Assessment of Parotid Gland Tumors by Means of Quantitative Multiparametric Ultrasound (mpUS)

Myeloid-Specific JAK2 Contributes to Inflammation and Salt Sensitivity of Blood Pressure

BACKGROUND

Salt sensitivity of blood pressure (SSBP), characterized by acute changes in blood pressure with changes in dietary sodium intake, is an independent risk factor for cardiovascular disease and mortality in people with and without hypertension. We previously found that elevated sodium concentration activates antigen-presenting cells (APCs), resulting in high blood pressure, but the mechanisms are unknown. Here, we hypothesized that APC-specific JAK2 (Janus kinase 2) through STAT3 (signal transducer and activator of transcription 3) and SMAD3 (small mothers against decapentaplegic homolog 3) contributes to SSBP.

METHODS

We performed bulk or single-cell transcriptomic analyses following in vitro monocytes exposed to high salt and in vivo high sodium treatment in humans using a rigorous salt-loading/depletion protocol to phenotype SSBP. We also used a myeloid cell-specific CD11c+ JAK2 knockout mouse model and measured blood pressure with radiotelemetry after N-omega-nitro-L-arginine-methyl ester and a high salt diet treatment. We used flow cytometry for immunophenotyping and measuring cytokine levels. Fluorescence in situ hybridization and immunohistochemistry were performed to spatially visualize the kidney's immune cells and cytokine levels. Echocardiography was performed to assess cardiac function.

RESULTS

We found that high salt treatment upregulates gene expression of the JAK/STAT/SMAD pathway while downregulating inhibitors of this pathway, such as suppression of cytokine signaling and cytokine-inducible SH2, in human monocytes. Expression of the JAK2 pathway genes mirrored changes in blood pressure after salt loading and depletion in salt-sensitive but not salt-resistant humans. Ablation of JAK2, specifically in CD11c+ APCs, attenuated salt-induced hypertension in mice with SSBP. Mechanistically, we found that SMAD3 acted downstream of JAK2 and STAT3, leading to increased production of highly reactive isolevuglandins and proinflammatory cytokine IL (interleukin)-6 in renal APCs, which activate T cells and increase production of IL-17A, IL-6, and TNF-α (tumor necrosis factor-alpha).

CONCLUSIONS

Our findings reveal the APC JAK2 signaling pathway as a potential target for the diagnosis and treatment of SSBP in humans.

The Systemic Immune-Inflammation Index as an Independent Predictor of Survival in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma Undergoing Neoadjuvant Radiotherapy

Purpose

Patients with locally advanced esophageal squamous cell carcinoma (ESCC) scheduled for neoadjuvant radiotherapy still have a poor prognosis. This study was to explore the prognostic value of the pretreatment systemic immune-inflammation index (SII) in patients with locally advanced ESCC after neoadjuvant radiotherapy (NRT).

Materials and Methods

Eighty-two consecutive patients with ESCC scheduled for neoadjuvant radiotherapy between 2011 and 2017 were enrolled in this study. SII values (SII = platelet × neutrophil/lymphocyte), prognostic nutritional index values (PNI = albumin concentration (g/L) + 5 × total lymphocyte count (109/L)), platelet-lymphocyte ratio (PLR), and neutrophil-lymphocyte ratio (NLR) were retrospectively collected and calculated before treatment. The Cut-off Finder application was applied to find out the cut-off points of the SII, NLR, PNI and PLR. A regression model was used to examine prognostic factors for overall survival (OS) rates.

Results

The median follow-up was 44 months (3 to 83). Sixty patients (73.17%) underwent surgery as scheduled. This study found that factors improving OS were a lower SII (≤916.6 × 109/L) (P=0.040) and neoadjuvant chemoradiotherapy (NCRT) (P=0.034). The patients with a lower SII and NCRT had a better OS (P< 0.001). Moreover, additionally, a higher SII was associated with a lower resectability rate (P=0.035).

Conclusion

The SII can predict resectability in ESCC patients following neoadjuvant radiotherapy. Both the SII and neoadjuvant chemoradiotherapy appear to influence OS.

Investigating the Causal Relationship Between Sleep Behaviors and Primary Open-Angle Glaucoma: A Bidirectional Two-Sample Mendelian Randomization Study

Background

Although previous studies of sleep-related behaviors in relation to primary open-angle glaucoma (POAG) have been noted, the causal relationship remains unclear. The purpose of our present study was to investigate the relationships of genetically predicted sleep traits with POAG using a two-sample bidirectional Mendelian randomization (MR) method.

Methods

Summary-level data collected from publicly available genome-wide association studies (GWAS) of European decent were applied for the bidirectional MR analysis. After quality control steps, independent single-nucleotide polymorphisms for eight sleep behaviors and POAG were selected as the genetic instruments. The inverse-variance weighted (IVW) approach was adopted as the primary method, which was complemented by a series of sensitivity analyses to assess the robustness of the results by estimating heterogeneity and pleiotropy. Multivariable MR (MVMR) was used to assess the direct effect of sleep traits on POAG, after adjusting for several confounding factors.

Results

Our investigation revealed a positive correlation between genetically predicted ease of getting up in the morning and sleep duration and POAG using the IVW method (odds ratio (OR)=1.78, 95% confidence interval (CI):1.29-2.46, P = 4.33× 10-4; OR = 1.66, 95% CI:1.18-2.34, P = 3.38×10-3, respectively). Other supplementary MR methods also confirmed similar results. Moreover, the MVMR results also revealed that the adverse effects of these two sleep traits on POAG persisted after adjusting for body mass index, smoking, drinking, and education (all P < 0.05). Conversely, the relationships between genetic liability of POAG and different sleep behaviors were not statistically significant in the reverse-direction MR estimate (all P > 0.05).

Conclusion

Our study demonstrated that genetic prediction of getting up easily in the morning or sleep duration were associated with a higher risk of POAG, but not vice versa, in a European population. Further validation and clinical interventions are required to offer potential strategies to prevent and manage POAG.

Feasibility of direct brain 18F-fluorodeoxyglucose-positron emission tomography attenuation and high-resolution correction methods using deep learning

Objectives

To develop the following three attenuation correction (AC) methods for brain 18F-fluorodeoxyglucose-positron emission tomography (PET), using deep learning, and to ascertain their precision levels: (i) indirect method; (ii) direct method; and (iii) direct and high-resolution correction (direct+HRC) method.

Methods

We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM).

Results

Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975.

Conclusion

The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.

Magnetic resonance imaging findings of sclerosing microcystic adenocarcinoma: A case report and review of the literature

Sclerosing microcystic adenocarcinoma (SMA) is a rare malignant tumor of the salivary glands that closely resembles cutaneous microcystic adnexal carcinoma (MAC). It was newly listed in the 5th edition of the WHO classification of head and neck tumors. This report describes the case of a 61-year-old woman who presented with masses on the floor of the mouth. The masses showed low signal intensity on T2-weighted images (T2WI) and with low apparent diffusion coefficient (ADC) values. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) revealed a plateau or persistence after rapid initial enhancement. Histopathologically, the tumors comprised small infiltrating strands of cells that formed small ducts and cysts embedded in thick fibrous stroma, consistent with SMA. Low signal intensity on T2WI with a low ADC value and a plateau or persistence after rapid initial enhancement on DCE-MRI reflect the fibrous and cellular components of the tumor and can be considered characteristic MRI findings of SMA.

A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information

Purpose

This study aimed to develop a deep learning model based on chest radiography (CXR) images and clinical data to accurately classify gram-positive and gram-negative bacterial pneumonia in children to guide the use of antibiotics.

Methods

We retrospectively collected CXR images along with clinical information for gram-positive (n=447) and gram-negative (n=395) bacterial pneumonia in children from January 1, 2016, to June 30, 2021. Four types of machine learning models based on clinical data and six types of deep learning algorithm models based on image data were constructed, and multi-modal decision fusion was performed.

Results

In the machine learning models, CatBoost, which only used clinical data, had the best performance; its area under the receiver operating characteristic curve (AUC) was significantly higher than that of the other models (P<0.05). The incorporation of clinical information improved the performance of deep learning models that relied solely on image-based classification. Consequently, AUC and F1 increased by 5.6% and 10.2% on average, respectively. The best quality was achieved with ResNet101 (model accuracy: 0.75, recall rate: 0.84, AUC: 0.803, F1: 0.782).

Conclusion

Our study established a pediatric bacterial pneumonia model that utilizes CXR and clinical data to accurately classify cases of gram-negative and gram-positive bacterial pneumonia. The results confirmed that the addition of image data to the convolutional neural network model significantly improved its performance. While the CatBoost-based classifier had greater advantages owing to a smaller dataset, the quality of the Resnet101 model trained using multi-modal data was comparable to that of the CatBoost model, even with a limited number of samples.