Monthly Archives: December 2012

Streptococcus suis (S. suis) has often been reported as an important swine pathogen and is considered as a new emerging zoonotic agent. Consequently, it is important to be informed on its susceptibility to antimicrobial agents. In the current study, the Minimum Inhibitory Concentration (MIC) population distribution of nine antimicrobial agents has been determined for nasal S. suis strains, isolated from healthy pigs at the end of the fattening period from 50 closed or semiclosed pig herds. The aim of the study was to report resistance based on both clinical breakpoints (clinical resistance percentage) and epidemiological cutoff values (non-wild-type percentage). Non-wild-type percentages were high for tetracycline (98%), lincomycin (92%), tilmicosin (72%), erythromycin (70%), tylosin (66%), and low for florfenicol (0%) and enrofloxacin (0.3%). Clinical resistance percentages were high for tetracycline (95%), erythromycin (66%), tylosin (66%), and low for florfenicol (0.3%) and enrofloxacin (0.3%). For tiamulin, for which no clinical breakpoint is available, 57% of the isolates did not belong to the wild-type population. Clinical resistance and non-wild-type percentages differed substantially for penicillin. Only 1% of the tested S. suis strains was considered as clinically resistant, whereas 47% of the strains showed acquired resistance when epidemiological cutoff values were used. In conclusion, MIC values for penicillin are gradually increasing, compared to previous reports, although pigs infected with strains showing higher MICs may still respond to treatment with penicillin. The high rate of acquired resistance against tiamulin has not been reported before. Results from this study clearly demonstrate that the use of different interpretive criteria contributes to the extent of differences in reported antimicrobial resistance results. The early detection of small changes in the MIC population distribution of isolates, while clinical failure may not yet be observed, provides the opportunity to implement appropriate risk management steps.

Influenza virus infects a wide variety of species including humans, pigs, horses, sea mammals and birds. Weight loss caused by influenza infection and/or co-infection with other infectious agents results in significant financial loss in swine herds. The emergence of pandemic H1N1 (A/CA/04/2009/H1N1) and H3N2 variant (H3N2v) viruses, which cause disease in both humans and livestock constitutes a concerning public health threat. Influenza virus contains eight single-stranded, negative-sense RNA genome segments. This genetic structure allows the virus to evolve rapidly by antigenic drift and shift. Antigen-specific antibodies induced by current vaccines provide limited cross protection to heterologous challenge. In pigs, this presents a major obstacle for vaccine development. Different strategies are under development to produce vaccines that provide better cross-protection for swine. Moreover, overriding interfering maternal antibodies is another goal for influenza vaccines in order to permit effective immunization of piglets at an early age. Herein, we present a review of influenza virus infection in swine, including a discussion of current vaccine approaches and techniques used for novel vaccine development.

This study was conducted to evaluate the relationship between boar and semen related parameters and the variation in field fertility results. In 8 years time semen insemination doses from 110,186 ejaculates of 7429 boars were merged to fertility parameters of inseminations of 165,000 sows and these records were used for analysis. From all ejaculates boar and semen related data were recorded at the artificial insemination (AI) centers. Fertility parameters, such as farrowing rate (FR), ranging between 80.0% and 84.0%, and the total number of piglets born (TNB), ranging between 12.7 and 13.1, were recorded and from these the least square means per ejaculate were calculated. Only 5.9% of the total variation in FR was due to boar and semen variability of which 21% (P = 0.0001) was explained by genetic line of the boar, 11% (P = 0.047) was explained by laboratory technician, and 7% (P = 0.037) was explained by the AI center. For TNB the total variation was 6.6% boar and semen related of which 28% (P < 0.0001) was explained by genetic line of the boar and 7% (P = 0.011) was explained by the AI center. Only 4% of the boar and semen related variation was caused by sperm motility (microscopically assessed at collection, ranging from 60% to 90%). Other variation in FR and TNB was explained by management and semen related parameters (age of boar, 3%; P = 0.009; and 8%; P = 0.031, respectively), days between ejaculations (1%; < 0.0001 of FR), number of cells in ejaculate (1%; P = 0.042 of TNB), year (9%; P = 0.032), and 13%; P = 0.0001, respectively), and month (11%; P = 0.0001; and 5%; P = 0.0001, respectively). Although semen motility is considered an important parameter to validate the quality of the ejaculate processed, it only minimally relates to fertility results under the current Dutch AI practice. Other boar and semen related parameters, like genetic line of the boar, are more relevant factors to select boars for AI purposes.