DDCT antibody
- Known as:
- DDCT (anti-)
- Catalog number:
- orb101707
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- Biorbyt biorb
- Gene target:
- DDCT antibody
Related genes to: DDCT antibody
- Gene:
- DDT NIH gene
- Name:
- D-dopachrome tautomerase
- Previous symbol:
- -
- Synonyms:
- DDCT, MIF2, MIF-2, D-DT
- Chromosome:
- 22q11.23
- Locus Type:
- gene with protein product
- Date approved:
- 1998-03-06
- Date modifiied:
- 2018-02-16
Related products to: DDCT antibody
Related articles to: DDCT antibody
- Legacy pollutants continue to be detected in wildlife and ecosystems, even decades after regulatory action. The study provides the first comprehensive assessment of polychlorinated biphenyls (PCB congeners 28, 52, 101, 138, 153, 180) and chlorinated pesticides, including hexachlorobenzene (HCB), hexachlorocyclohexane (HCH), and dichlorodiphenyltrichloroethane (DDT) and its metabolites in Black-headed Gull (Chroicocephalus ridibundus) eggs from the southern Baltic Sea, covering the largest breeding colonies between Germany and Poland. Sampling was conducted over 2 consecutive years. All eggs (n = 60) contained DDT and its metabolites and PCB congeners above the reporting level, with mean total PCB concentrations of 0.135-0.268 mg/kg and mean total DDT concentrations of 0.42-0.63 mg/kg. HCB was quantified at low levels (0.01-0.02 mg/kg) in five eggs, which does not allow conclusions about site-specific sources. HCH was not quantified in any sample. No relationships were found between contaminant concentrations and biometric parameters. The consistent occurrence and concentrations of PCBs and DDT metabolites highlight the enduring environmental relevance of these substances. Acute toxic effects were not directly assessed in this study; only associations with measured endpoints were evaluated. Our findings provide crucial new baseline data for the Baltic Sea region and reinforce the value of Black-headed Gull eggs as bioindicators of legacy and chemical exposure. - Source: PubMed
Publication date: 2026/10/07
Wlodarz MagdalenaHabedank FriederikeOrnés Angela Schmitz - An industry-wide survey of 26 biopharmaceutical companies benchmarked artificial intelligence and machine learning (AI/ML) adoption in translational and ADME sciences. AI/ML is widely used in early discovery and small-molecule workflows but rarely the primary driver of decision making. Data quality and organizational readiness were identified as key barriers to adoption. Respondents highlighted molecular design and hybrid mechanistic-AI approaches as key opportunities. Beyond benchmarking adoption, this work provides cross-industry perspectives on the scientific, organizational and data foundations needed to scale impact through integrated workflows, shared benchmarks and collaborative translational decision-making. - Source: PubMed
Publication date: 2026/10/07
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Publication date: 2026/10/07
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