Table_1_Most common diagnoses and antibiotics used in South American Camelid patients at a university clinic in Austria.DOCX

Knowledge of common diseases and their treatment in minor species, such as llamas and alpacas, is growing, but frequently drugs are not licensed in these species. Our aim was to evaluate frequent diagnoses and commonly applied antibiotics in patients of a university clinic with a particular focus on EMA Category B antibiotics, which are critically important for human health. We retrospectively analyzed anonymized patient records between 2005 and 2019 regarding the causes for antibiotic treatment and choice of antibiotic substance. The most frequent indications for antibiotic treatment were diseases of the digestive tract and perioperative prophylaxis for castrations. The number of applications of EMA Category B antibiotics initially increased with the number of patients treated, then remained stable, while the use of Category D drugs increased over time. Most Category B antibiotics were used for diseases of crias and diseases of the digestive tract, primarily dental disease. The use of EMA Category B antibiotics cannot be completely avoided based on the types of cases treated. However, antibiotic stewardship guidelines should be followed wherever possible.</p

Interactive visualization of the complete trace of an observed station.

<p>Outbreak stations are marked in (striped) red. After selecting a station to be “observed” (blue) the view immediately shows the whole trace of that station: the green/green striped stations and deliveries indicate the forward trace and turquoise denotes the backward trace. In the two stations receiving deliveries from the blue station, cross-contamination is also assumed (black stripes). Deliveries leaving the observed station arrive at two outbreak stations (red/green), but not at the third one (red). This figure can be reproduced by using the available sample data.</p

KNIME workflow “Tracing and Visualization”.

<p>The “Supply Chain Reader” node is used to read tracing information from the integrated FoodChain-Lab database. Data processing results are then provided to the “Tracing” node, which is able to perform specific tracing calculations. The “Tracing View” node is the main node for interactive data and tracing analysis. Finally the “GIS View” node can be used to create a geographical visualization of the food chain network using additional GIS information fed in via the “Shapefile Reader” node.</p

Simple basic visualization combining network and geographical view.

<p>Interactive trade network visualization: network graph (left) and GIS map view based on shapefiles (right). Stations on the left and on the right are identical and always synchronized, i.e. the blue stations are identical. In contrast to the GIS view, the graph view automatically groups stations that are connected via deliveries to demonstrate relationships between stations. This figure can be reproduced by using the available sample data.</p

Collapsing many stations into one meta-station.

<p>The meta-station is a collapsed version of all stations from a certain country (collapse based on attribute “Country”) resulting in a common link (yellow), i.e. this collapsed station (the country) has traces to all outbreak stations (red) and therefore carries the maximum score of 1. This figure can be reproduced by using the available sample data.</p

Schematic description of the data structure in FoodChain-Lab.

<p>A) General principle of food supply chain reconstruction as performed by FoodChain-Lab. The connection between a delivery to one station to the lot of the following station is of major importance for tracing analysis. B) Detailed data structure used by FoodChain-Lab for storing food supply chain information. The most important attributes for detailed data analysis are highlighted in green.</p

EHEC outbreak 2011.

<p>All seven outbreak clusters in Germany and France (red) investigated in detail can be traced back to the source of the outbreak, which is the producer of fenugreek seeds in Egypt (yellow, the backtrace score calculated is 1). Numerous other stations without cases (no color) also received seeds from this producer. The automatically generated network view is synchronized with a geographical view based on maps from OpenStreetMap.</p

Comparison of <i>spa</i> Types, SCC<i>mec</i> Types and Antimicrobial Resistance Profiles of MRSA Isolated from Turkeys at Farm, Slaughter and from Retail Meat Indicates Transmission along the Production Chain

<div><p>The prevalence of MRSA in the turkey meat production chain in Germany was estimated within the national monitoring for zoonotic agents in 2010. In total 22/112 (19.6%) dust samples from turkey farms, 235/359 (65.5%) swabs from turkey carcasses after slaughter and 147/460 (32.0%) turkey meat samples at retail were tested positive for MRSA. The specific distributions of <i>spa</i> types, SCC<i>mec</i> types and antimicrobial resistance profiles of MRSA isolated from these three different origins were compared using chi square statistics and the proportional similarity index (Czekanowski index). No significant differences between <i>spa</i> types, SCC<i>mec</i> types and antimicrobial resistance profiles of MRSA from different steps of the German turkey meat production chain were observed using Chi-Square test statistics. The Czekanowski index which can obtain values between 0 (no similarity) and 1 (perfect agreement) was consistently high (0.79–0.86) for the distribution of <i>spa</i> types and SCC<i>mec</i> types between the different processing stages indicating high degrees of similarity. The comparison of antimicrobial resistance profiles between the different process steps revealed the lowest Czekanowski index values (0.42–0.56). However, the Czekanowski index values were substantially higher than the index when isolates from the turkey meat production chain were compared to isolates from wild boar meat (0.13–0.19), an example of a separated population of MRSA used as control group. This result indicates that the proposed statistical method is valid to detect existing differences in the distribution of the tested characteristics of MRSA. The degree of similarity in the distribution of <i>spa</i> types, SCC<i>mec</i> types and antimicrobial resistance profiles between MRSA isolates from different process stages of turkey meat production may reflect MRSA transmission along the chain.</p></div

Antibiotic use on German pig farms - A longitudinal analysis for 2011, 2013 and 2014

<div><p>To study antibiotic use in livestock in a temporal context with the development of antimicrobial resistance, long-term changes in antibiotic use must be mapped and their possible causes must be explored. Therefore, the present work assesses the changes in antibiotic use over time in German livestock husbandry. In addition, factors associated with antibiotic use were analyzed to identify possible strategies for further reducing antimicrobial usage. For 2011, 2013 and 2014, antibiotic usage data were collected and examined within the VetCAb project. Three hundred participating pig holdings provided information on their antibiotic use based on obligatory application and delivery forms (ADFs) filled in by their veterinarian as well as information on their current stabling capacities for each production type held. Data on sow, piglet, weaner and fattening pig holdings were described separately, using the semi-annual treatment frequency (TF) to measure antibiotic consumption. Multiple linear mixed models were used to investigate the effects of time, farm size, region and farm management category on the treatment frequency. The study yielded significant time changes with p-values below 0.001 in antibiotic administration with a decreasing median TF in piglets from 3.8 in the first half of 2011 (IQR = 1.1–10.6) to 1.7 in the second half of 2014 (IQR = 0.2–4.5) and in fattening pigs from 5.1 in the first half of 2011 (IQR = 0.2–15.4) to 0.7 in the second half of 2014 (IQR = 0.1–6.7). Meanwhile the TF fluctuated between 8.2 and 12.2 in weaners during the observational period (IQRs between zero (lower quartile) and 37.9 (upper quartile)). Piglet, weaner and fattening pig holdings belonging to the upper third of the holdings in size used significantly more antibiotics than the other holdings investigated. Particularly for weaner and fattening pig holdings, a higher TF was noted for farms without breeding units. The region was only a significant factor in weaners. In conclusion, for 2011, 2013 and 2014, the present study shows a clear reduction in antibiotic treatment frequency in German pig holdings. In addition, the association with various factors such as herd size and farm organization on the antibiotic usage frequency is indisputable. Therefore, these factors should be included in monitoring systems and considered when evaluating intervention measures.</p></div

Multi-factorial model results with logarithm transformation for the treatment frequency in sows.

<p>Multi-factorial model results with logarithm transformation for the treatment frequency in sows.</p