Annexin 1 Reduces Dermatitis-Induced Itch and Cholestatic Itch through Inhibiting Neuroinflammation and Iron Overload in the Spinal Dorsal Horn of Mice.

The unclear pathogenesis of chronic itch originating from several systemic disorders poses challenges to clinical intervention. Recent studies recapitulate the spinal neurocircuits associated with neuroinflammation and synaptic plasticity responsible for pruriceptive sensations. The resolution of nociception and inflammation by Annexin 1 (ANXA1) has been identified. Given that pain and itch share many neural mechanisms, we employed two mice models of chronic itch to study the underlying targets and therapeutic potential of ANXA1, comprising allergic contact dermatitis-induced itch and cholestatic itch. Herein, we report that spinal expression of ANXA1 is down-regulated in mice with dermatitis-induced itch and cholestatic itch. Repetitive injections of ANXA1-derived peptide Ac2-26 (intrathecal, 10 µg) reduce itch-like scratching behaviors following dermatitis and cholestasis. Single exposure to Ac2-26 (intrathecal, 10 µg) alleviates the established itch phenotypes. Moreover, systemic delivery of Ac2-26 (intravenous, 100 µg) is effective against chronic dermatitis-induced itch and cholestatic itch. Strikingly, Ac2-26 therapy inhibits transferrin receptor 1 over-expression, iron accumulation, cytokine IL-17 release and the production of its receptor IL-17R, as well as astrocyte activation in the dorsal horn of spinal cord in mouse with dermatitis and cholestasis. Pharmacological intervention with iron chelator deferoxamine impairs chronic itch behaviors and spinal iron accumulation after dermatitis and cholestasis. Also, spinal IL-17/IL-17R neutralization attenuates chronic itch. Taken together, this current research indicates that ANXA1 protects against the beginning and maintenance of long-term dermatitis-induced itch and cholestatic itch, which may occur via the spinal suppression of IL-17-mediated neuroinflammation, astrocyte activation and iron overload.

Multi-model fusion of classifiers for blood pressure estimation.

Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi-model fusion of the classifiers. Here, the support vector machine, the random forest and the K-nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi-model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.

Empirical Mode Decomposition Based Multi-Modal Activity Recognition.

This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition.

Visible Light-Activated Self-Recovery Hydrophobic CeO2/Black TiO2 Coating Prepared Using Air Plasma Spraying.

Hydrophobic coatings are widely used in many areas from home to industry. However, the hydrophobicity can be easily degraded by adsorption of the atmospheric contaminations. In this study, a novel CeO2/black TiO2 hydrophobic coating with self-recovery capability is prepared using air plasma spraying without any chemical modification. The water contact angles (WCAs) of the coatings decreased after oleic acid contamination. Because of the presence of black TiO2, the composite coating has the photodegradation property under irradiation of visible light, and the part of the black TiO2 transforms to be superhydrophilic after irradiation for the generation of the surface oxygen vacancies. The oleic acid was decomposed and the WCAs changed depending on the volume percentage of the CeO2. The coating exhibits hydrophilicity when the volume percentage of the CeO2 is less than 60%, and hydrophobic when higher. After storage in a dark and clean environment, all the coatings can recover their hydrophobicity for the black TiO2 that returned back to its origin state. It is believed that this hydrophobic self-cleaning ceramic coating should have potential in engineering applications.