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Michael Tang

Publications and source records attributed to Michael Tang.

2 recordsLinked to original sources

Naloxone does not increase the minimum alveolar anesthetic concentration of sevoflurane in mice.

Several previous studies concluded that opioid receptors do not mediate the capacity of inhaled anesthetics to produce immobility in the face of noxious stimulation because administration of naloxone (a nonspecific opioid receptor antagonist) does not increase the minimum alveolar anesthetic concentration (MAC) of inhaled anesthetic that produces immobility in 50% of subjects given a noxious stimulation. In contrast, a recent study found that 0.1 mg/kg naloxone given intraperitoneally increased sevoflurane MAC in mice by 18% (P < 0.01). We repeated the recent study with sevoflurane in the same strain of mice, administering nothing (control), 0.1 mg/kg, and 1.0 mg/kg of naloxone. Our study differed in that we also tested a parallel group given saline rather than naloxone. We were blinded to drug administration. MAC decreased 4.8% +/- 11.0% (mean+/- sd) and 2.4% +/- 12.5% with the first and second administrations of saline. Similarly, MAC decreased 4.7% +/- 7.1% and 5.5% +/- 10.0% with the administration of 0.1 mg/kg and 1.0 mg/kg of naloxone. We do not find that naloxone increases MAC. Opioid receptors do not underlie a portion of the capacity of inhaled anesthetics to produce immobility.

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RAG: RNA-As-Graphs database--concepts, analysis, and features.

MOTIVATION: Understanding RNA's structural diversity is vital for identifying novel RNA structures and pursuing RNA genomics initiatives. By classifying RNA secondary motifs based on correlations between conserved RNA secondary structures and functional properties, we offer an avenue for predicting novel motifs. Although several RNA databases exist, no comprehensive schemes are available for cataloguing the range and diversity of RNA's structural repertoire. RESULTS: Our RNA-As-Graphs (RAG) database describes and ranks all mathematically possible (including existing and candidate) RNA secondary motifs on the basis of graphical enumeration techniques. We represent RNA secondary structures as two-dimensional graphs (networks), specifying the connectivity between RNA secondary structural elements, such as loops, bulges, stems and junctions. We archive RNA tree motifs as 'tree graphs' and other RNAs, including pseudoknots, as general 'dual graphs'. All RNA motifs are catalogued by graph vertex number (a measure of sequence length) and ranked by topological complexity. The RAG inventory immediately suggests candidates for novel RNA motifs, either naturally occurring or synthetic, and thereby might stimulate the prediction and design of novel RNA motifs. AVAILABILITY: The database is accessible on the web at http://monod.biomath.nyu.edu/rna

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