RESUMO
Treatment of cancer often requires the use of adjuvant chemotherapy (ACT). In real clinical practice, numerous patients suffer from severe toxicity and reduced quality of life (QoL). Hence, there is a need to maintain QoL and to reduce therapy toxicity to comply with recommended chemotherapy (CT) regimens. The present study focused on the effects of the multi-component nutritional supplement Oncoxin (ONCX) on QoL and CT-induced toxicity in patients undergoing ACT. A total of 133 patients aged 50-70 years with gastric cancer IIB-IIIC or non-small cell lung cancer IIB-IIIA were enrolled in the present study: 84 received ONCX, and 49 were included in the control arm and received CT only. It was identified that after 2 weeks of treatment the patients receiving ONCX exhibited clinically meaningful improvement of QoL (measured by Edmonton Symptom Assessment System Questionnaire) compared with those in the control group (odds ratio, 2.07; 95% CI, 1.00-4.29). By the end of a 3 week-period, the albumin level was higher in patients of the ONCX group compared with those in the control group (mean, 38.1; 95% CI, 37.1-39.1 g/l; vs. mean, 35.5; 95% CI, 33.9-37.0; P=0.03; respectively). Furthermore, the use of ONCX substantively reduced the hepatic toxicity of ACT. The present prospective real clinical setting study revealed positive effects of ONCX on QoL and ACT toxicity. The present study was retrospectively registered under the study registration number NCT03550482 at ClinicalTrials.gov (June 8, 2018).
RESUMO
Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the "aging clocks" varying in biological relevance, ease of use, cost, actionability, interpretability, and applications. Here we present and investigate a novel non-invasive class of visual photographic biomarkers of aging. We developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation.