Publications by authors named "Fangshu Ye"

Article Synopsis
  • Randomized clinical trials (RCTs) aim to assess the effectiveness of treatments and often focus on maximizing statistical power or minimizing sample size when designing trials.* -
  • Researchers can enhance trial design using prior evidence from network meta-analysis (NMA) to inform sample size for new studies, especially when treatments don't have direct comparisons.* -
  • The new R package OssaNMA and an accompanying R Shiny app provide tools for researchers to improve RCT power and evaluate treatment costs, while also examining how treatment group allocation affects study power.*
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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.

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Swine dysentery, caused by and the newly recognized 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 -related colitis. However, data on antimicrobial susceptibility trends and genetic diversity of species from North America is limited.

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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.

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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, 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) a PRRSV modified-live vaccine (PRRS MLV; Ingelvac) with serum or oral fluid (OF) to match the sample matrix to be tested.

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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 ( = 34).

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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 (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).

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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 -value of the comparison in the existing network, a "promising" difference between two treatments is noticed.

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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.

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Article Synopsis
  • Proper trial design requires careful determination of sample size and allocation to ensure sufficient power for testing hypotheses, but existing designs often fail to integrate prior research data.
  • The authors propose a methodology that leverages evidence from a network meta-analysis to enhance the design of new trials, optimizing the allocation ratio among treatments.
  • Simulations demonstrate that this approach can effectively increase trial power and better utilize available data, improving the chances of accurately assessing the new treatment's efficacy.
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Article Synopsis
  • The study investigated how grower pigs respond to porcine deltacoronavirus (PDCoV) infection by monitoring both clinical signs and laboratory tests over 42 days.
  • Two groups of pigs were observed: those infected with PDCoV and a control group, with regular samples taken to measure virus presence and immune response.
  • Key findings included no clinical symptoms in pigs, with virus shedding noted between days 6-22, antibody response beginning at day 10, and an increase in proinflammatory markers, indicating a need for effective surveillance strategies to monitor PDCoV in pig populations.
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