Antihypertensive Effect of Heimia Salicifolia
In order to evaluate the effects produced by the alkaloids of leaves from H. salicifolia on the blood pressure of rats, four alkaloids from H. salicifolia were isolated by column and thin layer chromatography. The alkaloids’ antihypertensive effects on angiotensin II-caused acute hypertension as well as their hypotensive effects on normotensive rats were studied. The alkaloid 3, the most abundant isolated alkaloid, turned out to be the most effective, while the other alkaloids showed a slight, non-significant vascular activity. Their mixture showed an additive effect, though slightly different from the alkaloid 3.
Angiotensin-converting enzyme inhibition, angiotensin, and cognition.
In these studies, we have investigated possible cognition-enhancing effects of angiotensin-converting enzyme (ACE) inhibition, and putative neurochemical correlates for these actions. In a mouse habituation model, ACE inhibitors improved basal performance and antagonized scopolamine-induced deficits. The performance of aged mice and those with lesions of the nucleus basalis was also improved. ACE inhibition also improved scopolamine-impaired performance of rats in a swim-maze model. Neurochemical studies showed that a low dose (10 micrograms/kg i.p.) of ceranapril caused significant alterations in ex vivo rat brain catecholamine levels in the nucleus accumbens, amygdala, and septum. In further studies, angiotensin II (Ang II) was shown to decrease potassium-stimulated [3H] acetylcholine release from slices of rat entorhinal and human temporal cortex, an effect that could be antagonized by the angiotensin receptor antagonist [1-sar,8-thr]Ang II. It is concluded that ACE inhibition can improve both basal and impaired performance in animal models of learning, and that this improvement may be in part a consequence of the removal by ACE inhibition of an inhibitory tone on central acetylcholine release, and/or an effect on central catecholaminergic function.
A natural language discourse model to explain linear programming models and solutions
Abstract This paper presents a natural language (i.e., English) discourse model to explain a linear programming model and, possibly, its computed solution (which need not be optimal). Drawing from earlier syntactic translations, a semantic model is presented, based on economic input-output relations commonly found in large-scale linear programs. These are then shown how to form rule-driven explanations, which comprises a fundamental component of an intelligent mathematical programming system.