More than just methylisothiazolinone: Retrospective analysis of patients with isothiazolinone allergy in North America, 2017-2020.

BACKGROUND: Isothiazolinones are a common cause of allergic contact dermatitis. OBJECTIVE: To examine the prevalence of positive patch test reactions to isothiazolinones from 2017-2020 and characterize isothiazolinone-allergic (Is+) patients compared with isothiazolinone nonallergic (Is-) patients. METHODS: Retrospective cross-sectional analysis of 9028 patients patch tested to methylchloroisothiazolinone (MCI)/methylisothiazolinone (MI) 0.02% aqueous, MI 0.2% aqueous, benzisothiazolinone (BIT) 0.1% petrolatum, and/or octylisothiazolinone (OIT) 0.025% petrolatum. Prevalence, reaction strength, concurrent reactions, clinical relevance, and source of allergens were tabulated. RESULTS: In total, 21.9% (1976/9028) of patients had a positive reaction to 1 or more isothiazolinones. Positivity to MI was 14.4% (1296/9012), MCI/MI was 10.0% (903/9017), BIT was 8.6% (777/9018), and OIT was 05% (49/9028). Compared with Is-, Is+ patients were more likely to have occupational skin disease (16.5% vs 10.3%, P <.001), primary hand dermatitis (30.2% vs 19.7%, P <.001), and be >40 years (73.1% vs 61.9%, P <.001). Positive patch test reactions to >1 isothiazolinone occurred in 44.1% (871/1976) of Is+ patients. Testing solely to MCI/MI would miss 47.3% (611/1292) of MI and 60.1% (466/776) of BIT allergic reactions. LIMITATIONS: Retrospective cross-sectional study design and lack of follow-up data. CONCLUSION: Sensitization to isothiazolinones is high and concurrent sensitization to multiple isothiazolinone allergens is common.

Computer vision model for the detection of canine pododermatitis and neoplasia of the paw.

BACKGROUND: Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology. ANIMALS: Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used. OBJECTIVES: We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia. MATERIALS AND METHODS: The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed. RESULTS: A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS. CONCLUSIONS AND CLINICAL RELEVANCE: This novel object detection model has the potential for application in the field of veterinary dermatology.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes.

BACKGROUND: The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions. METHODS: This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance. RESULTS: For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics. CONCLUSIONS: We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

DairyCoPilot-Automated data compilation and analysis tools for DairyComp data assets.

Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedicated tools to turn collected data sets into assets for informed decision-making processes. The DairyCoPilot app allows users to rapidly extract health and production data from DairyComp, then compile and analyze the data using a menu-driven point-and-click approach. Prospects for training consultants in applied data analysis skills make DairyCoPilot a tool to identify farm management bottlenecks with less time spent for data analysis, improving cow health, and dairy production. The DairyCoPilot Dashboard R Shiny application is published using RStudio Connect: https://connect.doit.wisc.edu/dairy-copilot/.

Comparative analysis of computer vision algorithms for the real-time detection of digital dermatitis in dairy cows.

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

A retrospective study of the efficacy of zonisamide in controlling seizures in 57 cats.

BACKGROUND: Evidence-based recommendations for antiepileptic drug selection in cats beyond phenobarbital are limited, and additional studies are needed for cats where seizures remain inadequately controlled by administration of phenobarbital alone or for cats that cannot safely receive phenobarbital. OBJECTIVE: To compare seizure frequency in cats before and after oral administration of zonisamide and describe adverse clinical or clinicopathologic effects in this cohort. ANIMALS: Fifty-seven cats with a history of seizures. METHODS: Multicenter, retrospective study. Median number of seizures per month and number of seizure days per month were compared before and after administration of zonisamide in all cats, a subgroup of cats with idiopathic epilepsy (IE), and a subgroup of cats receiving zonisamide as sole therapy. Clinical and clinicopathologic adverse effect data were also reported. RESULTS: A median decrease of 1 (P = .001, 95% confidence interval (CI) [-1.0, -0.5]) seizure per month, and 1 (P = .003, 95% CI [-1.5, -0.2]) seizure days per month was found across all cats after oral administration of zonisamide. The subgroup with IE showed median decreases of 1 (P = .03, 95% CI [-2.0, -0.5]) and 2 (P = .01, 95% CI [-2.5, -1.0]), respectively. The most common clinical adverse effects were sedation (17%), ataxia (11%), hyporexia (17%), and emesis (5%). One cat developed mild nonregenerative anemia, 2 cats developed mild metabolic acidosis, and 6 cats showed mild increases in ALT and ALP. CONCLUSION: Zonisamide was well tolerated and efficacious in controlling seizure activity in most cats.

Allergic or Not: Final Interpretation of Doubtful Patch Test Reactions From the North American Contact Dermatitis Group, 2019-2020.

Background: Doubtful patch test reactions generally do not meet criterion for positivity in patch testing. However, the North American Contact Dermatitis Group (NACDG) allows for doubtful reactions to be coded with a final determination of "allergic/positive" based on the temporal pattern, appearance, known characteristics of the allergen, and/or other supportive patch test reactions. Objectives: To analyze NACDG data from the 2019-2020 patch test cycle to identify patterns in the interpretation and relevance of doubtful reactions. Methods: The frequency and proportions of doubtful reactions were tabulated and analyzed for patterns. Statistical analyses were limited to allergens with ≥30 doubtful reactions to ensure adequate sample size. Results: Of patch-tested patients, 31.9% (1315/4121) had ≥1 doubtful reaction. Of 2538 total doubtful reactions, 46% (n = 1167) had a final interpretation of "allergic/positive." The allergens with the highest proportion of doubtful reactions at the final visit were hydroperoxides of linalool 1% (4.5%), fragrance mix I 8.0% (3.9%), and cetrimonium chloride 0.5% (3.4%). Methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) 0.02% (P < 0.001), MI 0.2% (P < 0.001), nickel sulfate hexahydrate 2.5% (P = 0.001), and neomycin sulfate 20.0% (P = 0.003) doubtful reactions were more likely to be interpreted as allergic than nonallergic. Methyldibromoglutaronitrile/phenoxyethanol 0.2% (P < 0.001), oleamidopropyl dimethylamine 0.1% (P < 0.001), formaldehyde 2.0% (P < 0.001), cetrimonium chloride 0.5% (P < 0.001), benzophenone-4 (sulisobenzone) 10% (P < 0.001), iodopropynyl butylcarbamate 0.5% (P < 0.001), cocamidopropyl betaine 1.0% (P = 0.002), and benzisothiazolinone 0.1% (P = 0.012) doubtful reactions were less likely to be interpreted as allergic. Of the 1167 doubtful reactions interpreted as allergic, 84.9% had current relevance. Conclusions: Doubtful reactions were common and approximately one half were coded with a final interpretation of "allergic/positive." Of those, most were clinically relevant. MCI/MI, MI, nickel, and neomycin were more likely to be interpreted as allergic.

Occupational Contact Dermatitis in Construction Workers: A Retrospective Analysis of the North American Contact Dermatitis Group Data, 2001-2020.

Background: Construction workers (CWs) are at risk for occupational contact dermatitis (CD) owing to workplace exposures. Objective: Determine the prevalence of occupational allergic CD and characterize common occupational allergens in CWs referred for patch testing in the United States and Canada. Methods: Retrospective cross-sectional analysis of patients patch tested by the North American Contact Dermatitis Group from 2001 to 2020. Results: Of 47,843 patch-tested patients, 681 (1.4%) were CWs. Compared with non-CWs, CWs were more likely to be male (91.0% vs 30.9%) have occupational skin disease (36.9% vs 11.4%) and have hand involvement (37.2% vs 22.5%) (all P < 0.0001). Of 681 CWs, 60.1% (411) had clinically relevant positive patch test reactions, and nearly 1/3 of CWs (128) had occupationally relevant reactions. Most common occupationally relevant allergens were potassium dichromate 0.25% pet. (30.5%, 39/128), bisphenol A epoxy resin 1% pet. (28.1%, 36/128), carba mix 3% pet. (14.8%, 19/128), cobalt (ii) chloride hexahydrate 1% pet. (14.1%, 18/128), and thiuram mix 1% pet. (14.1%, 18/128). Top sources of occupationally relevant allergens were cement/concrete/mortar (20.4%, 46/225), gloves (15.1%, 34/225), and coatings (paint/lacquer/shellac/varnish/stains) (9.8%, 22/225). Conclusions: Occupational CD in North American CWs is common. In this group, frequently identified etiological sources of occupational allergic CD included metals, epoxy resin, and rubber.

Differentiating between metabolic health statuses in Simmental cows and describing related milk fatty acids and relevant associated factors.

The aim of this observational study was to examine differences in milk fatty acid (FA) concentrations for different metabolic health statuses and for associated factors-specifically to examine with which FA concentrations an increased risk for developing a poor metabolic adaptation syndrome (PMAS) was associated. During weekly visits over 51 wk, blood samples were collected from cows between 5 and 50 days in milk. The farmer collected corresponding milk samples from all voluntary milkings. The analysis was performed on n = 2,432 samples from n = 553 Simmental cows. The observations were assigned to five different cow types (healthy, clever, athletic, hyperketonemic, and PMAS, representing five metabolic health statuses), based on the thresholds of 0.7 mmol/L, 1.2 mmol/L, and 1.4 for the concentrations of ß-hydroxybutyrate and nonesterified fatty acids and for the milk fat-to-protein ratio, respectively. Linear regression models using the predictor variables cow type, parity, week of lactation, and milk yield as fixed effects were developed using a stepwise forward selection to test for significant associations of predictor variables regarding FA concentrations in milk. There was a significant interaction term found between PMAS cows and parity compared to healthy cows for C18:1 (P < 0.001) and for C18:0 (P < 0.01). It revealed higher concentrations for PMAS in primiparous and multiparous cows compared to healthy cows, the slope being steeper for primiparous cows. Further, an interaction term was found between PMAS cows and milk yield compared to healthy cows and milk yield for C16:0 (P < 0.05), revealing a steeper slope for the decrease of C16:0 concentrations with increasing milk yield for PMAS compared to healthy cows. The significant associations and interaction terms between cow type, parity, week of lactation, and milk yield as predictor variables and C16:0, C18:0, and C18:1 concentrations suggest excellent opportunities for cow herd health screening during the early postpartum period.

Trends in the Prevalence of Methylchloroisothiazolinone/Methylisothiazolinone Contact Allergy in North America and Europe.

Importance: The common use of isothiazolinones as preservatives is a global cause of allergic contact dermatitis. Differences in allowable concentrations of methylisothiazolinone (MI) exist in Europe, Canada, and the US. Objective: To compare the prevalence of positive patch test reactions to the methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) combination and MI alone in North America and Europe from 2009 to 2018. Design, Setting, and Participants: This retrospective analysis of North American Contact Dermatitis Group, European Surveillance System on Contact Allergies (ESSCA), and the Information Network of Departments of Dermatology (IVDK) databases included data from patients presenting for patch testing at referral patch test clinics in North America and Europe. Exposures: Patch tests to MCI/MI and MI. Main Outcomes and Measures: Prevalence of allergic contact dermatitis to MCI/MI and MI. Results: From 2009 to 2018, participating sites in North America and Europe patch tested a total of 226 161 individuals to MCI/MI and 118 779 to MI. In Europe, positivity to MCI/MI peaked during 2013 and 2014 at 7.6% (ESSCA) and 5.4% (IVDK) before decreasing to 4.4% (ESSCA) and 3.2% (IVDK) during 2017 and 2018. Positive reactions to MI were 5.5% (ESSCA) and 3.4% (IVDK) during 2017 and 2018. In North America, the frequency of positivity to MCI/MI increased steadily through the study period, reaching 10.8% for MCI/MI during 2017 and 2018. Positive reactions to MI were 15.0% during 2017 and 2018. Conclusions and Relevance: The study results suggest that in contrast to the continued increase in North America, isothiazolinone allergy is decreasing in Europe. This trend may coincide with earlier and more stringent government regulation of MI in Europe.