The pharmacology of centrally acting drugs in animals of altered thyroid status

The pharmacological effects of a number of centrally acting drugs have been compared in euthyroid mice and mice made hyperthyroid by pretreatment with sodium-1-thyroxine. The potencies of two barbiturates, pentobarbitone and thiopentone - as indicated by the duration of their hypnotic actions and their acute toxicities - are increased in hyperthyroid mice. An acutely active uncoupler of phosphorylative oxidation is 2, 4-dinitrophenol, an agent which proved to be a potent hypnotic when administered intracerebrally. An attempt has been made to relate the mechanism of action of the barbiturates to the uncoupling effects of thyroxine and 2, 4-dinitrophenol. The pharmacological effects of chlorpromazine, reserpine and amphetamine-like drugs have also been studied in hyperthyroid mice. After pretreatment with thyroxine, mice show a reduced tendency to become hypothermic after chlorpromazine or reserpine; in fact, under suitable laboratory conditions these agents produce a hyperthermic effect. Yet their known depressant effects upon locomotor activity were not substantially altered. Thus it appeared that depression of locomotor activity and hypothermia are not necessarily correlated, an observation at variance with previously held opinion. These results have been discussed in the light of our knowledge of the role of the thyroid gland in thermoregulation. The actions of tremorine and its metabolite, oxotremorine, have also been examined. Hyperthyroid animals are less susceptible to both the hypothermia and tremor produced by these agents. An attempt is made to explain these observations, in view of the known mechanism of action of oxotremorine and the tremorgenic actions that thyroxine may have. A number of experimental methods have been used to study the anti-nociceptive (analgesic) effects of drugs in euthyroid and hyperthyroid mice. The sites and mechanisms of action of these drugs and the known actions of thyroxine have been discussed.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.