Presentation_1_Optimizing methadone dose adjustment in patients with opioid use disorder.PDF

IntroductionOpioid use disorder is a cause for concern globally. This study aimed to optimize methadone dose adjustments using mixed modeling and machine learning.MethodsThis retrospective study was conducted at Taichung Veterans General Hospital between January 1, 2019, and December 31, 2020. Overall, 40,530 daily dosing records and 1,508 urine opiate test results were collected from 96 patients with opioid use disorder. A two-stage approach was used to create a model of the optimized methadone dose. In Stage 1, mixed modeling was performed to analyze the association between methadone dose, age, sex, treatment duration, HIV positivity, referral source, urine opiate level, last methadone dose taken, treatment adherence, and likelihood of treatment discontinuation. In Stage 2, machine learning was performed to build a model for optimized methadone dose.ResultsLikelihood of discontinuation was associated with reduced methadone doses (β = 0.002, 95% CI = 0.000–0.081). Correlation analysis between the methadone dose determined by physicians and the optimized methadone dose showed a mean correlation coefficient of 0.995 ± 0.003, indicating that the difference between the methadone dose determined by physicians and that determined by the model was within the allowable range (p ConclusionWe developed a model for methadone dose adjustment in patients with opioid use disorders. By integrating urine opiate levels, treatment adherence, and likelihood of treatment discontinuation, the model could suggest automatic adjustment of the methadone dose, particularly when face-to-face encounters are impractical.</p

Synthesis, Characterization, and Electrochemistry of Nanotubular Polypyrrole and Polypyrrole-Derived Carbon Nanotubes

Polypyrrole nanotubes (PPy-NTs) were prepared by the oxidation of pyrrole with iron(III) chloride in the presence of a structure-guiding agent, methyl orange. Upon carbonization of the salt form of PPy-NTs, the conducting nitrogen-containing nanotubular carbonaceous material (C-PPy-NT) was obtained. The morphology, structure, and physicochemical properties of PPy-NTs in salt and base form as well as C-PPy-NTs were investigated by transmission electron microscopy, Fourier transform infrared and Raman spectroscopies, conductivity measurements, elemental microanalysis, inductively coupled plasma optical emission spectroscopy, X-ray photoelectron spectroscopy, and nitrogen physisorption. Results of the material characterization were linked to their electrochemical behavior. Specific capacitance of around 120 F g(-1) at low potential sweep rate of 5 mV s(-1) was observed for original PPy-NTs. However, when the potential sweep rate was increased to 100 mV s(-1), PPy-NT salt retained the value of specific capacitance, while the capacitance of PPy-NT base decreased by 70%. Upon carbonization of PPy-NT salt, the specific capacitance was doubled and capacitance fade measured in the interval 5-100 mV s(-1) was determined to be around 45%. It is proposed that the absolute value of specific capacitance is determined by specific surface area and surface functional groups, while the capacitance fade is determined by the conductivity of the electrode material. In this manner, a linear relationship between the percent of capacitance fade and the logarithm of the conductivity was revealed. C-PPy-NTs were also tested as an electrocatalyst for the oxygen reduction reaction (ORR) in alkaline media. High ORR activity was observed, characterized by the onset potential of -0.1 V versus saturated calomel electrode and the apparent number of electrons consumed per oxygen molecule higher than 3. Appreciable ORR activity can be linked with a high fraction of mesopores and the presence of surface functional groups, especially pyridinic and pyrrolic nitrogens, and also with a high degree of structural disorder