Entomotoxicology studies employ analytical methods and instrumentation to detect chemical substances in carrion insects feeding from the decomposing tissues. The identification of such chemicals may determine the cause of death and may be used for the estimation of the minimum time since death. To date, the main focus of entomotoxicological studies has been the detection of drugs, whereas little information concerns the effects of pesticides on blowflies. Pesticides are generally freely available and more affordable than drugs but they can also be a home hazard and an accessible candidate poison at a crime scene. A QuEChERS extraction method followed by Gas chromatography-mass spectrometry (GC-MS) analysis was developed for the detection of α- and β-endosulfan (organochlorine insecticide and acaricide) in Calliphora vomitoria L. (Diptera: Calliphoridae) and validated. Furthermore, the effects of endosulfan on the morphology, development time and survival of the immature blowflies were investigated. Larvae were reared on liver substrates homogeneously spiked with aliquots of endosulfan corresponding to the concentrations found in body tissues of humans and animals involved in endosulfan poisoning. Results demonstrated that the combination of QuEChERS extraction and GC-MS provide an adequate methods to detect both α- and β-endosulfan in blowfly immatures. Furthermore, the presence of α- and β-endosulfan in the food source 1) prevented C. vomitoria immatures reaching the pupal instar and, therefore, the adult instar at high concentrations, 2) did not affect the developmental time of blowflies at low concentrations 3) affected the size of immatures only at high concentrations, resulting in significantly smaller larvae.
ToF-SIMS has been increasingly widely used in recent years to look at biological matrices, in particular for biomedical research, although there is still a lot of development needed to maximise the value of this technique in the life sciences. The main issue for biological matrices is the complexity of the mass spectra and therefore the difficulty to specifically and precisely detect analytes in the biological sample. Here we evaluated the use of ToF-SIMS in the agrochemical field, which remains a largely unexplored area for this technique. We profiled a large number of biocidal active ingredients (herbicides, fungicides, and insecticides); we then selected fludioxonil, a halogenated fungicide, as a model compound for more detailed study, including the effect of co-occurring biomolecules on detection limits. There was a wide range of sensitivity of the ToF-SIMS for the different active ingredient compounds, but fludioxonil was readily detected in real-world samples (wheat seeds coated with a commercial formulation). Fludioxonil did not penetrate the seed to any great depth, but was largely restricted to a layer coating the seed surface. ToF-SIMS has clear potential as a tool for not only detecting biocides in biological samples, but also mapping their distribution.
Introduction Pulmonary infection (PI) is the most common complication post-oesophagectomy. Identifying patients at risk of PI could facilitate pre-emptive preventative measures. Analysis of exhaled volatile organic compounds (VOCs) is a novel method of diagnosing disease non-invasively. Given the integral contribution of the oro-respiratory system in generating breath VOCs, this study applied breathomics to develop a novel predictive model for PI prior to oesophagectomy. Methods Breath samples were collected from patients with oesophageal adenocarcinoma on the morning of their surgery using a quality-controlled methodologically optimised workflow. Breath was analysed using two gold standard analytical platforms: one (GC-MS) and two-dimensional (GCxGC-MS) thermal-desorption gas-chromatography time-of-flight mass-spectrometry, the latter being unrivalled in its chromatographic resolution. Raw spectral data was pre-processed and analysed using a tile-based Fisher ratio method to generate predictive models. Ethical approval REC: 17/WA/0161. Results Breath samples were analysed from 23 patients undergoing oesophagectomy and 12 (52%) developed PI. Principal component analysis revealed significantly distinct volatolomes discriminating PI risk status (R2Xcum 0.908, Q2cum 0.806, CV ANOVA p<0.001). A 5 VOC model had an area under the curve of 0.806 (95%CI 0.647-0.947) and 0.859 (95%CI 0.601-1) for predicting PI using GC-MS and GCxGC-MS respectively. Predictive VOCs were tentatively identified to be branched chain in structure suggesting a likely microbial origin. Conclusion This is the first predictive PI model using state-of-the-art breathomics. Identification of branched chain VOCs offers unprecedented insights into the disease biology of post-oesophagectomy PI and highlights an important role of the preoperative oro-respiratory microbiome as a risk factor for PI.
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