Background: Ventilation with the noble gas argon (Ar) has shown neuroprotective and cardioprotective properties in different in vitro and in vivo models. Hence, the neuroprotective effects of Ar were investigated in a severe, preclinically relevant porcine model of cardiac arrest. Methods and Results: Cardiac arrest was ischemically induced in 36 pigs and left untreated for 12 minutes before starting cardiopulmonary resuscitation. Animals were randomized to 4‐hour post‐resuscitation ventilation with: 70% nitrogen–30% oxygen (control); 50% Ar–20% nitrogen–30% oxygen (Ar 50%); and 70% Ar–30% oxygen (Ar 70%). Hemodynamic parameters and myocardial function were monitored and serial blood samples taken. Pigs were observed up to 96 hours for survival and neurological recovery. Heart and brain were harvested for histopathology. Ten animals in each group were successfully resuscitated. Ninety‐six‐hour survival was 60%, 70%, and 90%, for the control, Ar 50%, and Ar 70% groups, respectively. In the Ar 50% and Ar 70% groups, 60% and 80%, respectively, achieved good neurological recovery, in contrast to only 30% in the control group ( P <0.0001). Histology showed less neuronal degeneration in the cortex ( P <0.05) but not in the hippocampus, and less reactive microglia activation in the hippocampus ( P =0.007), after Ar compared with control treatment. A lower increase in circulating biomarkers of brain injury, together with less kynurenine pathway activation ( P <0.05), were present in Ar‐treated animals compared with controls. Ar 70% pigs also had complete left ventricular function recovery and smaller infarct and cardiac troponin release ( P <0.01). Conclusions: Post‐resuscitation ventilation with Ar significantly improves neurologic recovery and ameliorates brain injury after cardiac arrest with long no‐flow duration. Benefits are greater after Ar 70% than Ar 50%.

This study estimates the accuracy of the focused lung ultrasound (FLUS) compared with systematic thoracic ultrasonography (TUS) as the reference test for diagnosing pneumonia in pre- and postweaned dairy calves. One hundred thirty-five Holstein Friesian calves, aged between 1 to 6 mo were enrolled and were kept in the same pen with one or more animals showing signs of bovine respiratory disease complex (BRDC). One operator performed FLUS on each calf, and then a second, blinded operator performed TUS on the same calf. For the FLUS, we only scanned the lung lobes that are most frequently affected during BRDC and are thus easier to detect, such as the caudal aspect of the cranial lobe of the left lung (fifth and fourth left intercostal spaces; ICS), the middle lobe of the right lung (fifth right ICS), and the caudal aspect of the cranial lobe of the right lung (fourth right ICS). Pneumonia was diagnosed when a calf had a minimum of one small lobular lung lesion that was at least 1 cm deep within a normally aerated lobe (TUS score of ≥2). Diagnostic accuracy indexes of the FLUS were calculated using TUS as the gold standard. The McNemar test was performed to evaluate the differences between the 2 techniques. In addition, an intertest agreement was assessed using the weighted kappa test. A total of 76 out of 135 calves had a TUS score of ≥2 and were therefore considered to be affected by BRDC. The FLUS had a sensitivity of 81.6% (95% CI = 71.0-89.5%), specificity = 100% (95% CI = 93.9-100%), positive predictive value was 100%, negative predictive value was 96.6% (95% CI = 94.7---97.9%), and accuracy was 97% (95% CI = 92.6-99.2%). The McNemar test highlighted a difference of 10.3% between the FLUS and TUS. The agreement between the TUS and FLUS was substantial (weighted kappa test 0.78). Although FLUS shows some limitations in diagnosing lung lesions associated with BRDC compared with the systematic approach, this study shows that the focused method could be used as an additional tool for evaluating consolidation, especially when examining a large number of postweaned dairy calves.

Background: Although thoracic auscultation (AUSC) in calves is quick and easy to perform, the definition of lung sounds is highly variable and leads to poor to moderate accuracy in diagnosing bronchopneumonia (BP). Hypothesis/Objectives: Evaluate the diagnostic accuracy of an AUSC scoring system based on a standard lung sound nomenclature at different cut-off values, accounting for the absence of a gold standard test for BP diagnosis. Animals: Three hundred thirty-one calves. Methods: We considered the following pathological lung sounds: increased breath sounds (score 1), wheezes and crackles (score 2), increased bronchial sounds (score 3), and pleural friction rubs (score 4). Thoracic auscultation was categorized as AUSC1 (positive calves for scores ≥1), AUSC2 (positive calves for scores ≥2), and AUSC3 (positive calves for scores ≥3). The accuracy of AUSC categorizations was determined using 3 imperfect diagnostic tests with a Bayesian latent class model and sensitivity analysis (informative vs weakly informative vs noninformative priors and with vs without covariance between ultrasound and clinical scoring). Results: Based on the priors used, the sensitivity (95% Bayesian confidence interval [BCI]) of AUSC1 ranged from 0.89 (0.80-0.97) to 0.95 (0.86-0.99), with a specificity (95% BCI) of 0.54 (0.45-0.71) to 0.60 (0.47-0.94). Removing increased breath sounds from the categorizations resulted in increased specificity (ranging between 0.97 [0.93-0.99] and 0.98 [0.94-0.99] for AUSC3) at the cost of decreased sensitivity (0.66 [0.54-0.78] to 0.81 [0.65-0.97]). Conclusions and Clinical Importance: A standardized definition of lung sounds improved AUSC accuracy for BP diagnosis in calves.

Clinical scores are commonly used for cattle. They generally contain a mix of categorical and numerical variables that need to be assessed by scorers, such as farmers, animal caretakers, scientists, and veterinarians. This article examines the key concepts that need to be accounted for when developing the test for optimal outcomes. First, the target condition or construct that the scale is supposed to measure should be defined, and if possible, an adequate proxy used for classification should be determined. Then, items (e.g., clinical signs) of interest that are either caused by the target condition (reflective items) or that caused the target condition (formative items) are listed, and reliable items (inter and intra-rater reliability) are kept for the next step. A model is then developed to determine the relative weight of the items associated with the target condition. A scale is then built after validating the model and determining the optimal threshold in terms of sensitivity (ability to detect the target condition) and specificity (ability to detect the absence of the target condition). Its robustness to various scenarios of the target condition prevalence and the impact of the relative cost of false negatives to false positives can also be assessed to tailor the scale used based on specific application conditions.