The presence of pharmaceuticals in the environment has triggered concern among the general population and received considerable attention from the scientific community in recent years. However, only a few publications have focused on anticancer drugs, a class of pharmaceuticals that can exhibit cytotoxic, genotoxic, mutagenic, carcinogenic and teratogenic effects. The present study investigated the photodegradation, biodegradation, bacterial toxicity, mutagenicity and genotoxicity of cyclophosphamide (CP) and 5-fluorouracil (5-FU). The photodegradation experiments were performed at a neutral to slight pH range (7–7.8) using two different lamps (medium-pressure mercury lamp and a xenon lamp). The primary elimination of the parent compounds was monitored by means of liquid chromatography tandem mass spectrometry (LC-IT-MS/MS). NPOC (non-purgeable organic carbon) analyses were carried out in order to assess mineralization rates. The Closed Bottle Test (CBT) was used to assess ready biodegradability. A new method using Vibrio fischeri was adopted to evaluate toxicity. CP was not degraded by any lamp, whereas 5-FU was completely eliminated by irradiation with the mercury lamp but only partially by the Xe lamp. No mineralization was observed for the experiments performed with the Xe lamp, and a NPOC removal of only 18% was registered for 5-FU after 256 min using the UV lamp. Not one of the parent compounds was readily biodegradable in the CBT. Photo transformation products (PTPs) resulting from photolysis were neither better biodegradable nor less toxic than the parent compound 5-FU. In contrast, the results of the tests carried out with the UV lamp indicated that more biodegradable and non-toxic PTPs of 5-FU were generated. Three PTPs were formed during the photodegradation experiments and were identified. The results of the in silico QSAR predictions showed positive mutagenic and genotoxic alerts for 5-FU, whereas only one of the formed PTPs presented positive alerts for the genotoxicity endpoint.

Nuclear factor-erythroid 2-related factor-2 (Nrf2) is an important modulator of cellular responses against Cd in mammalian cells. However, whether such modulation is conserved in Marsupenaeus japonicas remains unknown.In our study, the shrimps were injected with dsRNA targeting Nrf2 at 4 μg g⁻¹ body weight (b.w.) or sulforaphane (SFN) at 5 μg g⁻¹ b.w., and then were exposed to 40 mg L⁻¹ CdCl₂ for 48 h. After Nrf2 knockdown, the Cd content increased, but decreased in the SFN group. This suggested that Nrf2 could promote Cd excretion. A terminal deoxynulceotidyl transferase nick-end-labeling (TUNEL) assay revealed that the Nrf2 knockdown increased the number of apoptotic cells in M. japonicas, while SFN decreased the number of apoptotic cells. After Nrf2 knockdown, the total antioxidant capacity (T-AOC), superoxide dismutase (Sod) activity, and related gene expression decreased significantly, while the malondialdehyde (MDA) content increased remarkably. By contrast, SFN injection alleviated the oxidative stress, as evidenced by increased T-AOC, Sod activity, sod mRNA expression and a reduced MDA content. Similarly, detoxification related enzyme activities (ethoxyresorufin O-deethylase and glutathione-S-transferase (GST)) and their corresponding gene expressions (cyp3a (cytochrome P450 family 3 subfamily A) and gst) were suppressed in the ds-Nrf2 injection group, while they were elevated in the SFN group. In addition, ds-Nrf2 activated mitochondrial apoptotic pathway, as evidenced the mRNA and protein levels of caspase-3, Bcl2 associated X protein (Bax), and p53, while SFN treatment suppressed them. These results displayed that in M. japonicus Cd-induced cellular oxidative damage probably acts via the Nrf2 pathway.

Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.