SENCAR mice were used to determine the effects of the provitamin A compound beta-carotene on papilloma formation and the conversion of papillomas to carcinomas in a two-stage protocol with one application of the initiator 7,12-dimethylbenz[a]anthracene (DMBA, 20 micrograms) and 20 weekly applications of the promotor 12-O-tetradecanoylphorbol-13-acetate (TPA, 2 micrograms). A purified vitamin A-free diet was supplemented with beta-carotene at four levels (0.6, 6, 60 and 600 micrograms/g of diet) for female mice and two levels (60 and 600 micrograms/g) for male mice. Dietary supplementations of beta-carotene did not result in significant changes in body weight and survival of female and male mice. However, papillomas developed more rapidly and papilloma incidence (% mice with papillomas) reached its maximum (100%) sooner in male mice fed 600 micrograms of beta-carotene/g of diet than those fed 60 micrograms/g. There were smaller differences in papilloma incidence among the dietary groups in female mice, but the papilloma incidence again reached 100% sooner in mice fed 600 micrograms of beta-carotene/g of diet. Female and male mice fed 600 micrograms of beta-carotene/g of diet had significantly higher papilloma yields (average number of papillomas/mouse) than other dietary groups and a very low percentage of these papillomas converted to carcinomas in these mice. Thus, beta-carotene at 600 micrograms/g inhibited the conversion of papillomas to carcinomas in both sexes. In addition, papilloma yields were higher in female mice and these papillomas regressed more quickly than those in the corresponding groups of male mice. In conclusion, dietary beta-carotene caused differential effects on papilloma and carcinoma yields and sex-dependent differences in papilloma formation in female and male SENCAR mice treated with DMBA and TPA in a two-stage carcinogenesis protocol.

We have previously reported that high dietary retinoic acid (RA; 30 micrograms/g diet) inhibits carcinoma formation in a two-stage skin carcinogenesis protocol, using 7,12-dimethylbenz[a]anthracene (DMBA) as the initiator and 12-O-tetradecanoyl phorbol-13-acetate (TPA) as the tumor-promoter in female SENCAR mice. We next asked whether switching the diets from high to control levels of RA and vice versa would influence carcinoma formation. Mice at 3 weeks of age were initiated with DMBA (20 micrograms) once, followed by 20 weekly applications of TPA (2 micrograms). At 3 weeks of age mice were weaned onto a diet containing either 3 (control) or 30 (high) micrograms RA/g diet. Half of the mice from either dietary group were switched to the other diet at 20 weeks of age, when papilloma formation was at its peak. These four groups are designated RA 3 micrograms, RA 30 micrograms, RA 3/30 micrograms and RA 30/3 micrograms groups. As previously found, papilloma formation (including incidence and yield) was not significantly affected by dietary treatment. However, high dietary RA inhibited carcinoma formation; specifically cumulative carcinoma incidence (18.5-23.1% versus 50%) and yield (0.19-0.23 versus 0.68) were significantly lower (P < 0.05) in the high dietary RA treatment groups than the RA 3 micrograms control group, as was the carcinoma conversion efficiency (2.1-3.8% versus 9.4%). The beneficial effect on carcinoma formation was still evident when excess RA was given late during the carcinogenesis process (i.e. the RA 3/30 micrograms group). Moreover, a residual effect of excess RA was also seen after the dietary RA was switched to the control level at 20 weeks of age, when papilloma yield was highest (i.e. the RA 30/3 micrograms group). It is therefore concluded that the chemopreventive effect of high dietary RA on skin carcinogenesis induced by a two-stage carcinogenesis protocol with DMBA and TPA resides mainly at the step of conversion from benign papillomas to malignant carcinomas.

Previously we have shown that dietary retinoids are essential for papilloma formation induced by either an initiation-promotion or a complete skin carcinogenesis protocol. The present study was conducted to further determine the effect of dietary retinoic acid (RA) on papilloma formation and the conversion of papillomas to carcinomas. Skin tumors were induced in 3 week old female SENCAR mice by an initiation-promotion protocol with one application of 20 micrograms of 7,12-dimethylbenz[a]anthracene (DMBA), followed by 20 weekly applications of 2 micrograms of 12-O-tetradecanoylphorbol-13-acetate (TPA). Mice were fed RA at one of the three doses: 0.3 (nutritionally marginal dose), 3 (near physiological) and 30 (pharmacological) micrograms/g of diet. Mice fed 30 micrograms of RA/g of diet had the same survival rate as the other two groups despite a lower body weight and all three groups had similar papilloma incidence, which reached 100% at age 18 weeks. Mice fed 3 micrograms of RA/g of diet had the highest papilloma yield (approximately 14 papillomas/mouse) of all groups and it peaked between weeks 18 and 38 of age. These papillomas later regressed such that mice from all three groups had about the same papilloma yield at week 44 of age. Mice fed 30 micrograms of RA/g of diet failed to develop any visible carcinoma, while mice fed 0.3 or 3 micrograms/g showed 1.9% conversion of papillomas to carcinomas. Therefore, dietary RA at 30 micrograms/g of diet inhibited the conversion of papillomas to carcinomas without affecting papilloma incidence. In addition, dietary RA at 30 and 0.3 micrograms/g of diet lowered papilloma yield.

Background Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. Objective We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. Methods A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. Results The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. Conclusions Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.

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