A Review on in-vivo and in-vitro Models of Obesity and Obesity-Associated Co-Morbidities.

BACKGROUND: Obesity is becoming a global pandemic with pandemic proportions. According to the WHO estimates, there were over 1.9 billion overweight individuals and over 650 million obese adults in the globe in 2016. In recent years, scientists have encountered difficulties in choosing acceptable animal models, leading to a multitude of contradicting aspects and incorrect outcomes. This review comprehensively evaluates different screening models of obesity and obesity-associated comorbidities to reveal the advantages and disadvantages/limitations of each model while also mentioning the time duration each model requires to induce obesity. METHODOLOGY: For this review, the authors have gone through a vast number of article sources from different scientific databases, such as Google Scholar, Web of Science, Medline, and PubMed. RESULTS: In-vivo models used to represent a variety of obesity-inducing processes, such as diet-induced, drug-induced, surgical, chemical, stress-induced, and genetic models, are discussed. Animal cell models are examined with an emphasis on their use in understanding the molecular causes of obesity, for which we discussed in depth the important cell lines, including 3T3-L1, OP9, 3T3-F442A, and C3H10T1/2. Screening models of obesity-associated co-morbidities like diabetes, asthma, cardiovascular disorders, cancer, and polycystic ovarian syndrome (PCOS) were discussed, which provided light on the complex interactions between obesity and numerous health problems. CONCLUSION: Mimicking obesity in an animal model reflects multifactorial aspects is a matter of challenge. Future studies could address the ethical issues surrounding the use of animals in obesity research as well as investigate newly developed models, such as non-mammalian models. In conclusion, improving our knowledge and management of obesity and related health problems will require ongoing assessment and improvement of study models.

An artificial synaptic transistor using an α-In2Se3 van der Waals ferroelectric channel for pattern recognition.

Despite being widely investigated for their memristive behavior, ferroelectrics are barely studied as channel materials in field-effect transistor (FET) configurations. In this work, we use multilayer α-In2Se3 to realize a ferroelectric channel semiconductor FET, i.e., FeS-FET, whose gate-triggered and polarization-induced resistive switching is then exploited to mimic an artificial synapse. The FeS-FET exhibits key signatures of a synapse such as excitatory and inhibitory postsynaptic current, potentiation/depression, and paired pulsed facilitation. Multiple stable conductance states obtained by tuning the device are then used as synaptic weights to demonstrate pattern recognition by invoking a hidden layer perceptron model. Detailed artificial neural network (ANN) simulations are performed on binary scale MNIST data digits, invoking 784 input (28 × 28 pixels) and 10 output neurons which are used in the training of 42 000 MNIST data digits. By updating the synaptic weights with conductance weight values on 18 000 digits, we achieved a successful recognition rate of 93% on the testing data. Introduction of 0.10 variance of noise pixels results in an accuracy of more than 70% showing the strong fault-tolerant nature of the conductance states. These synaptic functionalities, learning rules, and device to system-level simulation results based on α-In2Se3 could facilitate the development of more complex neuromorphic hardware systems based on FeS-FETs.

Making consistent contacts to graphene: effect of architecture and growth induced defects.

The effects of contact architecture, graphene defect density and metal-semiconductor work function difference on the resistivity of metal-graphene contacts have been investigated. An architecture with metal on the bottom of graphene is found to yield resistivities that are lower, by a factor of four, and most consistent as compared to metal on top of graphene. Growth defects in graphene film were found to further reduce resistivity by a factor of two. Using a combination of method and metal used, the contact resistivity of graphene has been decreased by a factor of 10 to 1200 ± 250 [Formula: see text] using palladium as the contact metal. While the improved consistency is due to the metal being able to contact uncontaminated graphene in the metal on the bottom architecture, lower contact resistivities observed on defective graphene with the same metal are attributed to the increased number of modes of quantum transport in the channel.