after adjusting for age, sex, and puberty. PBF-WHR and VFA-WHR were correlated with the MASLD score even in youth with a normal ALT level. The cutoff points and area under the ROC curves were 34.6 and 0.69 for PBF-WHR, respectively, and 86.6 and 0.79 for VFA-WHR, respectively. Discussion: This study highlights the utility of combining BIA parameters and WHR in identifying the risk of MASLD in overweight and obese youth, even in those with a normal ALT level. BIA-based screening offers a less burdensome and more efficient alternative to conventional MASLD screening methods, facilitating early detection and intervention in youth at risk of MASLD.

Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

PURPOSE: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Intermittent Fasting Reduces Neuroinflammation and Cognitive Impairment in High-Fat Diet-Fed Mice by Downregulating Lipocalin-2 and Galectin-3.

Intermittent fasting (IF), an alternating pattern of dietary restriction, reduces obesity-induced insulin resistance and inflammation. However, the crosstalk between adipose tissue and the hippocampus in diabetic encephalopathy is not fully understood. Here, we investigated the protective effects of IF against neuroinflammation and cognitive impairment in high-fat diet(HFD)-fed mice. Histological analysis revealed that IF reduced crown-like structures and adipocyte apoptosis in the adipose tissue of HFD mice. In addition to circulating lipocalin-2 (LCN2) and galectin-3 (GAL3) levels, IF reduced HFD-induced increases in LCN2- and GAL3-positive macrophages in adipose tissue. IF also improved HFD-induced memory deficits by inhibiting blood-brain barrier breakdown and neuroinflammation. Furthermore, immunofluorescence showed that IF reduced HFD-induced astrocytic LCN2 and microglial GAL3 protein expression in the hippocampus of HFD mice. These findings indicate that HFD-induced adipocyte apoptosis and macrophage infiltration may play a critical role in glial activation and that IF reduces neuroinflammation and cognitive impairment by protecting against blood-brain barrier leakage.

TonEBP Haploinsufficiency Attenuates Microglial Activation and Memory Deficits in Middle-Aged and Amyloid ß Oligomer-Treated Mice.

Age-related microglial activation is associated with cognitive impairment. Tonicity-responsive enhancer-binding protein (TonEBP) is a critical mediator of microglial activation in response to neuroinflammation. However, the precise role of TonEBP in the middle-aged brain is not yet known. We used TonEBP haploinsufficient mice to investigate the role of TonEBP in middle-aged or amyloid ß oligomer (AßO)-injected brains and examined the effect of TonEBP knockdown on AßO-treated BV2 microglial cells. Consistent with an increase in microglial activation with aging, hippocampal TonEBP expression levels were increased in middle-aged (12-month-old) and old (24-month-old) mice compared with young (6-month-old) mice. Middle-aged TonEBP haploinsufficient mice showed reduced microglial activation and fewer memory deficits than wild-type mice. Electron microscopy revealed that synaptic pruning by microglial processes was reduced by TonEBP haploinsufficiency. TonEBP haploinsufficiency also reduced dendritic spine loss and improved memory deficits in AßO-treated mice. Furthermore, TonEBP knockdown attenuated migration and phagocytosis in AßO-treated BV2 cells. These findings suggest that TonEBP plays important roles in age-related microglial activation and memory deficits.

Intermittent Fasting Attenuates Metabolic-Dysfunction-Associated Steatohepatitis by Enhancing the Hepatic Autophagy-Lysosome Pathway.

An intermittent fasting (IF) regimen has been shown to protect against metabolic dysfunction-associated steatohepatitis (MASH). However, the precise mechanism remains unclear. Here, we explored how IF reduced hepatic lipid accumulation, inflammation, and fibrosis in mice with MASH. The mice were fed a high-fat diet (HFD) for 30 weeks and either continued on the HFD or were subjected to IF for the final 22 weeks. IF reduced body weight, insulin resistance, and hepatic lipid accumulation in HFD-fed mice. Lipidome analysis revealed that IF modified HFD-induced hepatic lipid composition. In particular, HFD-induced impaired autophagic flux was reversed by IF. The decreased hepatic lysosome-associated membrane protein 1 level in HFD-fed mice was upregulated in HFD+IF-fed mice. However, increased hepatic lysosomal acid lipase protein levels in HFD-fed mice were reduced by IF. IF attenuated HFD-induced hepatic inflammation and galectin-3-positive Kupffer cells. In addition to the increases in hepatic hydroxyproline and lumican levels, lipocalin-2-mediated signaling was reversed in HFD-fed mice by IF. Taken together, our findings indicate that the enhancement of the autophagy-lysosomal pathway may be a critical mechanism of MASH reduction by IF.

Comparison of image quality, contrast administration, and radiation doses in pediatric abdominal dual-layer detector dual-energy CT using propensity score matching analysis.

PURPOSE: To compare the image quality, contrast administration, and radiation dose between single-energy CT (SECT) and dual-energy CT (DECT) in pediatric patients. METHODS: From March to December 2021, children who underwent abdominal SECT or DECT were retrospectively included in this study. The DECT group received 10-30 % less contrast than the routine dose. CT images were obtained at hepatic venous phase using a routine reconstruction method (iDose4). DECT scans were additionally reconstructed with a virtual monoenergetic image (VMI) at 40 and 65 keV. Quantitative image evaluations compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, portal vein, and pancreas. Qualitative analysis assessed degree of contrast enhancement, lesion or organ conspicuity, image noise, artificiality, and overall image quality. RESULTS: Among 318 patients, 112 (median age, 16 years; 56 in each group) were included after propensity score matching. Compared with the SECT group, DECT group with iDose4 demonstrated lower CNRs and SNRs, while VMI at 40 or 65 keV showed no significant difference. In qualitative analysis, iDose4 produced higher scores on artificiality, and VMI at 40 keV demonstrated superior contrast enhancement and lesion conspicuity in the DECT group. Overall image quality was higher with VMI 65 keV among the DECT patients, and there was no significant difference compared to SECT. The volume CT dose index (CTDIvol) did not differ significantly between the two groups (median, 2.8 mGy vs. 2.9 mGy; p = 0.802). The injected contrast volume was reduced by 10 % in the DECT group. CONCLUSION: Pediatric abdominal DECT with reduced contrast administration showed no significant differences in image quality and radiation dose compared to SECT.

Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence.

This study evaluated how often clinically significant lung nodules were detected unexpectedly on chest radiographs (CXR) by artificial intelligence (AI)-based detection software, and whether co-existing findings can aid in differential diagnosis of lung nodules. Patients (> 18 years old) with AI-detected lung nodules at their first visit from March 2021 to February 2022, except for those in the pulmonology or thoracic surgery departments, were retrospectively included. Three radiologists categorized nodules into malignancy, active inflammation, post-inflammatory sequelae, or "other" groups. Characteristics of the nodule and abnormality scores of co-existing lung lesions were compared. Approximately 1% of patients (152/14,563) had unexpected lung nodules. Among 73 patients with follow-up exams, 69.9% had true positive nodules. Increased abnormality scores for nodules were significantly associated with malignancy (odds ratio [OR] 1.076, P = 0.001). Increased abnormality scores for consolidation (OR 1.033, P = 0.040) and pleural effusion (OR 1.025, P = 0.041) were significantly correlated with active inflammation-type nodules. Abnormality scores for fibrosis (OR 1.036, P = 0.013) and nodules (OR 0.940, P = 0.001) were significantly associated with post-inflammatory sequelae categorization. AI-based lesion-detection software of CXRs in daily practice can help identify clinically significant incidental lung nodules, and referring accompanying lung lesions may help classify the nodule.

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs.

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case-control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6-9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003-1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0-2 days: aOR: 1.025, 95% CI: 1.006-1.045, p = 0.010; 3-5 days: aOR: 1.03 95% CI: 1.011-1.051, p = 0.002; 6-9 days: aOR; 1.052, 95% CI: 1.015-1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.

Efficient Collaboration Between Radiologists Using the PACS-Integrated Refer Function to Reduce Communication Times.

The purpose of this study was to assess the utility of a picture archiving and communication systems (PACS)-integrated refer function for improving collaboration between radiologists and radiographers during daily reading sessions. Retrospective analysis was conducted on refers sent by radiologists using a PACS-integrated refer system from March 2020 to December 2021. Refers were categorized according to receiver: radiologists in the same division (intra-division), radiologists in a different division (inter-division), and radiographers. The proportions of answered refers, content of refers, and timing of refer posts were evaluated. Additionally, time intervals in minutes from initial refer post to refer response were assessed to assess the efficiency of the refer system and compared according to receivers using the Mann-Whitney U test. Among a total of 691 refers posted by radiologists, 579 (83.8%) were answered directly using the refer function in PACS. Of the answered refers, 346 refers (59.8%) were made between radiologists, and 173 (50%) were intra-division refers. About the content of refers, about 82.6% of radiologists' refers were about imaging interpretation consultation, and about 98.9% of refers from radiologists to radiographers were for image quality control. The median time interval until refer response was 9 min, and this response time did not differ between intra-division and inter-division refers (p = 0.998). Of the refers that got responses, 74.3% (257/346) were sent among radiologists before official reports were made, and the median time until refer response was 9-10 min. The proportion of refers answered by radiographers was 85.7% (233/272). The median time interval until refer response by radiographers was 87 min for all refers, and 63% were made within 6 h. Therefore, the PACS-integrated refer function can facilitate communication between radiologists for image interpretation and quality control.

Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients.

This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate -0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005-1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients.

Amyloid ß oligomer promotes microglial galectin-3 and astrocytic lipocalin-2 levels in the hippocampus of mice fed a high-fat diet.

Type 2 diabetes is associated with a risk factor for Alzheimer's disease (AD). Activation of glial cells, such as microglia and astrocytes, is crucial for the development of neuroinflammation in both diabetes and AD. The role of amyloid-beta oligomer (AßO) in the hippocampus of diabetic mice has been investigated; however, the effect of galectin-3 and lipocalin-2 (LCN2) on amyloid toxicity-related glial activation in diabetic mice is not known. To fill this knowledge gap, we fed mice a high-fat diet (HFD) for 20 weeks to induce a diabetic state and then injected the hippocampus with AßO. Sholl analysis of iba-1-positive microglia showed retraction of microglial ramifications in the hippocampus of HFD-fed diabetic mice. AßO treatment caused more retraction of microglial process in HFD-fed mice. In particular, microglial galectin-3 levels and astrocytic LCN2 levels were increased in the hippocampus of HFD-fed mice with AßO treatment. These findings suggest that galectin-3 and LCN2 are involved in amyloid toxicity mechanisms, especially glial activation under diabetic conditions.

The impact of artificial intelligence on the reading times of radiologists for chest radiographs.

Whether the utilization of artificial intelligence (AI) during the interpretation of chest radiographs (CXRs) would affect the radiologists' workload is of particular interest. Therefore, this prospective observational study aimed to observe how AI affected the reading times of radiologists in the daily interpretation of CXRs. Radiologists who agreed to have the reading times of their CXR interpretations collected from September to December 2021 were recruited. Reading time was defined as the duration in seconds from opening CXRs to transcribing the image by the same radiologist. As commercial AI software was integrated for all CXRs, the radiologists could refer to AI results for 2 months (AI-aided period). During the other 2 months, the radiologists were automatically blinded to the AI results (AI-unaided period). A total of 11 radiologists participated, and 18,680 CXRs were included. Total reading times were significantly shortened with AI use, compared to no use (13.3 s vs. 14.8 s, p < 0.001). When there was no abnormality detected by AI, reading times were shorter with AI use (mean 10.8 s vs. 13.1 s, p < 0.001). However, if any abnormality was detected by AI, reading times did not differ according to AI use (mean 18.6 s vs. 18.4 s, p = 0.452). Reading times increased as abnormality scores increased, and a more significant increase was observed with AI use (coefficient 0.09 vs. 0.06, p < 0.001). Therefore, the reading times of CXRs among radiologists were influenced by the availability of AI. Overall reading times shortened when radiologists referred to AI; however, abnormalities detected by AI could lengthen reading times.

Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs.

PURPOSE: To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. MATERIALS AND METHODS: A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest radiographs was conducted with all clinicians and radiologists at our hospital in this prospective study. In our hospital, version 2 of the abovementioned software was utilized from March 2020 to February 2021 and could detect three types of lesions. Version 3 was utilized for chest radiographs by detecting nine types of lesions from March 2021. The participants of this survey answered questions on their own experience using AI-based software in daily practice. The questionnaires were composed of single choice, multiple choices, and scale bar questions. Answers were analyzed according to the clinicians and radiologists using paired t-test and the Wilcoxon rank-sum test. RESULTS: One hundred twenty-three doctors answered the survey, and 74% completed all questions. The proportion of individuals who utilized AI was higher among radiologists than clinicians (82.5% vs. 45.9%, p = 0.008). AI was perceived as being the most useful in the emergency room, and pneumothorax was considered the most valuable finding. Approximately 21% of clinicians and 16% of radiologists changed their own reading results after referring to AI, and trust levels for AI were 64.9% and 66.5%, respectively. Participants thought AI helped reduce reading times and reading requests. They answered that AI helped increase diagnostic accuracy and were more positive about AI after actual usage. CONCLUSION: Actual adaptation of AI for daily chest radiographs received overall positive feedback from clinicians and radiologists in this hospital-wide survey. Participating doctors preferred to use AI and regarded it more favorably after actual working with the AI-based software in daily clinical practice.

Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs.

PURPOSE: Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer. MATERIALS AND METHODS: Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs. RESULTS: Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively. CONCLUSION: This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.

Lipocalin-2 promotes acute lung inflammation and oxidative stress by enhancing macrophage iron accumulation.

Lipocalin-2 (LCN2) is an acute-phase protein that regulates inflammatory responses to bacteria or lipopolysaccharide (LPS). Although the bacteriostatic role of LCN2 is well studied, the function of LCN2 in acute lung damage remains unclear. Here, LCN2 knockout (KO) mice were used to investigate the role of LCN2 in LPS-treated mice with or without recombinant LCN2 (rLCN2). In addition, we employed patients with pneumonia. RAW264.7 cells were given LCN2 inhibition or rLCN2 with or without iron chelator deferiprone. LCN2 KO mice had a higher survival rate than wild-type (WT) mice after LPS treatment. In addition to elevated LCN2 levels in serum and bronchoalveolar lavage fluid (BALF), LPS treatment also increased LCN2 protein in alveolar macrophage lysates of BALF. LCN2 deletion attenuated neutrophil and macrophage infiltration in the lungs of LPS-treated mice as well as serum and BALF interleukin-6 (IL-6). Circulating proinflammatory cytokines and LCN2-positive macrophages were prominently increased in the BALF of pneumonia patients. In addition to increase of iron-stained macrophages in pneumonia patients, increased iron-stained macrophages and oxidative stress in LPS-treated mice were inhibited by LCN2 deletion. In contrast, rLCN2 pretreatment aggravated lung inflammation and oxidative stress in LPS-treated WT mice and then resulted in higher mortality. In RAW264.7 cells, exogenous LCN2 treatment also increased inflammation and oxidative stress, whereas LCN2 knockdown markedly diminished these effects. Furthermore, deferiprone inhibited inflammation, oxidative stress, and phagocytosis in RAW264.7 cells with high LCN2 levels, as well as LPS-induced acute lung injury in WT and LCN2 KO mice. Thus, these findings suggest that LCN2 plays a key role in inflammation and oxidative stress following acute lung injury and that LCN2 is a potential therapeutic target for pneumonia or acute lung injury.

Lipocalin-2 deletion attenuates lipopolysaccharide-induced acute lung inflammation via downregulating chemotaxis-related genes.

Lipocalin-2 (LCN2) is an acute phase protein used as a biomarker for acute lung injury (ALI). Although the innate immune functions of LCN2 have been studied, how LCN2 contributes to ALI induced by lipopolysaccharide (LPS) remains unknown. In this study, we investigated the effect of LCN2 deletion on LPS-induced ALI using RNA-sequencing. LPS-treated LCN2 knockout (KO) mice had a decreased histopathological score and reduced neutrophil and macrophage infiltration in lung tissue compared with LPS-treated WT mice. RNA-sequencing analysis identified 38 differentially expressed genes (DEGs), including Cxcl5, Cxcl13, Xcl1, Saa1, and Cd14. In particular, Gene Ontology analysis of DEGs revealed a significant reduction in the inflammatory response, neutrophil chemotaxis, and chemokine-mediated signaling in LPS-treated LCN2KO mice compared with LPS-treated WT mice. Thus, these results suggest that LCN2 deletion alleviates LPS-induced ALI and that LCN2 may be involved in chemotaxis-related gene expression.

Lipocalin-2 activates hepatic stellate cells and promotes nonalcoholic steatohepatitis in high-fat diet-fed Ob/Ob mice.

BACKGROUND AND AIMS: In obesity and type 2 diabetes mellitus, leptin promotes insulin resistance and contributes to the progression of NASH via activation of hepatic stellate cells (HSCs). However, the pathogenic mechanisms that trigger HSC activation in leptin-deficient obesity are still unknown. This study aimed to determine how HSC-targeting lipocalin-2 (LCN2) mediates the transition from simple steatosis to NASH. APPROACH AND RESULTS: Male wild-type (WT) and ob/ob mice were fed a high-fat diet (HFD) for 20 weeks to establish an animal model of NASH with fibrosis. Ob/ob mice were subject to caloric restriction or recombinant leptin treatment. Double knockout (DKO) mice lacking both leptin and lcn2 were also fed an HFD for 20 weeks. In addition, HFD-fed ob/ob mice were treated with gadolinium trichloride to deplete Kupffer cells. The LX-2 human HSCs and primary HSCs from ob/ob mice were used to investigate the effects of LCN2 on HSC activation. Serum and hepatic LCN2 expression levels were prominently increased in HFD-fed ob/ob mice compared with normal diet-fed ob/ob mice or HFD-fed WT mice, and these changes were closely linked to liver fibrosis and increased hepatic α-SMA/matrix metalloproteinase 9 (MMP9)/signal transducer and activator of transcription 3 (STAT3) protein levels. HFD-fed DKO mice showed a marked reduction of α-SMA protein compared with HFD-fed ob/ob mice. In particular, the colocalization of LCN2 and α-SMA was increased in HSCs from HFD-fed ob/ob mice. In primary HSCs from ob/ob mice, exogenous LCN2 treatment induced HSC activation and MMP9 secretion. By contrast, LCN2 receptor 24p3R deficiency or a STAT3 inhibitor reduced the activation and migration of primary HSCs. CONCLUSIONS: LCN2 acts as a key mediator of HSC activation in leptin-deficient obesity via α-SMA/MMP9/STAT3 signaling, thereby exacerbating NASH.

Adzuki Bean MY59 Extract Reduces Insulin Resistance and Hepatic Steatosis in High-Fat-Fed Mice via the Downregulation of Lipocalin-2.

Adzuki bean is well known as a potential functional food that improves metabolic complications from obesity and diabetes. Lipocalin-2 (LCN2) has been implicated to have an important role in obesity and diabetes. However, the protective roles of adzuki bean MY59 extract (ABE) on insulin resistance and hepatic steatosis are not fully understood. In the present study, we investigated the effects of ABE on LCN2 expression in high-fat diet (HFD)-fed mice. ABE reduced HFD-induced fat mass and improved insulin resistance. In addition to hepatic steatosis, HFD-fed mice showed many apoptotic cells and neutrophils in the epididymal fat pads. However, these findings were significantly reduced by ABE supplementation. In particular, we found that increased LCN2 proteins from serum, epididymal fat pads, and liver in HFD-fed mice are significantly reduced by ABE. Furthermore, ABE reduced increased heme oxygenase-1 and superoxide dismutase-1 expressions in adipose tissue and liver in HFD-fed mice. We found that hepatic nuclear factor-kappa B (NF-κB) p65 expression in HFD-fed mice was also reduced by ABE. Thus, these findings indicate that ABE feeding could improve insulin resistance and hepatic steatosis by decreasing LCN2-mediated inflammation and oxidative stress in HFD-fed mice.

Feasibility of Ultrasound Attenuation Imaging for Assessing Pediatric Hepatic Steatosis.

We investigated the feasibility of ultrasound attenuation imaging (ATI) for assessing pediatric hepatic steatosis. A total of 111 children and adolescents who underwent liver ultrasonography with ATI for suspected hepatic steatosis were included. Participants were classified into the normal, mild, or moderate−severe fatty liver group according to grayscale US findings. Associations between clinical factors, magnetic resonance imaging proton density fat fraction, steatosis stage and ATI values were evaluated. To determine the cutoff values of ATI for staging hepatic steatosis, areas under the curve (AUCs) were analyzed. Factors that could cause measurement failure with ATI were assessed. Of 111 participants, 88 had successful measurement results. Median ATI values were significantly increased according to steatosis stage (p < 0.001). Body mass index (BMI) was a significant factor for increased ATI values (p = 0.047). To differentiate fatty liver from normal liver, a cutoff value of 0.59 dB/cm/MHz could be used with an AUC value of 0.853. To differentiate moderate to severe fatty liver from mild fatty liver, a cutoff value of 0.69 dB/cm/MHz could be used with an AUC value up to 0.91. ATI can be used in children as an effective ultrasonography technique for quantifying and staging pediatric hepatic steatosis.