Household laundry detergents disrupt barrier integrity and induce inflammation in mouse and human skin.

BACKGROUND: Epithelial barrier impairment is associated with many skin and mucosal inflammatory disorders. Laundry detergents have been demonstrated to affect epithelial barrier function in vitro using air-liquid interface cultures of human epithelial cells. METHODS: Back skin of C57BL/6 mice was treated with two household laundry detergents at several dilutions. Barrier function was assessed by electric impedance spectroscopy (EIS) and transepidermal water loss (TEWL) measurements after the 4 h of treatments with detergents. RNA sequencing (RNA-seq) and targeted multiplex proteomics analyses in skin biopsy samples were performed. The 6-h treatment effect of laundry detergent and sodium dodecyl sulfate (SDS) was investigated on ex vivo human skin. RESULTS: Detergent-treated skin showed a significant EIS reduction and TEWL increase compared to untreated skin, with a relatively higher sensitivity and dose-response in EIS. The RNA-seq showed the reduction of the expression of several genes essential for skin barrier integrity, such as tight junctions and adherens junction proteins. In contrast, keratinization, lipid metabolic processes, and epidermal cell differentiation were upregulated. Proteomics analysis showed that the detergents treatment generally downregulated cell adhesion-related proteins, such as epithelial cell adhesion molecule and contactin-1, and upregulated proinflammatory proteins, such as interleukin 6 and interleukin 1 beta. Both detergent and SDS led to a significant decrease in EIS values in the ex vivo human skin model. CONCLUSION: The present study demonstrated that laundry detergents and its main component, SDS impaired the epidermal barrier in vivo and ex vivo human skin. Daily detergent exposure may cause skin barrier disruption and may contribute to the development of atopic diseases.

Low-noise phase-sensitive optical parametric amplifier with lossless local pump generation using a digital dither optical phase-locked loop.

The low noise figure of phase-sensitive amplifiers (PSAs) is attractive for optically pre-amplified measurement and communication systems. However, a major practical implementation difficulty pertains to the requirement of phase-locked signal, idler, and pump waves. Previously, injection locking to a co-propagating weak pump pilot or tapping portions of the received signal (lossy) for carrier re-generation have been used. Here we present a novel, lossless approach without any pump pilot, that generates a phase-locked receiver-local pump within the PSA using a digital dither-based optical phase-locked loop. We experimentally demonstrate a 2 dB noise figure with a low 0.3 dB penalty due to imperfect locking. By comparing the phase-locking performance in a PSA to that in a 50/50-coupler, we discuss and predict potential performance improvements connected to loop delay and laser phase characteristics.

Coherent combining of low-power optical signals based on optically amplified error feedback.

In free-space optical communication links, the combining of optical signals from multiple apertures is a well-known method to collect more power for improved sensitivity or mitigation of atmospheric disturbances. However, for analog optical combining no detailed analysis has been made in cases when the optical signal power is very low (<-60 dBm) as would be the case in very long-haul free-space links. We present a theoretical and experimental study of analog coherent combining of noise-limited signals from multiple independent apertures by applying low frequency optical phase dithering to actively compensate the relative phases. It is experimentally demonstrated that a 97% combining efficiency of four 10 GBaud QPSK signals is possible with a signal power per aperture exceeding -80 dBm, in fair agreement with theory. We also discuss the scaling aspects to many apertures.

Interleukin-35 Prevents the Elevation of the M1/M2 Ratio of Macrophages in Experimental Type 1 Diabetes.

Macrophages play an important role in the early development of type 1 diabetes (T1D). Based on the phenotype, macrophages can be classified into pro-inflammatory (M1) and anti-inflammatory (M2) macrophages. Despite intensive research in the field of macrophages and T1D, the kinetic response of M1/M2 ratio has not been studied in T1D. Thus, herein, we studied the M1 and M2 macrophages in the early development of T1D using the multiple low dose streptozotocin (MLDSTZ) mouse model. We determined the proportions of M1 and M2 macrophages in thymic glands, pancreatic lymph nodes and spleens on days 3, 7 and 10 after the first injection of STZ. In addition, we investigated the effect of IL-35 in vivo on the M1/M2 ratio and IL-35+ plasmacytoid dendritic cells in diabetic mice and in vitro on the sorted macrophages. Our results revealed that the M1/M2 ratio is higher in STZ-treated mice but this was lowered upon the treatment with IL-35. Furthermore, IL-35 treated mice had lower blood glucose levels and a higher proportion of IL-35+ cells among pDCs. Macrophages treated with IL-35 in vitro also had a higher proportion of M2 macrophages. Together, our data indicate that, under diabetic conditions, pro-inflammatory macrophages increased, but IL-35 treatment decreased the pro-inflammatory macrophages and increased anti-inflammatory macrophages, further suggesting that IL-35 prevents hyperglycemia by maintaining the anti-inflammatory phenotype of macrophages and other immune cells. Thus, IL-35 should be further investigated for the treatment of T1D and other autoimmune disorders.

Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.

OBJECTIVES: The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models. METHODS: Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point. CONCLUSION: Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool. KEY POINTS: • Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status. • In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions. • The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.

High-density lipoproteins induce miR-223-3p biogenesis and export from myeloid cells: Role of scavenger receptor BI-mediated lipid transfer.

BACKGROUND AND AIMS: We recently showed that miR-223-3p on high-density lipoproteins (HDL) is exported to endothelial cells, where it inhibits inflammation. However, the origin of miR-223-3p on HDL is unknown. We hypothesize that HDL-associated miR-223-3p originates in myeloid cells and is exported to HDL in a scavenger receptor BI (SR-BI)-dependent manner. METHODS: Polymorphonuclear neutrophils (PMNs) and human monocyte derived macrophages (HMDMs) were incubated with native HDL (nHDL) or discoidal reconstituted HDL (rHDL). Total RNA was isolated before and after incubation. Mature and primary miR-223-3p (pri-mir-223-3p) levels were quantified by real-time PCR. RESULTS: Incubation with nHDL and rHDL increased miR-223-3p export from PMNs and HMDMs. In PMNs, nHDL but not rHDL, increased mature and pri-mir-223-3p. Incubation with HDL also increased Dicer mRNA, a critical regulator of miRNA biogenesis. Incubation of HMDMs with nHDL did not increase cellular levels of mature miR-223-3p, but significantly increased pri-mir-223 levels. Incubation with rHDL had no effect on either mature or pri-mir-223-3p levels. Activated PMNs increased miR-223-3p export to HDL and the production of reactive oxygen species and activated protein kinase C. Blocking HDL binding to SR-BI increased miR-223-3p export to HDL in both PMNs and HMDMs, but did not affect mature and primary miR-223-3p levels. Chemical inhibition of cholesterol flux by Block Lipid Transport (BLT)-1 inhibited HDL-induced pri-mir-223 expression in PMNs. CONCLUSIONS: HDL-associated miR-223-3p originates in PMNs and macrophages. HDL stimulates miR-223-3p biogenesis in PMNs in a process that is regulated by SR-BI-mediated lipid flux.

Automatic Delineation of the Clinical Target Volume in Rectal Cancer for Radiation Therapy using Three-dimensional Fully Convolutional Neural Networks.

Accurate, robust, and fast delineation of the clinical target volume (CTV) for the use in radiotherapy of rectal cancer (RC) is highly sought-after. Convolutional neural networks (CNNs) have proven themselves very effective in various segmentation tasks on medical images. Despite this, their application in CTV delineation is not yet fully explored. This study uses the three-dimensional fully convolutional neural network architecture called V-net for CTV delineation. The West China Hospital (Chengdu, China) provided this study with 120 annotated CT scans. For improved performance and to battle data scarcity, the available scans were augmented. Trained on 100 CT-scans for 20 hours and tested on 20 previously unseen CT-scans the network achieved a mean dice similarity coefficient (DSC) of 0.90 and a mean delineation time per CTV of 0.60 seconds. The proposed method is compared with two other state-of-the-art CNNs and is shown to be superior.

A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography.

Proper training of convolutional neural networks (CNNs) requires annotated training datasets oflarge size, which are not currently available in CT colonography (CTC). In this paper, we propose a well-designed framework to address the challenging problem of data shortage in the training of 3D CNN for the detection of polyp candidates, which is the first and crucial part of the computer-aided diagnosis (CAD) of CTC. Our scheme relies on the following two aspects to reduce overfitting: 1) mass data augmentation, and 2) a flat 3D residual fully convolutional network (FCN). In the first aspect, we utilize extensive rotation, translation, and scaling with continuous value to provide numerous data samples. In the second aspect, we adapt the well-known V-Net to a flat residual FCN to resolve the problem of detection other than segmentation. Our proposed framework does not rely on accurate colon segmentation nor any electrical cleansing of tagged fluid, and experimental results show that it can still achieve high sensitivity with much fewer false positives. Code has been made available at: http://github.com/chenyzstju/ctc_screening_cnn.

Breast Region Segmentation being Convolutional Neural Network in Dynamic Contrast Enhanced MRI.

Breast density and background parenchymal enhancement (BPE) are suggested to be related to the risk of breast cancer. The first step to quantitative analysis of breast density and BPE is segmenting the breast from body. Nowadays, convolutional neural networks (CNNs) are widely used in image segmentation and work well in semantic segmentation, however, CNNs have been rarely used in breast region segmentation. In this paper, the CNN was employed to segment the breast region in transverse fat-suppressed breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Image normalization was initially performed. Subsequently, the dataset was divided into three sets randomly: train set validation set and test set. The 2-D U-Net was trained by train set and the optimum model was chosen by validation set. Finally, segmentation results of test set obtained by U-Net were adjusted in the postprocessing. In this step, two largest volumes were computed to determine whether the smaller volume is the scar after mastectomy. With the limitation of small dataset, 5-fold cross-validation and data augmentation were used in this study. Final results on the test set were evaluated by volume-based and boundary-based metrics with manual segmentation results. By using this method, the mean dice similarity coefficient (DSC), dice difference coefficient (DDC), and root-mean-square distance reached 97.44%, 5.11%, and 1.25 pixels, respectively.