Antimicrobial susceptibility of U.S. porcine Brachyspira isolates and genetic diversity of B. hyodysenteriae by multilocus sequence typing.

Swine dysentery, caused by Brachyspira hyodysenteriae and the newly recognized Brachyspira hampsonii in grower-finisher pigs, is a substantial economic burden in many swine-rearing countries. Antimicrobial therapy is the only commercially available measure to control and prevent Brachyspira-related colitis. However, data on antimicrobial susceptibility trends and genetic diversity of Brachyspira species from North America is limited. We evaluated the antimicrobial susceptibility profiles of U.S. Brachyspira isolates recovered between 2013 and 2022 to tiamulin, tylvalosin, lincomycin, doxycycline, bacitracin, and tylosin. In addition, we performed multilocus sequence typing (MLST) on 64 B. hyodysenteriae isolates. Overall, no distinct alterations in the susceptibility patterns over time were observed among Brachyspira species. However, resistance to the commonly used antimicrobials was seen sporadically with a higher resistance frequency to tylosin compared to other tested drugs. B. hampsonii was more susceptible to the tested drugs than B. hyodysenteriae and B. pilosicoli. MLST revealed 16 different sequence types (STs) among the 64 B. hyodysenteriae isolates tested, of which 5 STs were previously known, whereas 11 were novel. Most isolates belonged to the known STs: ST93 (n = 32) and ST107 (n = 13). Our findings indicate an overall low prevalence of resistance to clinically important antimicrobials other than tylosin and bacitracin, and high genetic diversity among the clinical Brachyspira isolates from pigs in the United States during the past decade. Further molecular, epidemiologic, and surveillance studies are needed to better understand the infection dynamics of Brachyspira on swine farms and to help develop effective control measures.

Normalizing real-time PCR results in routine testing.

Normalization, the process of controlling for normal variation in sampling and testing, can be achieved in real-time PCR assays by converting sample quantification cycles (Cqs) to "efficiency standardized Cqs" (ECqs). We calculated ECqs as E-ΔCq, where E is amplification efficiency and ΔCq is the difference between sample and reference standard Cqs. To apply this approach to a commercial porcine reproductive and respiratory syndrome virus (PRRSV) RT-qPCR assay, we created reference standards by rehydrating and then diluting (1 × 10-4) a PRRSV modified-live vaccine (PRRS MLV; Ingelvac) with serum or oral fluid (OF) to match the sample matrix to be tested. Sample ECqs were calculated using the mean E and reference standard Cq calculated from the 4 reference standards on each plate. Serum (n = 132) and OF (n = 130) samples were collected from each of 12 pigs vaccinated with a PRRSV MLV from -7 to 42 d post-vaccination, tested, and sample Cqs converted to ECqs. Mean plate Es were 1.75-2.6 for serum and 1.7-2.3 for OF. Mean plate reference standard Cqs were 29.1-31.3 for serum and 29.2-31.5 for OFs. Receiver operating characteristic analysis calculated the area under the curve for serum and OF sample ECqs as 0.999 (95% CI: 0.997, 1.000) and 0.947 (0.890, 1.000), respectively. For serum, diagnostic sensitivity and specificity of the commercial PRRSV RT-qPCR assay were estimated as 97.9% and 100% at an ECq cutoff ≥ 0.20, and for OF, 82.6% and 100%, respectively, at an ECq cutoff ≥ 0.45.

OssaNMA: An R package for using information from network meta-analyses to optimize the power and sample allocation of a new two-arm trial.

Randomized clinical trials (RCTs) are designed for measuring the effectiveness of the treatments and testing a hypothesis regarding the relative effect between two or more treatments. Trial designers are often interested in maximizing power when the total sample size is fixed or minimizing the required total sample size to reach a pre-specified power. One approach to maximizing power proposed by previous researchers is to leverage prior evidence using meta-analysis (NMA) to inform the sample size determination of a new trial. For example, researchers may be interested in designing a two-arm trial comparing treatments A and B which are already in the existing trial network but do not have any direct comparison. The researchers' intention is to incorporate the result into an existing network for meta-analysis. Here we develop formulas to address these options and use simulations to validate our formula and evaluate the performance of different analysis methods in terms of power. We also implement our proposed method into the R package OssaNMA and publish an R Shiny app for the convenience of the application. The goal of the package is to enable researchers to readily adopt the proposed approach which can improve the power of an RCT and is therefore resource-saving. In the R Shiny app, We also provide the option to include the cost of each treatment which would enable researchers to compare the total treatment cost associated with each design and analysis approach. Further, we explore the effect of allocation to treatment group on study power when the a priori plan is to incorporate the new trial result into an existing network for meta-analysis.

Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment.

BACKGROUND: Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn't been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z ('direct two-arm'), a two-arm trial between another old treatment B and Z ('indirect two-arm'), and a three-arm trial among A, B, and Z. METHODS: We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when [Formula: see text]. Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of [Formula: see text]. RESULTS: Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when [Formula: see text]. This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis. CONCLUSIONS: The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power.

In Vivo and In Vitro Characterization of the Recently Emergent PRRSV 1-4-4 L1C Variant (L1C.5) in Comparison with Other PRRSV-2 Lineage 1 Isolates.

The recently emerged PRRSV 1-4-4 L1C variant (L1C.5) was in vivo and in vitro characterized in this study in comparison with three other contemporary 1-4-4 isolates (L1C.1, L1A, and L1H) and one 1-7-4 L1A isolate. Seventy-two 3-week-old PRRSV-naive pigs were divided into six groups with twelve pigs/group. Forty-eight pigs (eight/group) were for inoculation, and 24 pigs (four/group) served as contact pigs. Pigs in pen A of each room were inoculated with the corresponding virus or negative media. At two days post inoculation (DPI), contact pigs were added to pen B adjacent to pen A in each room. Pigs were necropsied at 10 and 28 DPI. Compared to other virus-inoculated groups, the L1C.5-inoculated pigs exhibited more severe anorexia and lethargy, higher mortality, a higher fraction of pigs with fever (>40 °C), higher average temperature at several DPIs, and higher viremia levels at 2 DPI. A higher percentage of the contact pigs in the L1C.5 group became viremic at two days post contact, implying the higher transmissibility of this virus strain. It was also found that some PRRSV isolates caused brain infection in inoculation pigs and/or contact pigs. The complete genome sequences and growth characteristics in ZMAC cells of five PRRSV-2 isolates were further compared. Collectively, this study confirms that the PRRSV 1-4-4 L1C variant (L1C.5) is highly virulent with potential higher transmissibility, but the genetic determinants of virulence remain to be elucidated.

Critical evaluation of strategies to achieve direct real-time PCR detection of swine pathogens in oral fluids.

Based on publications reporting improvements in real-time PCR (rtPCR) performance, we compared protocols based on heat treatment or dilution followed by direct rtPCR to standard extraction and amplification methods for the detection of porcine reproductive and respiratory syndrome virus (PRRSV), influenza A virus (IAV), porcine epidemic diarrhea virus (PEDV), or Mycoplasma hyopneumoniae (MHP) in swine oral fluids (OFs). In part A, we subjected aliquots of positive OF samples to 1 of 4 protocols: protocol 1: heat (95°C × 30 min) followed by direct rtPCR; protocol 2: heat and cool (25°C × 20 min) followed by direct rtPCR; protocol 3: heat, cool, extraction, and rtPCR; protocol 4 (control): extraction and then rtPCR. In part B, positive OF samples were split into 3, diluted (D1 = 1:2 with Tris-borate-EDTA (TBE); D2 = 1:2 with negative OF; D3 = not diluted), and then tested by rtPCR using the best-performing protocol from part A (protocol 4). In part A, with occasional exceptions, heat treatment resulted in marked reduction in the detection of target and internal sample control (ISC) nucleic acids. In part B, sample dilution with TBE or OF produced no improvement in the detection of targets and ISCs. Thus, standard extraction and amplification methods provided superior detection of PRRSV, IAV, PEDV, and MHP nucleic acids in OFs.

Evaluation of a Porcine Endogenous Reference Gene (Internal Sample Control) in a Porcine Reproductive and Respiratory Syndrome Virus RT-qPCR.

Endogenous reference genes are used in gene-expression studies to "normalize" the results and, increasingly, as internal sample controls (ISC) in diagnostic quantitative polymerase chain reaction (qPCR). Three studies were conducted to evaluate the performance of a porcine-specific ISC in a commercial porcine reproductive and respiratory syndrome virus (PRRSV) reverse transcription-qPCR. Study 1 evaluated the species specificity of the ISC by testing serum from seven non-porcine domestic species (n = 34). In Study 2, the constancy of ISC detection over time (≥42 days) was assessed in oral fluid (n = 130), serum (n = 215), and feces (n = 132) collected from individual pigs of known PRRSV status. In Study 3, serum (n = 150), oral fluid (n = 150), and fecal samples (n = 75 feces, 75 fecal swabs) from commercial herds were used to establish ISC reference limits. Study 1 showed that the ISC was porcine-specific, i.e., all samples from non-porcine species were ISC negative (n = 34). In Study 2, the ISC was detected in all oral fluid, serum, and fecal samples, but differed in concentration between specimens (p < 0.05; mixed-effects regression model). The results of Study 3 were used to establish ISC reference limits for the 5th, 2.5th and 1.25th percentiles. Overall, the ISC response was consistent to the point that failure in detection is sufficient justification for re-testing and/or re-sampling.

Effect of extrinsic factors on the detection of PRRSV and a porcine-specific internal sample control in serum, oral fluid, and fecal specimens tested by RT-rtPCR.

We characterized the effect of 1) temperature × time, 2) freeze-thaw cycles, and 3) high porcine reproductive and respiratory syndrome virus (PRRSV) RNA concentrations on the detection of PRRSV and a porcine-specific internal sample control (ISC) in serum, oral fluid, and fecal samples using a commercial PRRSV RT-rtPCR assay (Idexx). In study 1, the effect of temperature × time on PRRSV and ISC detection was shown to be specimen dependent. In serum stored at 4, 10, or 20°C, PRRSV detection was consistent for up to 168 h, but storage at 30°C reduced detectable PRRSV RNA. ISC RNA was stable in serum held at 4 and 10°C, but not at 20 and 30°C. In contrast, PRRSV and ISC RNAs in oral fluid and fecal samples continuously decreased at all temperature × time treatments. Based on these data, serum samples should be stored at ≤ 20°C to optimize PRRSV RNA detection. Oral fluid and fecal samples should be frozen in a non-self-defrosting freezer until tested. In study 2, freeze-thaw cycles had little impact on PRRSV and ISC detection, but more so in oral fluids than serum or fecal samples. Thus, freeze-thaw cycles in oral fluids should be minimized before RT-rtPCR testing. In study 3, the ISC was not affected by high concentrations of PRRSV RNA in serum, oral fluid, or fecal samples. It should not be assumed that data from our PRRSV study are applicable to other pathogens; additional pathogen-specific studies are required.

When we shouldn't borrow information from an existing network of trials for planning a new trial.

Introduction: To achieve higher power or increased precision for a new trial, methods based on updating network meta-analysis (NMA) have been proposed by researchers. However, this approach could potentially lead to misinterpreted results and misstated conclusions. This work aims to investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. Methods: We use simulations to evaluate the scenarios of interest. In particular, a new trial is to be conducted independently or depending on the results from previous NMA in various scenarios. Three analysis methods are applied to each simulation scenario: with the existing network, sequential analysis and without the existing network. Results: For the scenario that the new trial will be conducted only when a promising finding (p-value <5%) is indicated by the existing network, the type I error risk increased dramatically (38.5% in our example data) when analyzed with the existing network and sequential analysis. The type I error is controlled at 5% when analyzing the new trial without the existing network. Conclusion: If the intention is to combine a trial result with an existing network of evidence, or if it is expected that the trial will eventually be included in a network meta-analysis, then the decision that a new trial is performed should not depend on a statistically "promising" finding indicated by the existing network.

Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment.

BACKGROUND: A critical step in trial design is determining the sample size and sample allocation to ensure the proposed study has sufficient power to test the hypothesis of interest: superiority, equivalence, or non-inferiority. When data are available from prior trials and leveraged with the new trial to answer the scientific questions, the value of society's investment in prior research is increased. When prior information is available, the trial design including the sample size and allocation should be adapted accordingly, yet the current approach to trial design does not utilize such information. Ensuring we maximize the value of prior research is essential as there are always constraints on resources, either physical or financial, and designing a trial with adequate power can be a challenge. METHODS: We propose an approach to increasing the power of a new trial by incorporating evidence from a network meta-analysis into the new trial design and analysis. We illustrate the methodology through an example network meta-analysis, where the goal is to identify the optimal allocation ratio for the new three-arm trial, which involves the reference treatment, the new treatment, and the negative control. The primary goal of the new trial is to show that the new treatment is non-inferior to the reference treatment. It may also be of interest to know if the new treatment is superior to the negative control. We propose an optimal treatment allocation strategy which is derived from minimizing the standard error of the log odds ratio estimate of the comparison of interest. We conducted a simulation study to assess the proposed methods to design a new trial while borrowing information from the existing network meta-analysis and compare it to even allocation methods. RESULTS: Using mathematical derivation and simulations, we document that our proposed approach can borrow information from a network meta-analysis to modify the treatment allocation ratio and increase the power of the new trial given a fixed total sample size or to reduce the total sample size needed to reach a desired power. CONCLUSIONS: When prior evidence about the hypotheses of interest is available, the traditional equal allocation strategy is not the most powerful approach anymore. Our proposed methodology can improve the power of trial design, reduce the cost of trials, and maximize the utility of prior investments in research.

Characterization of the Subclinical Infection of Porcine Deltacoronavirus in Grower Pigs under Experimental Conditions.

This study characterized the susceptibility and dynamic of porcine deltacoronavirus infection in grower pigs under experimental conditions using a combination of syndromic and laboratory assessments. Seven-week-old conventional pigs (n = 24) were randomly distributed into PDCoV- (n = 12) and mock-inoculated (n = 12) groups. Serum was collected at -7, 0, 3, 7, 10, 14, 17, 21, 28, 35, and 42 days post-inoculation (DPI) to evaluate viremia (RT-qPCR) and antibody response (S1-based ELISA). Viral shedding and potential infectivity were determined using pen-based oral fluids and feces collected every other day between DPI 0 and 42. Pigs showed no clinical signs or viremia throughout the study. Active virus shedding was detected in feces (6-22 DPI) and oral fluids (2-30 DPI), peaking at DPI 10. IgG was first detected at DPI 10, being statistically significant after DPI 14 and increasing thereafter, coinciding with the progressive resolution of the infection. Likewise, a significant increase in proinflammatory IL-12 was detected between DPI 10 and 21 in PDCoV-inoculated pigs, which could enhance innate resistance to PDCoV infection. This study demonstrated that active surveillance based on systematic sampling and laboratory testing combining molecular and serological tools is critical for the accurate detection of subclinical circulation of PDCoV in pigs after weaning.