Background Liver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment. Methods A total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set ( n = 286) and validation set ( n = 70). Multi-layer-perceptron model provided by pycox library was first used to construct the recurrence prediction model. Then tabular neural network (TabNet) that combines elements of deep learning and tabular data processing techniques was utilized to compare with Milan criteria and verify the performance of the model we proposed. Results Patients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, p < 0.05). In addition, our model showed better performance results with improved AUC, NRI and hazard ratio, proving the robustness of the model. Conclusion A prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.
This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.
Abstract Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score ( KDTN , i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score ( KDSN , i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug–gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in living organisms. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.
Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10(-12), P < 2 × 10(-4)) and a high codon adaptation index (P < 2 × 10(-16), P < 2 × 10(-8)), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.
BACKGROUND In epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large. OBJECTIVE Our study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis. METHODS An XGBoost model was trained and tested to classify “current depression” and “no lifetime depression” for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network. RESULTS The XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (<i>P</i><.05) and indirect (<i>P</i>≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes. CONCLUSIONS XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.
The microstructure and electrochemical properties of a Si 70 Mn 30 alloy prepared by melt spinning and arc melting have been investigated. Melt-spun ribbons and arc-melted ingots were fragmented and mixed with acetylene black (AB), polyvinylidene fluoride (PVDF), and N- methylpyrrolidinone by planetary milling for 2 h to characterize their electrochemical properties. The results showed that the microstructures of the melt-spun ribbons represented a fine eutectic constituent composed of a silicon phase with a thickness of approximately 50~70 nm and a Mn 4 Si 7 compound. The cyclic behavior of the rapidly solidified melt-spun Si 70 Mn 30 ribbons im- proved remarkably over that of the arc-melted specimens because of a finer microstructural scale; as the thickness of silicon crystal decreased 40 fold, the initial irreversible capacity was reduced by more than 2.5 times. The improved cycle performance of the melt-spun ribbons is a consequence of the small diffusion distance for the lithium-ion exchange.
Obesity is considered an important risk factor for the development of primary liver cancer. However, the association of weight change with risk of primary liver cancer is not well-defined, especially among middle-aged men with chronic liver disease. This population-based longitudinal study used data obtained by the Korean National Health Insurance Service-National Sample Cohort. The study population comprised 33,260 middle-aged men aged between 40 and 64 with chronic liver disease. The association of weight change with risk of primary liver cancer was evaluated using multivariable-adjusted Cox proportional hazards regression. All participants were followed up until primary liver cancer, death, or December 31, 2013, whichever came earliest. During 226,619 person-years of follow-up, 536 cases (1.6 %) of primary liver cancer were identified. Weight gain (change in body mass index greater than 2.0 kg/m2) was associated with higher risk of primary liver cancer (adjusted hazard ratio [aHR], 1.57; 95% confidence interval [CI] 1.04-2.36), whereas no significant association was found for weight loss (aHR, 0.90; 95% CI, 0.55-1.46) compared to weight stable group. Stratified analyses revealed that weight gain-associated higher risk of primary liver cancer was more notable among non-obese and non-drinking subgroups. Weight gain is associated with higher risk of primary liver cancer in among middle-aged men with chronic liver disease. Future studies are warranted to confirm whether weight management is beneficial in populations at high risk of primary liver cancer.
A COVID-19 vaccine BNT162b2 (Pfizer-BioNTech) has recently been authorized for adolescents in the US. However, the impact of adverse events on adolescents after vaccination has not been fully investigated. To assess the safety of the COVID-19 vaccine in adolescents, the incidence of adverse events (AEs) in adolescents and adults was compared after vaccination. We included 6304 adolescents (68.14 per 100,000 people) who reported adverse events using vaccine adverse event reporting system (VAERS) data from 10 May 2021 to 30 September 2021. The mean age was 13.6 ± 1.1 years and women (52.7%) outnumbered men. We analyzed severe and common adverse events in response to the COVID-19 vaccine among 6304 adolescents (68.14 per 100,000 people; 52% female; mean age, 13.6 ± 1.1 years). The risk of myocarditis or pericarditis among adolescents was significantly higher in men than in women (OR = 6.61, 95% CI = 4.43 to 9.88; p < 0.001), with a higher frequency after the second dose of the vaccine (OR = 8.52, 95% CI = 5.79 to 12.54; p < 0.001). In addition, severe adverse events such as multisystem inflammatory syndromes, where the incidence rate per 100,000 people was 0.11 (n = 10), and the relative risk was 244.3 (95% CI = 31.27 to 1908.38; p < 0.001), were significantly higher in adolescents than in adults. The risk of the inflammatory response to the COVID-19 vaccine, including myocarditis, pericarditis, or multisystem inflammatory syndromes, was significantly higher in men than in women, with a higher frequency in adolescents than in adults. The inflammation-related AEs may require close monitoring and management in adolescents.
Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0–30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0–120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11–2.54) increase in the risk of depressive episode. Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.
'%3e%3cg id='b'%3e%3cpath id='a' stroke='%23000' stroke-width='2' d='M-6-25H6m-12 3H6m-12 3H6'/%3e%3cuse xlink:href='%23a' y='44'/%3e%3c/g%3e%3cpath stroke='%23fff' d='M0 17v10'/%3e%3ccircle r='12' fill='%23cd2e3a'/%3e%3cpath fill='%230047a0' d='M0-12A6 6 0 0 0 0 0a6 6 0 0 1 0 12 12 12 0 0 1 0-24z'/%3e%3c/g%3e%3cg transform='rotate(-123.69)'%3e%3cuse xlink:href='%23b'/%3e%3cpath stroke='%23fff' d='M0-23.5v3M0 17v3.5m0 3v3'/%3e%3c/g%3e%3c/svg%3e)