Transcutaneous electrical nerve stimulation (TENS) has been shown to be an effective measure for pain relief. The aim of the present study was to determine the optimal intensity and interval of repeated 100 Hz TENS for the treatment of chronic inflammatory hyperalgesia in a monoarthritic pain model of the rat, and to assess the changes of the spinal substance P (SP) release in response to TENS treatment. A reliable, reproducible chronic monoarthritic pain model was produced by intra-articular injection of complete Freund's adjuvant (CFA) at single ankle joint. The efficacy of 100 Hz TENS treatments with different frequencies and intensities was compared. In the acute period (within 3 weeks) of monoarthritis, twice-a-week schedule of TENS reduced the swelling of the inflamed ankle significantly. In the stable period (4–9 weeks), however, once-a-week schedule produced a significantly better therapeutic effect on both inflammation and arthritic hyperalgesia than that of twice- or five-times-a-week schedule. Using three levels of intensity of TENS, we found that the weaker (1-1-2 mA) stimulation produced significantly better therapeutic effects. Repeated TENS produced a reduction of SP content in spinal perfusate in parallel with the progressive reduction of the arthritic pain scores. Our results suggest that (i) consecutive TENS treatments produced cumulative effect for chronic hyperalgesia, (ii) for chronic inflammatory hyperalgesia, a weaker intensity and more sparsely arranged treatment schedule may produce better therapeutic effect and (iii) a decrease in SP release may serve as one of the possible neurochemical mechanisms underlying the therapeutic effects of multiple TENS treatments on chronic inflammatory hyperalgesia.

The cDNA fragment of human TRAIL (TNF-related apoptosis inducing ligand) was cloned into RevTet-On, a Tetregulated and high-level gene expression system. The gene expression system was constructed in a human leukemic cell line: Jurkat. By using RevTet-On TRAIL gene expression system in Jurkat as a cell model, we studied the influence of TRAIL gene on the changes of cellular apoptosis before and after the TRAIL gene expression, which was induced by adding tetracycline derivative doxycycline (Dox). The results indicated that the cellular apoptosis ratio was largely dependent on the TRAIL gene expression level. Moreover, it was found that the apoptosis-inducing TRAIL could cause significant changes in the biophysical properties of Jurkat cells. The cell surface charge density decreased, the membrane fluidity declined, the elastic coefficients K 1 increased, and the proportion of α-helix in membrane protein secondary structure decreased. Thus, the apoptosis-inducing TRAIL gene caused significant changes on the biomechanic properties of Jurkat cells.

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Quantifying the temporal variation of wastewater treatment plant (WWTP) discharges is essential for water pollution control and environment protection in metropolitan areas. This study develops an ensemble machine learning (ML) model to predict discharges from WWTPs and to quantify the contribution of extraneous water (mixed precipitation and infiltrated groundwater) by leveraging the power of ML and population migration big data. The approach is applied to predict the discharges at 265 WWTPs in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. The major conclusions are as follows. First, the ensemble ML model provides an efficient and reliable way to predict WWTP discharges using data easily accessible to the public. The predicted treated sewage amount increased from 20.4 × 106 m3/day in 2015 to 24.5 × 106 m3/day in 2020. Second, the predictors, including daily precipitation, average precipitation of past proceeding days, daily temperature, and population migration, play different roles in predicting different city’s discharges. Finally, mixed precipitation and infiltrated groundwater account for, on average, 1.6 and 10.3% of total discharges from WWTPs in the GBA. This study represents the first attempt to bring population migration big data into data-driven environmental engineering modeling and can be easily extended to predict other environmental variables of concern.