Preparation of High-Molecular-Weight Aliphatic Polycarbonates by Condensation Polymerization of Diols and Dimethyl Carbonate

A synthetic strategy was developed for the condensation polymerization of aliphatic diols with dimethyl carbonate to produce high-molecular-weight aliphatic polycarbonates. In the first step, oligomers were formed bearing almost equal numbers of hydroxyl and methyl carbonate end-groups. In the second step, the condensation reaction was conducted at a high temperature (>180 °C) to connect the −OH and −OC­(O)­OCH₃ chain-ends while removing the generated methanol under reduced pressure. Small amounts of sodium alkoxide (0.02–0.5 mol %) were used as a catalyst. Using an anhydrous diol was crucial for increasing the reaction rate and also for obtaining reproducible results. In the second step, the pressure was gradually reduced and the temperature was optimized, in order to minimize side products. Using this strategy, high-molecular-weight poly­(1,4-butylene carbonate) (PBC) and its copolymers incorporating various other diols (2–10 mol %) were prepared, with weight-average molecular weight (<i>M</i>_w) of 100 000–200 000, in a short reaction time, totaling 6.5 h. This strategy was also effective for producing other high-molecular-weight aliphatic polycarbonates (<i>M</i>_w ∼ 200 000) using 1,6-hexanediol and cyclohexane-1,4-dimethanol. When the [−OH]/[−OCH₃] ratio of the oligomers generated in the first step deviated from ∼1, it was hard to attain such a high molecular weight

Comparison of Mini Mental Status Examination (MMSE) and Categorical Verbal Fluency Test (CVFT) scores between normal controls and Alzheimer's disease (AD) patients.

<p>^a Student's <i>t</i>- test.</p><p>^b Pearson Chi-square test.</p><p>^c Multivariate analysis of covariance, adjusting for education.</p

Bootstrap validation^a of the diagnostic accuracy for Alzheimer' disease of the Mini Mental Status Examination (MMSE), Categorical Fluency Test total score (CVFT-T) and composite score (CVFT-C).

<p>^a 100 runs; in each run, 60% samples were randomly selected for validations using bootstrap sampling estimation.</p><p>AUC, area under the receiver operator curver; SE, standard error; 95% CI, 95% confidence interval.</p

Logistic regression model for predicting Alzheimer's disease using the Categorical Verbal Fluency Test (CVFT).

<p>B, beta coefficient; SE, standard error; Wald, Wald statistics; OR, odds ratio; CI, confidence interval.</p

Diagnostic accuracies of the Mini Mental Status Examination (MMSE), Categorical Verbal Fluency Test total score (CVFT-T) and composite score (CVFT-C) for Alzheimer's disease.

<p>^a Optimal cut-off scores for Alzheimer's disease by receiver operator curve (ROC) analyses from predicted probability of age-, gender-, and education-adjusted logistic regression model.</p><p>AUC, area under ROC; SE, standard error; 95% CI, 95% confidence interval.</p