Digital dermatitis in Swedish dairy herds assessed by ELISA targeting Treponema phagedenis in bulk tank milk.

BACKGROUND: Digital dermatitis (DD) is a contagious hoof infection affecting cattle worldwide. The disease causes lameness and a reduction in animal welfare, which ultimately leads to major decreases in milk production in dairy cattle. The disease is most likely of polymicrobial origin with Treponema phagedenis and other Treponema spp. playing a key role; however, the etiology is not fully understood. Diagnosis of the disease is based on visual assessment of the feet by trained hoof-trimmers and veterinarians, as a more reliable diagnostic method is lacking. The aim of this study was to evaluate the use of an enzyme-linked immunosorbent assay (ELISA) on bulk tank milk samples testing for the presence of T. phagedenis antibodies as a proxy to assess herd prevalence of DD in Swedish dairy cattle herds. RESULTS: Bulk tank milk samples were collected in 2013 from 612 dairy herds spread across Sweden. A nationwide DD apparent prevalence of 11.9% (8.1-14.4% CI95%) was found, with the highest proportion of test-positive herds in the South Swedish regions (31.3%; 19.9-42.4% CI95%). CONCLUSIONS: This study reveals an underestimation of DD prevalence based on test results compared to hoof trimming data, highlighting the critical need for a reliable and accurate diagnostic method. Such a method is essential for disease monitoring and the development of effective control strategies. The novelty of ELISA-based diagnostic methods for DD, coupled with the disease's polymicrobial origin, suggests an avenue for improvement. Developing an expanded ELISA, incorporating antigens from various bacterial species implicated in the disease, could enhance diagnostic accuracy. The significance of this study is underscored by the extensive analysis of a substantial sample size (612). Notably, this investigation stands as the largest assessment to date, evaluating the application of ELISA on bulk tank milk for DD diagnosis at the herd level.

Finding significant pathogens in blood cultures in children: Should we set the timer to 36 hours?

Background: Knowledge of time to positivity (TTP) for blood cultures is useful to assess timing of discontinuation of empiric antimicrobials for suspected bacteremia with no focus. Methods: An audit of positive blood cultures from the Children's Hospital of Eastern Ontario (CHEO) from November 1, 2019, to October 31, 2020, was performed to determine TTP, defined as the start of incubation to a positive signal from automated incubators. Results: Three hundred seventy-six positive blood cultures were identified from 248 patients (average age: 6.27 [SD 6.24] years). Of these, 247 isolates were speciated; 90 (36.4%) were definitive/probable (DP) pathogens (median TTP 12.75 hours) and 157 (63.6%) possible/probable (PP) contaminants (median TTP 24.08 hours). At each time point, the adjusted rate of positive blood culture was significantly higher for DP pathogens compared to PP contaminants (hazard ratio [HR] 1.80 [95% CI 1.37, 2.36]) and for children ≤27 days old compared to the oldest age group (HR 1.94 [95% CI 1.19, 3.17]). By 36 hours, the proportion of positive cultures was significantly higher in the youngest age group (≤27 days) compared with the 3-11 years old age group (91.7% [95% CI 68.6%, 97.8%] versus 58.2% [95% CI 46.91%, 68.06%]). Conclusion: Across all ages, the TTP was significantly shorter for blood cultures with DP pathogens compared to those with PP contaminants (HR 1.80 [95% CI 1.37, 2.36]). In newborns, 90% of blood cultures were positive by 36 hours supporting this re-assessment time for empiric antimicrobials. TTP was longer in children ≥12 months, possibly related to other factors such as blood culture volume.

Historique: Il est utile de connaître le délai de positivité (DdP) des hémocultures pour évaluer le moment de mettre un terme aux antimicrobiens empiriques en cas de présomption de bactériémie sans source apparente. Méthodologie: Les chercheurs ont procédé à un audit des hémocultures positives du Centre hospitalier pour enfants de l'est de l'Ontario (CHEO) entre le 1er novembre 2019 et le 31 octobre 2020 pour déterminer le DdP, défini comme la période entre le début de l'incubation et le signal positif d'incubateurs automatisés. Résultats: Les chercheurs ont extrait 376 hémocultures positives provenant de 248 patients (d'un âge moyen de 6,27 ± 6,24 ans). De ce nombre, ils ont différencié 247 isolats, dont 90 (36,4 %) étaient des agents pathogènes confirmés ou probables (CP) (DdP médian de 12,75 heures) et 157 (63,6 %), des contaminants possibles ou probables (PP) (DdP médian de 24,08 heures). À chaque point temporel, le taux corrigé d'hémocultures positives était sensiblement plus élevé à l'égard des agents pathogènes CP que des contaminants PP (rapport de risque instantanés [RRI] : 1,80 [IC à 95 % 1,37,2,36]) et des nouveau-nés de 27 jours de vie ou moins que des enfants plus âgés (RRI 1,94 [IC à 95 % 1,19,3,17]). Au bout de 36 heures, la proportion de cultures positives était sensiblement plus élevée dans le groupe le plus jeune (27 jours de vie ou moins) que dans celui des enfants de trois à 11 ans, soit de 91,7 % (IC à 95 % 68,6 %, 97,8 %) par rapport à 58,2 % (IC à 95 % 46,91 %, 68,06 %). Conclusion: À tout âge, le DdP était sensiblement plus court, à l'égard des hémocultures contenant des agents pathogènes CP que des contaminants PP (RRI 1,80 [IC à 95 % 1,37,2,36]). Chez les nouveau-nés, 90 % des hémocultures sont positives au bout de 36 heures, ce qui appuie ce moment pour réévaluer la prise d'antimicrobiens empiriques. Le DdP était plus long chez les enfants âgés de plus de 12 mois, peut-être à cause d'autres facteurs comme le volume de l'hémoculture.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.

Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Image-guided abscess drainage in children with perforated appendicitis - can it wait?

BACKGROUND: Image-guided drainage is the management of choice for perforated appendicitis with intra-abdominal abscess/es. However, there is paucity of data regarding the optimal time for intervention in children. OBJECTIVE: The purpose of this study is to assess the relationship between the time from diagnosis of a drainable abscess to abscess drainage (delta time) and the clinical outcome in patients with complicated acute appendicitis. MATERIALS AND METHODS: This is an institutional review board (IRB)-approved retrospective study comprising 80 pediatric patients who had image-guided abscess drainage due to perforated acute appendicitis. Delta time was associated with clinical outcome including length of stay, catheter dwell time, need for additional interventions, and need for tissue plasminogen activator (t-PA). Gamma regression models were used to assess the adjusted effect of delta time on the "length of stay" and "catheter dwell time" using "volume of the largest abscess" and "number of collections" as severity indices. Logistic regression was used to assess the effect of delta time on the "need for the t-PA" and "need for additional interventions." RESULTS: Mean age (SD) was 10.2 (3.8) years. Mean time between diagnosis and intervention (delta time) was 1.5 (1.2) days. There was no evidence that delta time effects the length of stay, catheter dwell time, need for t-PA, and need for additional interventions (P > 0.05). However, there was an association between the number of collections and volume of the largest abscess with length of stay (P = 0.006; P = 0.058), catheter dwell time (P = 0.029; P < 0.001), and need for additional interventions (P = 0.029; P = 0.016). CONCLUSIONS: Our results suggest that time between diagnosis of an appendicitis associated abscess and intervention is not significantly associated with need for tPA, need for additional intervention, drain dwell time, or length of stay.

Cognitive Behavioral Therapy for Children With Headaches: Will an App Do the Trick?

Participants were enrolled into a pilot randomized-controlled 4-week trial comparing the efficacy and feasibility of app-based cognitive behavioral therapy (CBT) to a stretching program. Headache-related disability and quality of life were assessed using the Pediatric Migraine Disability Scale (PedMIDAS), Kidscree27, and Pediatric Quality of Life Inventory. Multivariable regression analysis were performed to assess the group effects in the presence of adherence and other covariates. Twenty participants completed the study. Adherence was significantly higher in the stretching than in the CBT app group (100% vs 54%, P < .034). When controlling for adherence and baseline scores, the stretching group showed greater reduction in PedMIDAS score (average: 29.2, P < .05) as compared to the CBT app group. However, in terms of the Quality-of-Life Indicators, pre- and postintervention raw scores were not significantly different between groups (P > .05). App-based CBT was not superior to a stretching program in reducing headache-related disability in a select population of pediatric headache patients. Future studies should assess if implementing features to the CBT app, like tailoring to pediatric age groups, would improve outcomes.

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

OBJECTIVES: Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. METHODS: A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. RESULTS: Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. CONCLUSIONS: Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications. KEY POINTS: Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.

Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

Prediction of preterm birth in nulliparous women using logistic regression and machine learning.

OBJECTIVE: To predict preterm birth in nulliparous women using logistic regression and machine learning. DESIGN: Population-based retrospective cohort. PARTICIPANTS: Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. METHODS: We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. RESULTS: During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. CONCLUSION: Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort.

OBJECTIVE: To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks. METHODS: Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks. RESULTS: During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5-73.6%) and 73.4% (68.6-78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5-72.3%) and 75.5% (72.3-78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7-75.1%) and 78.2% (95% CI: 75.8-80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes. CONCLUSIONS: Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.

Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach.

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease. Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships. Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events. Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data.

Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.