Microbial impact to environmental toxicants Ni(II) and Co(II): Joint toxicity and cellular response in Paramecium.

Cobalt (Co) and Nickel (Ni) are increasingly found in our environment. We analysed their combined toxicity and uptake mechanisms in the early food chain by studying bacteria and the bacterivorous ciliate Paramecium as a primary consumer. We exposed both species to these metals to measure the toxicity, uptake and transfer of metals from bacteria to Paramecium. We found that Ni is more toxic than Co, and that toxicity increases for both metals when (i) food bacteria are absent and (ii) both metals are applied in combination. The cellular content in bacteria after exposure shows a concentration dependent bias for either Ni or Co. Comparing single treatment and joint exposure, bacteria show increased levels of both metals when these are both exposed. To imitate the basic level of the food chain, we fed these bacteria to paramecia. The cellular content shows a similar ratio of Nickel and Cobalt as in food bacteria. This is different to the direct application of both metals to paramecia, where Cobalt is enriched over Nickel. This indicates that bacteria can selectively pre-accumulate metals for introduction into the food chain. We also analysed the transcriptomic response of Paramecium to sublethal doses of Nickel and Cobalt to gain insight into their toxicity mechanisms. Gene ontology (GO) analysis indicates common deregulated pathways, such as ammonium transmembrane transport and ubiquitine-associated protein degradation. Many redox-related genes also show deregulation of gene expression, indicating cellular adaptation to increased RONS stress. This suggests that both metals may also target the same cellular pathways and this is consistent with the increased toxicity of both metals when used together. Our data reveal complex ecotoxicological pathways for these metals and highlights the different parameters for their fate in the ecosystem, in the food chain and their ecotoxicological risk after environmental contamination.

Healing predictors of stable juvenile osteochondritis dissecans knee lesions after 6 and 12 months of nonoperative treatment.

BACKGROUND: Nonoperative treatment of stable juvenile osteochondritis dissecans (JOCD) lesions of the knee fails in up to 50% of cases. Healing predictors are needed to identify potential failures and thus determine treatment options. PURPOSE: A predictive model for healing potential after 6 and 12 months of nonoperative treatment of stable JOCD lesions based on sensitive magnetic resonance imagining (MRI) follow-up measurements was developed. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A retrospective cross-sectional study was conducted to analyze 62 white patients (76 stable JOCD lesions) who were initially treated by restriction of activity until they were free of pain. The primary end point was healing investigated on MRI with follow-up measurements after 6 and 12 months of nonoperative treatment. Multivariate logistic regression was used to determine the influence of age, sex, JOCD lesion size, clinical symptoms, and the occurrence of cystlike lesions (CLLs) on healing potential. Additionally, optimal prognostic cutoffs were defined to differentiate failures from nonfailures. RESULTS: After 6 months of nonoperative treatment, 51 (67%) of 76 stable JOCD lesions showed no progression toward healing or showed signs of instability. Normalized lesion width and area and CLL occurrence differed significantly between failures and nonfailures (P < .05). A multivariate logistic regression best-predictors model that included age, CLL size, and normalized lesion width best predicted healing after 6 months and resulted in an area under the curve (AUC) of 0.779 (P < .001). A cutoff at 48% healing probability, as predicted by a nomogram based on age, normalized lesion width, and CLL size, differentiated failures from nonfailures (sensitivity, 60.0%; specificity, 83.7%). After 12 months, 37 lesions (49%) had progressed toward healing, and the sole observation of CLL size had the highest predictive validity (AUC, 0.766). The optimal cutoff was a healing probability of 61% (lesion size, 1.3 mm; sensitivity, 70.3%; specificity, 74.1%). CONCLUSION: A 6-month period of nonoperative treatment with or without casting might be appropriate if the healing potential is >48%. A 12-month period of nonoperative treatment may be successful if the CLL is <1.3 mm in length as assessed on MRI.