Generalized fear after acute stress is caused by change in neuronal cotransmitter identity.

Overgeneralization of fear to harmless situations is a core feature of anxiety disorders resulting from acute stress, yet the mechanisms by which fear becomes generalized are poorly understood. In this study, we show that generalized fear in mice results from a transmitter switch from glutamate to γ-aminobutyric acid (GABA) in serotonergic neurons of the lateral wings of the dorsal raphe. Similar change in transmitter identity was found in the postmortem brains of individuals with posttraumatic stress disorder (PTSD). Overriding the transmitter switch in mice prevented the acquisition of generalized fear. Corticosterone release and activation of glucocorticoid receptors mediated the switch, and prompt antidepressant treatment blocked the cotransmitter switch and generalized fear. Our results provide important insight into the mechanisms involved in fear generalization.

Generalized fear following acute stress is caused by change in co-transmitter identity of serotonergic neurons.

Overgeneralization of fear to harmless situations is a core feature of anxiety disorders resulting from acute stress, yet the mechanisms by which fear becomes generalized are poorly understood. Here we show that generalized fear in mice in response to footshock results from a transmitter switch from glutamate to GABA in serotonergic neurons of the lateral wings of the dorsal raphe. We observe a similar change in transmitter identity in the postmortem brains of PTSD patients. Overriding the transmitter switch in mice using viral tools prevents the acquisition of generalized fear. Corticosterone release and activation of glucocorticoid receptors trigger the switch, and prompt antidepressant treatment blocks the co-transmitter switch and generalized fear. Our results provide new understanding of the plasticity involved in fear generalization.

Using classification techniques for statistical analysis of Anemia.

Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a database containing data about nutritional factors for children. The data set was taken from NFHS-4, a survey conducted by the Government of India in 2015-16. The work attempts to predict anemia among children and establish a relation between mother's health and diet during pregnancy and its effects on anemic status of her child. It aims to help parents and clinicians to understand the influence of an infant's feeding practices and diet on his/her health and provide guidelines regarding diet to prevent anemia. Earlier, systems were built on computer using medical experts' advicewhich was then translated into algorithms for use. However, this method was time consuming thus, artificial intelligence came into play utilizing knowledge discovery and data mining tools for predictive modeling. The two techniques, decision tree and association rule mining has been applied and compared to select more appropriate technique for this particular task and a model is proposed in the healthcare domain with the aim to reduce the risk of the blood-related disease anemia.