A predictive model for L-T4 dose in postoperative DTC after RAI therapy and its clinical validation in two institutions.

Purpose: To develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions.

Methods: A total of 266 DTC patients who received RAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was validated through a two-center prospective study (n=263).

The role of Cu(II) in the reduction of N-nitrosodimethylamine with iron and zinc.

The role of Cu(II) in the reduction of N-nitrosodimethylamine (NDMA) with zero-valent metals was investigated by determining the effects of Cu(II) on the removal, kinetics, products, and mechanism. NDMA removal was enhanced, and all reactions followed a pseudo-first-order kinetic model except for the Fe and Fe/0.1 mM Cu(II) systems.

Reduction of N-nitrosodimethylamine with zero-valent zinc.

N-Nitrosodimethylamine (NDMA) is known as the disinfection by-product and the pollutant in the source water. Reduction with zero-valent zinc (Zn(0)) was investigated as a potential technology to treat NDMA. The results showed that Zn(0) was effective for NDMA reduction at initial pH 7.