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Two new triterpenoid saponins, named spermacosides A-B (1 - 2), together with two known oleanane-type triterpenoid saponins, 3-O-ß-D-xylopyranosyl-(1â3)-ß-D-glucopyranosylbayogenin (3) and 3-O-ß-D-glucopyranosylbayogenin (4), were isolated from the ethyl acetate extract of Spermacoce ocymoides Burm.f. in a phytochemical investigation. Their chemical structures were elucidated by spectroscopic data analysis (1D and 2D NMR, and HR-ESI-MS), as well as comparison with reported data. All these compounds were evaluated for inhibiting nitric oxide production in lipopolysaccharide-stimulated RAW 264.7 cells. Among them, 1 showed a slight effect with an IC50 value of 108.65 ± 7.91 µM, and compounds 2-4 were inactive.
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Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.
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The purpose of this article is to systematically collate effort-reward imbalance (ERI) rates among health workers internationally and to assess gender differences. The effort-reward (ER) ratio ranges quite widely from 0.47 up to 1.32 and the ERI rate from 3.5% to 80.7%. Many studies suggested that health workers contribute more than they are rewarded, especially in Japan, Vietnam, Greece, and Germany-with ERI rates of 57.1%, 32.3%, 80.7%, and 22.8% to 27.6%, respectively. Institutions can utilize systems such as the new appraisal and reward system, which is based on performance rather than the traditional system, seniority, which creates a more competitive working climate and generates insecurity. Additionally, an increased workload and short stay patients are realities for workers in a health care environment, while the structure of human resources for health care remains inadequate. Gender differences within the ER ratio can be explained by the continued impact of traditional gender roles on attitudes and motivations that place more pressure to succeed for men rather than for women. This systematic review provides some valued evidence for public health strategies to improve the ER balance among health workers in general as well as between genders in particular. An innovative approach for managing human resources for health care is necessary to motivate and value contributions made by health workers.
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Capillary Zone Electrophoresis (CZE) was used to quantify ketoprofen in 50mg ketoprofen capsules and ketoprofen gel 2.5%. The results were compared to results of spectrophotometric UV-VIS method. Results: the accuracies of 2 methods were different but not significant. Results of CZE method were rather lower than spectrophotometric UV-VIS method because impurities were separated in electrophoresis process. The authors concluded that CZE could be used to quantify ketoprofen in products