The effect of analyst training on fecal egg counting variability.

Fecal egg counts (FECs) are essential for veterinary parasite control programs. Recent advances led to the creation of an automated FEC system that performs with increased precision and reduces the need for training of analysts. However, the variability contributed by analysts has not been quantified for FEC methods, nor has the impact of training on analyst performance been quantified. In this study, three untrained analysts performed FECs on the same slides using the modified McMaster (MM), modified Wisconsin (MW), and the automated system with two different algorithms: particle shape analysis (PSA) and machine learning (ML). Samples were screened and separated into negative (no strongylid eggs seen), 1-200 eggs per gram of feces (EPG), 201-500 EPG, 501-1000 EPG, and 1001+ EPG levels, and ten repeated counts were performed for each level and method. Analysts were then formally trained and repeated the study protocol. Between analyst variability (BV), analyst precision (AP), and the proportion of variance contributed by analysts were calculated. Total BV was significantly lower for MM post-training (p = 0.0105). Additionally, AP variability and analyst variance both tended to decrease for the manual MM and MW methods. Overall, MM had the lowest BV both pre- and post-training, although PSA and ML were minimally affected by analyst training. This research illustrates not only how the automated methods could be useful when formal training is unavailable but also how impactful formal training is for traditional manual FEC methods.

Comparison of Clinical, Genetic, and Pathologic Features of Limbic and Diffuse Transactive Response DNA-Binding Protein 43 Pathology in Alzheimer's Disease Neuropathologic Spectrum.

BACKGROUND: Increasing evidence suggests that TAR DNA-binding protein 43 (TDP-43) pathology in Alzheimer's disease (AD), or AD-TDP, can be diffuse or limbic-predominant. Understanding whether diffuse AD-TDP has genetic, clinical, and pathological features that differ from limbic AD-TDP could have clinical and research implications. OBJECTIVE: To better characterize the clinical and pathologic features of diffuse AD-TDP and differentiate it from limbic AD-TDP. METHODS: 363 participants from the Mayo Clinic Study of Aging, Alzheimer's Disease Research Center, and Neurodegenerative Research Group with autopsy confirmed AD and TDP-43 pathology were included. All underwent genetic, clinical, neuropsychologic, and neuropathologic evaluations. AD-TDP pathology distribution was assessed using the Josephs 6-stage scale. Stages 1-3 were classified as Limbic, those 4-6 as Diffuse. Multivariable logistic regression was used to identify clinicopathologic features that independently predicted diffuse pathology. RESULTS: The cohort was 61% female and old at onset (median: 76 years [IQR:70-82]) and death (median: 88 years [IQR:82-92]). Fifty-four percent were Limbic and 46% Diffuse. Clinically, ∼10-20% increases in odds of being Diffuse associated with 5-year increments in age at onset (p = 0.04), 1-year longer disease duration (p = 0.02), and higher Neuropsychiatric Inventory scores (p = 0.03), while 15-second longer Trailmaking Test-B times (p = 0.02) and higher Block Design Test scores (p = 0.02) independently decreased the odds by ~ 10-15%. There was evidence for association of APOEɛ4 allele with limbic AD-TDP and of TMEM106B rs3173615 C allele with diffuse AD-TDP. Pathologically, widespread amyloid-ß plaques (Thal phases: 3-5) decreased the odds of diffuse TDP-43 pathology by 80-90%, while hippocampal sclerosis increased it sixfold (p < 0.001). CONCLUSION: Diffuse AD-TDP shows clinicopathologic and genetic features different from limbic AD-TDP.

Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples.

Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and the traditional McMaster (MM) and Wisconsin (MW) manual techniques. Additionally, a Bayesian analysis was performed to estimate and compare sensitivity and specificity of all five methods. Feces were collected from horses, screened with triplicate Mini-FLOTAC counts, and placed into five categories: negative (no eggs seen), > 0 - ≤ 200 eggs per gram (EPG), > 200 - ≤ 500 EPG, > 500 - ≤ 1000 EPG, and > 1000 EPG. Ten replicates per horse were analyzed for each technique. Technical variability for samples > 200 EPG was significantly higher for MM than CC/PSA and CC/ML (p <  0.0001). Biological variability for samples> 0 was numerically highest for CC/PSA, but with samples > 200 EPG, MM had a significantly lower CV than MW (p =  0.001), MW had a significantly lower CV than CC/PSA (p <  0.0001), CC/ML had a significantly lower CV than both MW and SP/PSA (p <  0.0001, p =  0.0003), and CC/PSA had a significantly lower CV than CC/SP (p =  0.0115). Sensitivity was> 98 % for all five methods with no significant differences. Specificity, however, was significantly the highest for CC/PSA, followed numerically by SP/PSA, MM, CC/ML, and finally MW. Overall, the automated counting system is a promising new development in equine parasitology. Continued refinement to the counting algorithms will help improve precision and specificity, while additional research in areas such as egg loss, analyst variability at the counting step, and accuracy will help create a complete picture of its impact as a new fecal egg count method.