Myocardial perfusion imaging using technetium-99m sestamibi in asymptomatic diabetic patients.

UNLABELLED: Myocardial perfusion single-photon emission computed tomography (SPECT) has been extensively applied in the clinical assessment of patients with diabetes mellitus. The aim of the present study was to evaluate stress technetium-99m sestamibi SPECT MPI perfusion in silent myocardial ischemia and its association with some clinical and laboratory parameters in an asymptomatic diabetic population. PATIENTS, MATERIAL, METHODS: 83 subjects (age: 57.1±6.9 years) with at least five years history of type 2 diabetes, and no suspected or documented coronary artery disease (CAD) accomplished myocardial perfusion imaging; angiography was also performed in patients with abnormal MPI. RESULTS: MPI results showed that 58 patients had normal myocardial perfusion, while 25 patients showed perfusion defects (23 reversible and 2 fixed) on MPI. 12 out of the 25 (48%) with abnormal MPI findings represented abnormal angiography. We observed that pretest likelihood of CAD (odds ratio 2.32; 95%-CI: 1.05-5.13; p = 0.038) and higher HbA1c level (odds ratio 1.70; 95%-CI, 1.07-2.71; p = 0.02) were independently associated with abnormal MPI. CONCLUSION: Occult CAD was present on MPI in 1/3 patients with DM without abnormal electrocardiographic findings or evidence of peripheral arterial disease.

Expertness based cooperative Q-learning.

By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules from the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. Some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter-Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined.