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Hernandez, M.

Publications and source records attributed to Hernandez, M..

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New insights of the local immune response against both fertile and infertile hydatid cysts

BackgroundCystic echinococcosis is caused by the metacestode of the zoonotic flatworm Echinococcus granulosus. Within the viscera of the intermediate host, the metacestode grows as a unilocular cyst known as hydatid cyst. This cyst is comprised of two layers of parasite origin: germinal and laminated layers, and one of host origin: the adventitial layer, that encapsulates the parasite. This adventitial layer is composed of collagen fibers, epithelioid cells, eosinophils and lymphocytes. To establish itself inside the host, the germinal layer produces the laminated layer, and to continue its life cycle, generates protoscoleces. Some cysts are unable to produce protoscoleces, and are defined as infertile cysts. The molecular mechanisms involved in cyst fertility are not clear, however, the host immune response could play a crucial role.\n\nMethodology/Principal fidingsWe collected hydatid cysts from both liver and lungs of slaughtered cattle, and histological sections of fertile, infertile and small hydatid cysts were stained with haematoxylin-eosin. A common feature observed in infertile cysts was the disorganization of the laminated layer by the infiltration of host immune cells. These infiltrating cells eventually destroy parts of laminated layer. Immunohistochemical analysis of both parasite and host antigens, identify these cells as cattle macrophages and are present inside the cysts associated to germinal layer.\n\nConclusions/SignificanceThis is the first report that indicates to cell from immune system present in adventitial layer of infertile bovine hydatid cysts could disrupt the laminated layer, infiltrating and probably causing the infertility of cyst.\n\nAuthor SummaryCystic echinococcosis is caused by the zoonotic flatworm Echinococcus granulosus. Within the viscera of the intermediate host, mainly liver and lungs of herbivores such as cows and sheep as well as human beings, the parasite grows as a unilocular cyst known as hydatid cyst. These cysts develop in their inner chamber a structure known as protoscolex, when consumed by the definitive host (e.g. dogs), it grows into a worm that resides in the small intestine and produces eggs that contaminate the environment. In cattle, most hydatid cysts are unable to produce protoscoleces, and thus are termed infertile hydatid cysts. The molecular mechanisms that explain the causes of hydatid cyst infertility remain unknown. We routinely collected cattle hydatid cysts from both liver and lugs and processed them for histological analysis. We found that there is a subset of fertile hydatid cysts that have low protoscolex viability and high immune infiltration surrounding the cyst. All infertile cysts have high immune infiltration, and many of them show disruption of the laminated layer and immune cells of host origin inside the cyst. This is the first report that shows that the cyst can be infiltrated by the host immune system.

immunology

Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

physiology