RESUMEN
We use measured aerosol fine particulate matter (PM2.5) data, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and meteorological parameters (temperature, wind speed, wind direction, boundary layer height, and relative humidity) from the Chinese national control monitoring network, to consider seasonal and regional differences in the relationship between AOD and PM2.5. We propose a two-stage combined estimation model of PM2.5 concentrations based on the ε-support vector regression (ε-SVR/Epsilon-SVR) and the Mind Evolutionary Computation-BP neural network (MEC-BP) for analyzing spatiotemporal variations in PM2.5 concentrations in China between 2000 and 2017. The results showed that the two-stage combined estimation model provided a reliable estimation of the monthly ground-level PM2.5 concentrations at a spatial resolution of 1°×1° during 2000-2017 in China. This effectively offsets the time and space gaps in the current data sets of the ground monitoring network (R2=0.838, root mean square errors (RMSE)=11.512 µg·m-3, mean absolute percentage error (MAPE)=14.905%, mean squared percentage error (MSPE)=0.243%, mean absolute error (MAE)=6.476 µg·m-3, mean squared error (MSE)=132.519 µg·m-3). The preliminary spatiotemporal analysis results showed that:â Over the period 2000-2017, 2014 represented an important demarcation point for the annual PM2.5 concentration, as its trend changed from one of continuous increase to one of rapid decrease. The PM2.5 concentration decreases more rapidly in areas with high concentrations of PM2.5 in particular, including the northern coastal area, the eastern coastal area, and the middle reaches of the Changjiang River. â¡ During the studied period, the annual average PM2.5 concentration exceeded the second level criterion of the Chinese national air quality standard (35 µg·m-3) over more than 65% of China. Although the PM2.5 pollution situation in China improved to a certain extent in the latter years of the studied period, the air pollution situation remained poor.
RESUMEN
Hyperprolactinemia is a common adverse effect that occurs as a result of antipsychotic therapies, which often results in discontinuation. Empirical evidence has shown that some herbal medicines have suppressive effects on prolactin (PRL) hyperactivities. This study was designed to compare the herbal preparation called Peony-Glycyrrhiza Decoction (PGD) with bromocriptine (BMT), a dopamine agonist widely used for PRL-secreting disorders, in the treatment of risperidone-induced hyperprolactinemia. Twenty schizophrenic women who were under risperidone maintenance treatment, diagnosed with hyperprolactinemia (serum PRL levels >50 mug/L), and currently experiencing oligomenorrhea or amenorrhea were selected for the study. Subjects were randomized to additional treatment with PGD (45 g/d) followed by BMT (5 mg/d) or BMT followed by PGD at the same doses for 4 weeks each, with an interval of 4-week washout period between 2 treatment sessions. The severity of psychotic symptoms, adverse events, serum PRL, estradiol, testosterone, and progesterone levels were examined at baseline and end point. Peony-Glycyrrhiza Decoction treatment produced a significant baseline-end point decrease in serum PRL levels, without exacerbating psychosis and changing other hormones, and the decreased amplitudes were similar to those of BMT (24% vs 21%-38%). Moreover, there was a significantly greater proportion of patients during PGD treatment than BMT treatment showing improvements on adverse effects associated with hyperprolactinemia (56% vs 17%, P = 0.037). These results suggest that the herbal therapy can yield additional benefits while having comparable efficacy in treating antipsychotic-induced hyperprolactinemia in individuals with schizophrenia.