Surgical resection and radiofrequency ablation initiate cancer in cytokeratin-19(+)- liver cells deficient for p53 and Rb

The long term prognosis of liver cancer patients remains unsatisfactory because of cancer recurrence after surgical interventions, particularly in patients with viral infections. Since hepatitis B and C viral proteins lead to inactivation of the tumor suppressors p53 and Retinoblastoma (Rb), we hypothesize that surgery in the context of p53/Rb inactivation initiate de novo tumorigenesis. We, therefore, generated transgenic mice with hepatocyte and cholangiocyte/liver progenitor cell (LPC)-specific deletion of p53 and Rb, by interbreeding conditional p53/Rb knockout mice with either Albumin-cre or Cytokeratin-19-cre transgenic mice. We show that liver cancer develops at the necrotic injury site after surgical resection or radiofrequency ablation in p53/Rb deficient livers. Cancer initiation occurs as a result of specific migration, expansion and transformation of cytokeratin-19+-liver (CK-19+) cells. At the injury site migrating CK-19+ cells formed small bile ducts and adjacent cells strongly expressed the transforming growth factor β (TGFβ). Isolated cytokeratin-19+ cells deficient for p53/Rb were resistant against hypoxia and TGFβ-mediated growth inhibition. CK-19+ specific deletion of p53/Rb verified that carcinomas at the injury site originates from cholangiocytes or liver progenitor cells. These findings suggest that human liver patients with hepatitis B and C viral infection or with mutations for p53 and Rb are at high risk to develop tumors at the surgical intervention site

Pressure measurement in the reticulum to detect different behaviors of healthy cows

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and 'other', as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows

Remodeling in the AV block dog is essential for tolerating moderate treadmill activity

Background: A preclinical model standardized at different remodeling stages after AV block induction in awake state is suitable for the evaluation of improved cardiac devices. We studied exercise-induced cardiorespiratory parameters at three different timepoints after inducing AV block in dogs. Methods: Mongrel dogs (n = 12) were placed on a treadmill with a 10% incline and performed a moderate exercise protocol (10-minute run at 6 km/h). Dogs ran at sinus rhythm (SR), at two days (AVB2d, initiation of remodeling), three weeks (CAVB3) and six weeks (CAVB6, completed remodeling) after AV block. Results: All dogs completed the exercise protocol at SR, CAVB3 and CAVB6, while 6/12 dogs at AVB2d failed to complete the exercise protocol. The atrial rate was higher at all AV block timepoints (126 ± 20 to 141 ± 19 bpm at rest and 221 ± 10 to 231 ± 13 bpm during exercise) compared to SR (100 ± 29 bpm at rest and 162 ± 28 bpm during exercise, p < 0.05). Upon exercise, stroke volume increased from 66 ± 15 ml at SR, to 96 ± 21 ml at AVB2d (p < 0.05), 91 ± 13 ml at CAVB3 (p < 0.05) and 85 ± 24 ml at CAVB6 but failed to compensate for the AV block-induced bradycardia. Therefore, cardiac output was lower after AV block compared to SR. Exercising dogs at AVB2d showed most arrhythmic events, lowest VO2, and signs of desaturation and acidification in venous blood. Conclusion: Dogs with limited remodeling after AV block have a reduced exercise tolerance, which is reflected in changes in cardiorespiratory parameters

Pressure measurement in the reticulum to detect different behaviors of healthy cows

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and 'other', as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows