(1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure. The cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values below 30% as COVID-19 Low Risk. The statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) Conclusions: The deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk.
The aim of the present study was to analyze seminal quality of young bulls subjected to different frequencies of gossypol supplementation. Forty-eight Nellore bulls, with 19 months of age and weighing 357.8 ± 7.2 kg, were used in this study. Animals were fed with 10.5 kg of standard supplement containing free-gossypol from whole cottonseed (WCS) at the following frequency: 3x/week (G3x), 5x/week (G5x) or 7x/week (G7x - Control). Additionally, a negative control was provided, and the treated animals received only mineral supplement (MM) ad libtum. The experiment lasted for 84 days and semen was collected at the beginning and at the end for analysis and cryopreservation. Fresh semen was used for initial analysis and plasma membrane integrity and sperm morphology were also determined. General motility using computer assisted sperm analysis (CASA), plasma and acrosomal membranes integrity, mitochondrial activity, and induced oxidative stress were assessed in post-thawed semen. The study design was completely randomized. Parametric data were analyzed by ANOVA and non-parametric data by the Wilcoxon test, using the statistical program SAS. Level of significance was set at 5%. Supplementation with WCS, regardless the frequency, increased total (P = .009) and head (P = .005) defects in comparison to animals receiving only forage and mineral supplement. Infrequent supplementation, particularly 5 times in the week (G5X), increased head (P = .026) and midpiece (P = .014) abnormalities. Sperm motility in fresh semen was lower in animals that received daily supplementation than those supplemented on alternate days (P = .021). Additionally, animals supplemented daily showed lower percentage of spermatozoa with intact acrosome compared to those supplemented on alternate days (P = .005). Thus, regardless the frequency of supplementation, free-gossypol supplementation affects sperm quality. Although the amount of free gossypol supplied weekly was the same among treatments, daily supplementation compromised sperm kinetics, differently from infrequent supplementation that led to sperm defects developed during spermatogenesis.
Background: In 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China. The most serious clinical entity associated with SARS-CoV-2 is a severe interstitial pneumonia that can lead to acute respiratory distress. Radiation pneumonitis (RP) is lung radiation toxicity. RP and SARS-CoV-2 interstitial pneumonia show overlapping clinical features. The aim of this study is to test the performance of a deep learning algorithm in discriminating RP from COVID-19 pneumonia. Methods: Seventy patients were analysed, 34 affected by COVID-19 pneumonia and 36 by RP. The CT images were analyzed by InferReadTM CT Lung (COVID-19) ®, an Artificial Intelligence algorithm based on a novel deep convolutional neural network structure. In a previous publication the cut-off value of the estimated risk probability of COVID-19 was set at levels higher than 30% ("COVID19 High Risk"), as the percentage of COVID-19 confirmed patients above this cut-off value was higher than 95%. Values of estimated risk probability below 30% were classified as "COVID19 Low Risk". Statistical analysis included Mann Whitney U test (significance threshold at p < 0.05) and ROC curve with fitting performed by using the maximum likelihood fit of a binormal model.
Results:The algorithm classified as "COVID19 Low Risk" 66.7% of patients presenting RP. All RP classified as "COVID19 High Risk" were ≥G3. The algorithm showed good accuracy in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, AUC = 0.72). This accuracy increased when cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84. The total lung volume (p = 0.001), the left lower lobe ( p < 0.001) and the right lower lobe (p < 0.001) involvement resulted increased in COVID-19 group compared to RP. In patients pretreated with radiotherapy and presenting diffuse pneumonitis classified by AI as COVID19 High Risk a combination of dosimetric factors may help to identify RP (PPV increased from 60% to 99.8%). Conclusions: Deep-learning algorithm can help to discriminate RP from COVID-19 pneumonia, classifying most RP as Low-risk COVID19. In patients classified as high risk, treated with radiation therapy also dosimetric factors should be taken into account.
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