The presence of Mycobacterium tuberculosis complex (MTBC) in wild swine, such as in wild boar (Sus scrofa) in Eurasia, is cause for serious concern. Development of accurate, efficient, and noninvasive methods to detect MTBC in wild swine would be highly beneficial to surveillance and disease management efforts in affected populations. Here, we describe the first report of identification of volatile organic compounds (VOC) obtained from the breath and feces of wild boar to distinguish between MTBC-positive and MTBC-negative boar. We analyzed breath and fecal VOC collected from 15 MTBC-positive and 18 MTBC-negative wild boar in Donaña National Park in Southeast Spain. Analyses were divided into three age classes, namely, adults (>2 years), sub-adults (12–24 months), and juveniles (<12 months). We identified significant compounds by applying the two-tailed statistical t-test for two samples assuming unequal variance, with an α value of 0.05. One statistically significant VOC was identified in breath samples from adult wild boar and 14 were identified in breath samples from juvenile wild boar. One statistically significant VOC was identified in fecal samples collected from sub-adult wild boar and three were identified in fecal samples from juvenile wild boar. In addition, discriminant function analysis (DFA) was used to build classification models for MTBC prediction in juvenile animals. Using DFA, we were able to distinguish between MTBC-positive juvenile wild boar and MTBC-negative juvenile wild boar using breath VOC or fecal VOC. Based on our results, further research is warranted and should be performed using larger sample sizes, as well as wild boar from various geographic locations, to verify these compounds as biomarkers for MTBC infection in this species. This new approach to detect MTBC infection in free-ranging wild boar potentially comprises a reliable and efficient screening tool for surveillance in animal populations.

We present here the first study that directly correlates gastric cancer (GC) with specific biomarkers in the exhaled breath composition on a South American population, which registers one of the highest global incidence rates of gastric affections. Moreover, we demonstrate a novel solid state sensor that predicts correct GC diagnosis with 97% accuracy. Alveolar breath samples of 30 volunteers (patients diagnosed with gastric cancer and a controls group formed of patients diagnosed with other gastric diseases) were collected and analyzed by gas-chromatography/mass-spectrometry (GC-MS) and with an innovative chemical gas sensor based on gold nanoparticles (AuNP) functionalized with octadecylamine ligands. Our GC-MS analyses identified 6 volatile organic compounds that showed statistically significant differences between the cancer patients and the controls group. These compounds were different from those identified in previous studied performed on other populations with high incidence rates of this malady, such as China (representative for Eastern Asia region) and Latvia (representative for Baltic States), attributable to lifestyle, alimentation and genetics differences. A classification model based on principal component analysis of our sensor data responses to the breath samples yielded 97% accuracy, 100% sensitivity and 93% specificity. Our results suggest a new and non-intrusive methodology for early diagnosis of gastric cancer that may be deployed in regions lacking well-developed health care systems as a prediagnosis test for selecting the patients that should undergo deeper investigations (e.g., endoscopy and biopsy).

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