Mitofusin-2 Negatively Regulates Melanogenesis by Modulating Mitochondrial ROS Generation.

Inter-organellar communication is emerging as one of the most crucial regulators of cellular physiology. One of the key regulators of inter-organellar communication is Mitofusin-2 (MFN2). MFN2 is also involved in mediating mitochondrial fusion-fission dynamics. Further, it facilitates mitochondrial crosstalk with the endoplasmic reticulum, lysosomes and melanosomes, which are lysosome-related organelles specialized in melanin synthesis within melanocytes. However, the role of MFN2 in regulating melanocyte-specific cellular function, i.e., melanogenesis, remains poorly understood. Here, using a B16 mouse melanoma cell line and primary human melanocytes, we report that MFN2 negatively regulates melanogenesis. Both the transient and stable knockdown of MFN2 leads to enhanced melanogenesis, which is associated with an increase in the number of mature (stage III and IV) melanosomes and the augmented expression of key melanogenic enzymes. Further, the ectopic expression of MFN2 in MFN2-silenced cells leads to the complete rescue of the phenotype at the cellular and molecular levels. Mechanistically, MFN2-silencing elevates mitochondrial reactive-oxygen-species (ROS) levels which in turn increases melanogenesis. ROS quenching with the antioxidant N-acetyl cysteine (NAC) reverses the MFN2-knockdown-mediated increase in melanogenesis. Moreover, MFN2 expression is significantly lower in the darkly pigmented primary human melanocytes in comparison to lightly pigmented melanocytes, highlighting a potential contribution of lower MFN2 levels to higher physiological pigmentation. Taken together, our work establishes MFN2 as a novel negative regulator of melanogenesis.

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

Superior even skin tone and anti-ageing benefit of a combination of 4-hexylresorcinol and niacinamide.

OBJECTIVES: To demonstrate the synergistic effect of 4-hexylresorcinol (4-HR) with niacinamide in boosting anti-melanogenic efficacy in vitro and establish the in vivo efficacy and safety of the combination in a human trial. METHODS: Primary human epidermal melanocytes and 3D pigmented skin equivalents were treated with 4-HR, niacinamide, and their combinations for their effect on pigmentation. This was followed by a randomized, double-blind, split-face clinical study in Chinese subjects, and effects on skin tone, hyperpigmentation, fine lines and wrinkles, hydration, and skin firmness were measured for a 12-week study period. RESULTS: In vitro tyrosinase enzyme activity studies showed that 4-HR is one of the most potent tyrosinase inhibitors. The combination of 4-HR and niacinamide showed a synergistic reduction in melanin production in cultured melanocytes and lightened the 3D skin equivalent model. In vitro as well as in the human trial, the combination of 4-HR and niacinamide showed significantly improved efficacy over niacinamide alone on hyperpigmentation spots as measured by L*, the visual appearance of fine lines and wrinkles in crow's feet and perioral area and skin firmness, with no product-related adverse events. CONCLUSIONS: A formulation containing a combination of 4-HR and niacinamide delivered superior skin tone and anti-ageing benefits significantly better than niacinamide alone with no adverse events. This study demonstrates that a product designed to affect multiple pathways of melanogenesis, inflammation, and ageing may provide an additional treatment option, beyond hydroquinone and retinoids, for hyperpigmentation and ageing.

OBJECTIFS: Démontrer l'effet synergique du 4-hexylrésorcinol (4-HR) associé au niacinamide pour stimuler in vitro l'efficacité antimélanogène, et établir l'efficacité et la sécurité d'emploi in vivo de cette association dans un essai chez l'homme. MÉTHODES: Des mélanocytes épidermiques humains primaires et des équivalents cutanés pigmentés en 3D ont été traités avec du 4-HR, du niacinamide et leurs combinaisons pour leur effet sur la pigmentation. Ceci a été suivi d'une étude clinique randomisée, en double aveugle en hémi-visage chez des sujets chinois, et les effets sur le teint, l'hyperpigmentation, les rides et ridules, l'hydratation et la fermeté de la peau ont été mesurés pendant une durée d'étude de 12 semaines. RÉSULTATS: Les études in vitro sur l'activité enzymatique de la tyrosinase ont montré que le 4-HR est l'un des inhibiteurs de la tyrosinase les plus puissants. L'association du 4-HR et du niacinamide a montré une réduction synergique de la production de mélanine dans les mélanocytes de culture et donné de la luminosité au modèle cutané 3D équivalent. Également in vitro avec l'étude chez l'homme, l'association du 4-HR et du niacinamide a fait ressortir une efficacité significativement plus élevée qu'avec le niacinamide seul sur les taches d'hyperpigmentation mesurées par L*, l'aspect visuel des rides et ridules des pattes d'oie et de la zone périorale, et la fermeté de la peau, sans événements indésirables liés au produit. CONCLUSIONS: Une formulation contenant une association de 4-HR et de niacinamide a permis d'obtenir un teint et un effet anti-âge nettement supérieurs à ceux du niacinamide seul, sans événements indésirables. Cette étude démontre qu'un produit conçu pour toucher plusieurs voies de mélanogenèse, d'inflammation et de vieillissement peut constituer une nouvelle option thérapeutique, au-delà de l'hydroquinone et des rétinoïdes, pour l'hyperpigmentation et le vieillissement.

Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review.

The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO2 and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review.

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015-2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

Epilepsy classification using optimized artificial neural network.

OBJECTIVES: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process. MATERIALS: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point. RESULTS: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification. DISCUSSION: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction. ABBREVIATIONS: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.

An extensive review on development of EEG-based computer-aided diagnosis systems for epilepsy detection.

Epilepsy is considered as fourth most prominent neurological disorder in the world that can affect people of all age groups. Currently, around 65 million people throughout the world are suffering from epilepsy. It is evident that electroencephalograph (EEG) signals are most commonly used for detection of epileptic seizures but today many modern techniques have been developed to analyze underlying features of these EEG signals. As EEG contains a large amount of complicated information, so many researchers are trying to develop automatic systems for complete feature extraction. This paper provides a generalized review and performance comparison of popular seizure detection algorithms that are developed in the last decade. The main objective of this paper is to briefly discuss all existing developments in the field of computer-aided diagnosis system for epilepsy detection so that future researchers can find a better track for the new invention.