Characterization of Inflammasomes and Their Regulation in the Red Fox.

BACKGROUND: Inflammasomes recognize endogenous and exogenous danger signals, and subsequently induce the secretion of IL-1ß. Studying inflammasomes in the red fox (Vulpes vulpes) is crucial for wildlife veterinary medicine, as it can help control inflammatory diseases in foxes. METHODS: We investigated the activation and intracellular mechanisms of three inflammasomes (NLRP3, AIM2, and NLRC4) in fox peripheral blood mononuclear cells (PBMCs), using established triggers and inhibitors derived from humans and mice. RESULTS: Fox PBMCs exhibited normal activation and induction of IL-1ß secretion in response to representative inflammasome triggers (ATP and nigericin for NLRP3, dsDNA for AIM2, flagellin for NLRC4). Additionally, PBMCs showed normal IL-1ß secretion when inoculated with inflammasome-activating bacteria. In inhibitors of the inflammasome signaling pathway, fox inflammasome activation was compared with mouse inflammasomes. MCC950, a selective NLRP3 inhibitor, suppressed the secretion of dsDNA- and flagellin-mediated IL-1ß in foxes, unlike mice. CONCLUSIONS: These findings suggest that NLRP3 may have a common role in dsDNA- and flagellin-mediated inflammasome activation in the red fox. It implies that this fox inflammasome biology can be applied to the treatment of inflammasome-mediated diseases in the red fox.

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI.

OBJECTIVE: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. METHOD & MATERIALS: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). RESULTS: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per [Formula: see text].

Isolation of Anti-Diabetic Active Compounds from Benincasae Exocarpium and Development of Simultaneous Analysis by HPLC-PDA.

Diabetes is a chronic metabolic disease that is a constant problem. Previous studies have reported that Benincasa cerifera Savi. extracts are effective in treating diabetes and its complications. Benincasae Exocarpium (BE) is a fruit peel of B. cerifera that has been reported to be used for the prevention and treatment of metabolic diseases such as hyperglycemia, obesity, and hyperlipidemia. However, there are not enough studies on the compounds and bioassays to support the efficacy of BE. The inhibitory activity of the BE extracts and fractions against advanced glycation end-products (AGE) formation and α-glucosidase activity was evaluated. These assays are relevant for the treatment of type 2 diabetes and its complications. Based on these results, compounds 1-11 were isolated through bioassay-guided isolation. In addition, we developed a high-performance liquid chromatography (HPLC) method that can simultaneously analyze these 11 compounds. Activity evaluation of the compounds was also conducted, and eight compounds exhibited significant activity. Among these, flavonoid compounds showed strong activity. A quantitative evaluation of eight bioactive compounds (2, 5-11) was conducted. In conclusion, this study demonstrated the potential of BE for prevention and treatment of type 2 diabetes and its complications.

Physical Unclonable Function and Hashing Are All You Need to Mutually Authenticate IoT Devices.

Internet of Things (IoT) has become the driving force in modern day technology with an increasing and rapid urge to create an intelligent, efficient, and connected world. IoT is used in manufacturing, agriculture, transportation, education, healthcare and many other business environments as well as home automation. Authentication for IoT devices is essential because many of these devices establish communication with servers through public networks. A rigorous lightweight device authentication scheme is needed to secure its physical hardware from cloning or side-channel attacks and accommodate the limited storage and computational power of IoT devices in an efficient manner. In this paper, we introduce a lightweight mutual two-factor authentication mechanism where an IoT device and the server authenticate each other. The proposed mechanism exploits Physical Unclonable Functions (PUFs) and a hashing algorithm with the purpose of achieving a secure authentication and session key agreement between the IoT device and the server. We conduct a type of formal analysis to validate the protocol's security. We also validate that the proposed authentication mechanism is secure against different types of attack scenarios and highly efficient in terms of memory storage, server capacity, and energy consumption with its low complexity cost and low communication overhead. In this sense, the proposed authentication mechanism is very appealing and suitable for resource-constrained and security-critical environments.

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer.

Noncancerous breast tissue and cancerous breast tissue have different elastic properties. In particular, cancerous breast tumors are stiff when compared to the noncancerous surrounding tissue. This difference in elasticity can be used as a means for detection through the method of elastographic tomosynthesis by means of physical modulation. This paper deals with a method to visualize elasticity of soft tissues, particularly breast tissues, via x-ray tomosynthesis. X-ray tomosynthesis is now used to visualize breast tissues with better resolution than the conventional single-shot mammography. The advantage of X-ray tomosynthesis over X-ray CT is that fewer projections are needed than CT to perform the reconstruction, thus radiation exposure and cost are both reduced. Two phantoms were used for the testing of this method, a physical phantom and an in silico phantom. The standard root mean square error in the tomosynthesis for the physical phantom was 2.093 and the error in the in silico phantom was negligible. The elastographs were created through the use of displacement and strain graphing. A Gaussian Mixture Model with an expectation-maximization clustering algorithm was applied in three dimensions with an error of 16.667%. The results of this paper have been substantial when using phantom data. There are no equivalent comparisons yet in 3D x-ray elastographic tomosynthesis. Tomosynthesis with and without physical modulation in the 3D elastograph can identify feature groupings used for biopsy. The studies have potential to be applied to human test data used as a guide for biopsy to improve accuracy of diagnosis results. Further research on this topic could prove to yield new techniques for human patient diagnosis purposes.

Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

A case of partial congenital pericardial defect presenting as acute coronary syndrome.

Congenital pericardial defects are rare and asymptomatic for both partial and complete defects. However, some patients can experience syncope, arrhythmia, and chest pain. When a patient experiences a symptom, it may be caused by herniation and dynamic compression or torsion of a heart structure including the coronary arteries. Diagnosis of a congenital pericardial defect may be difficult, especially in old patients with concomitant coronary artery disease. The clinical importance of congenital pericardial defect has not been stressed and congenital pericardial defects are regarded as benign, but in this case, pericardial defect was responsible for myocardial ischemia. The authors report a case of partial congenital pericardial defect causing herniation and dynamic compression of the coronary arteries, presenting as an acute coronary syndrome in an old man, with an emphasis on the unique features of the coronary angiogram that support the diagnosis of partial pericardial defects.

Irregular breathing classification from multiple patient datasets using neural networks.

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery [3-6, 25, 36]. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration [37]. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristic (ROC) curve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients breathing patterns validated the proposed irregular breathing classifier.

Protective effect of 4,4'-diaminodiphenylsulphone against oxidative stress but not to apoptotic stress in human diploid fibroblasts.

The antibiotic drug 4,4'-diaminodiphenylsulphone (DDS) is used to treat several dermatologic diseases, including Hansen's disease. This study confirmed the antioxidant nature of DDS in hydrogen peroxide (H(2)O(2))-induced oxidative stress and assessed its role in other apoptotic stresses in human diploid fibroblasts (HDFs). Oxidative stress was effectively reduced by DDS in a dose-dependent manner. Moreover, the oxidative stress-induced increases in the levels of the p53 and p21 proteins were inhibited by pre-treatment with DDS. In addition, H(2)O(2) and DDS increased the level of cytochrome P450 (CYP450) IIE1 in HDFs, implicating a role for DDS in H(2)O(2) scavenging via the activation of CYP450. DDS treatment increased the activity of catalase (CAT), glutathione peroxidase (GPx) and glutathione reductase (GR), as well as the GSH/GSSG ratio, indicating activation of the glutathione system against oxidative stress. However, DDS showed no protective effects on HDFs against other apoptotic stimuli, such as thapsigargin and staurosporine, suggesting that DDS would act only against oxidative stress. Therefore, in addition to its antibiotic function, DDS is a potent antioxidant against H(2)O(2)-induced oxidative stress in HDFs.

Suppression of ROS generation by 4,4-diaminodiphenylsulfone in non-phagocytic human diploid fibroblasts.

The action mode of 4,4-diaminodiphenylsulfone (DDS) is still under debate, although it has long been used in treatment of several dermatologic diseases including Hansens disease. In this study, we tested the effect of DDS as an antioxidant on paraquat-induced oxidative stress in non-phagocytic human diploid fibroblasts (HDFs). Overall, preincubation of HDFs with DDS prevented the oxidative stress and the resulting cytotoxic damages caused by paraquat in these cells. The specific effects of DDS in paraquat-treated HDFs are summarized as follows: a) reducing the expression of NADPH oxidase 4 (NOX4) by inhibiting paraquat-induced activation of PKC; b) inhibiting paraquat-induced decreases in mitochondrial complex protein levels as well as in membrane potentials; c) consequently, inhibiting the generation of cytosolic and mitochondrial superoxide anions. Taken together, these findings suggest that DDS would suppress the radical generation in non-phagocytic HDFs during oxidative stress, and that DDS might have the extended potential to be used further in prevention of other oxidative stress-related pathologies.