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Abstract Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.

(1) The purpose of this study was to investigate the effect of whey protein supplementation under dietary control on improvements in muscle mass and function following resistance exercise training. (2) Thirty-two men were randomly assigned to a whey protein supplementation group taking whey protein isolate (PSG, n = 17) and a placebo group (CON, n = 15). Participants were provided with three meals per day corresponding to the estimated individual daily energy intake. The supervised resistance exercise program was conducted 60 min per day, six days per week, for four weeks. (3) Post-intervention, there was a significant interaction between groups in terms of muscle mass increase (p = 0.033, η2 = 0.14), with a greater increase in the PSG. There were also significant interactions between the groups and increases in peak torque of the dominant knee flexors (p = 0.048, η2 = 0.12), dominant shoulder extensors, and non-dominant shoulder extensors (p = 0.028, η2 = 0.15; p = 0.015, η2 = 0.18), and the total work of the dominant knee and shoulder extensors (p = 0.012, η2 = 0.19; p = 0.013, η2 = 0.19), with greater increases in the PSG. (4) These results suggest that whey protein supplementation enhances resistance exercise-induced increase in muscle mass and overall muscular strength and endurance, independent of dietary influence.

House load operation (HLO) occurs when the generator supplies power to the house load without triggering reactor trips during grid disturbances. In Korea, the HLO capability of optimized power reactor 1000 (OPR1000) plants has prevented several reactor trips.Operational experiences demonstrate the difference in the reactor trip incidence due to grid disturbances between OPR1000 plants and Westinghouse plants in Korea, attributable to the availability of the HLO capability. However, probabilistic safety assessments (PSAs) for OPR1000 plants have not considered their specific design features in the initiating event analyses.In an at-power PSA, the HLO capability can affect the initiating event frequencies of general transients (GTRN) and loss of offsite power (LOOP), resulting from transients within the grid system. The initiating event frequencies of GTRN and LOOP for an OPR1000 plant are reduced by 17.7% and 78.7%, respectively, compared to the Korean industry-average initiating event frequencies, and its core damage frequency from internal events is reduced by 15.2%. The explicit consideration of the HLO capability in initiating event analyses makes significant changes in the risk contributions of the initiating events. Consequently, for more realistic at-power PSAs in Korea, we recommend incorporating plant-specific HLO-related design features when estimating initiating event frequencies.

Abstract Background Unnecessary costs and complications can be reduced by increasing hemoglobin (Hb) levels and minimizing blood transfusions in patients who underwent total knee arthroplasty (TKA). This study aimed to determine the effects of intravenous iron monotherapy before TKA on preoperative iron deficient anemia and postoperative transfusion rates. Methods This prospective cohort study included 45 patients scheduled for TKA in the experimental group (Group I) and 221 patients who underwent TKA in 2015 and 2018 in the control group (Group C). One thousand milligrams of ferric carboxymaltose was administrated 1 month before TKA in group I. Intergroup comparison of the rate and total volume of transfusion, perioperative changes in Hb and analysis of iron metabolism variables in group I were performed. Subgroup analysis of Group I was conducted according to the response to iron monotherapy. Results Although Hb levels increased after intravenous iron monotherapy in Group I, postoperative transfusion rates in Groups I and C were 17.8% and 18.6%, respectively, without significant intergroup differences in the rate and total units of transfusion. Ferritin level and transferrin saturation were corrected in both subgroups of Group I. Only 17 patients (37.78%) showed response to iron monotherapy, with an Hb increase of 1.0 g/dL or more. Subgroup analysis showed lower proportions of coexisting chronic diseases, including chronic kidney disease in responders. Conclusion IV iron monotherapy was shown to be insufficient in successfully treating preoperative iron-deficient anemia to reduce postoperative allogenic blood transfusion in patients who underwent TKA. As preoperative anemia should be managed due to the high rates of postoperative transfusion for this surgery, clinicians should consider the complex interplay among the causal factors of anemia, apart from ID, in patients with preoperative anemia who are scheduled for TKA.

Abstract Background Only limited data are available regarding postural stability between anterior cruciate ligament (ACL)-injured patients with medial meniscus (MM) tear and those with lateral meniscus (LM) tear. The purpose of this study was to compare preoperative postural stability for both involved and uninvolved knees in ACL rupture combined with MM and LM tears. It was hypothesized that there would be a significant difference in postural stability between these two groups. Methods Ninety-three ACL-injured patients (53 combined with MM tears vs. 40 combined with LM tears) were included. Static and dynamic postural stability were evaluated with the overall stability index (OSI), anterior–posterior stability index (APSI), and medial–lateral stability index (MLSI) using stabilometry. Knee muscle strength was evaluated using an isokinetic testing device. Results In the static postural stability test, none of the stability indices showed significant differences between the two groups for both knees (p > 0.05). In the dynamic postural stability test for involved side knees, the OSI and APSI were significantly higher in the LM tear group compared to the MM tear group (OSI: 2.0 ± 0.8 vs. 1.6 ± 0.5, p = 0.001; APSI: 1.5 ± 0.6 vs. 1.3 ± 0.5, p = 0.023), but not the MLSI (p > 0.05). In the static and dynamic postural stability tests in each group, there were no significant differences between the involved and uninvolved side knees (p > 0.05). There was no significant difference in the knee muscle strength between the two groups (p > 0.05). All postural stability showed no significant correlation with knee muscle strength (p > 0.05). Conclusion Dynamic postural stability was poorer in patients with ACL rupture combined with LM tear than in those with MM tear. Therefore, close monitoring for postural stability would be necessary during preoperative and postoperative rehabilitation, especially for patients with ACL rupture combined with LM tear. Level of evidence: Level III

This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019–May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for identifying the main factors of the dependent variables and evaluating their associations with these predictors, including blood transfusion. Based on the results of this study, blood transfusion had a positive association with all-cause mortality. The proportions of red blood cell, platelet, fresh frozen plasma, and cryoprecipitate transfusions were significantly higher in those with death than in those without death (p-values < 0.01). Likewise, the top ten factors of all-cause mortality based on random forest variable importance were the Charlson Comorbidity Index (53.54), age (45.68), socioeconomic status (45.65), red blood cell transfusion (27.08), dementia (19.27), antiplatelet (16.81), gender (14.60), diabetes mellitus (13.00), liver disease (11.19) and platelet transfusion (10.11). The top ten predictors of the hospitalization period were the Charlson Comorbidity Index, socioeconomic status, dementia, age, gender, hemiplegia, antiplatelet, diabetes mellitus, liver disease, and cardiovascular disease. In conclusion, comorbidity, red blood cell transfusion, and platelet transfusion were the major factors of all-cause mortality based on machine learning analysis. The effective management of these predictors is needed in COVID-19 patients.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.

Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.

Cells sense mechanical force and their physical environment to change their fate and behavior through mechanotransduction. The mechanotransduction is, by translating mechanical forces and deformations into biochemical intracellular signaling, changing gene expressions and adjusting cellular and extracellular structure. Given that mechanosensitive signaling modulates cellular functions as diverse as proliferation and differentiation, such signaling is crucial for normal tissue development and homeostasis. Here we review current evidence for the mechanical regulation of cell differentiation, specifically in musculoskeletal tissues, such as skeletal muscle, fat, bone, and cartilage.

Tamoxifen resistance is often observed in the majority of estrogen receptor-positive breast cancers and it remains as a serious clinical problem in breast cancer management. Increased aerobic glycolysis has been proposed as one of the mechanisms for acquired resistance to chemotherapeutic agents in breast cancer cells such as adriamycin. Herein, we report that the glycolysis rates in LCC2 and LCC9--tamoxifen-resistant human breast cancer cell lines derived from MCF7--are higher than those in MCF7S, which is the parent MCF7 subline. Inhibition of key glycolytic enzyme such as hexokinase-2 resulted in cell growth retardation at higher degree in LCC2 and LCC9 than that in MCF7S. This implies that increased aerobic glycolysis even under O2-rich conditions, a phenomenon known as the Warburg effect, is closely associated with tamoxifen resistance. We found that HIF-1α is activated via an Akt/mTOR signaling pathway in LCC2 and LCC9 cells without hypoxic condition. Importantly, specific inhibition of hexokinase-2 suppressed the activity of Akt/mTOR/HIF-1α axis in LCC2 and LCC9 cells. In addition, the phosphorylated AMPK which is a negative regulator of mTOR was decreased in LCC2 and LCC9 cells compared to MCF7S. Interestingly, either the inhibition of mTOR activity or increase in AMPK activity induced a reduction in lactate accumulation and cell survival in the LCC2 and LCC9 cells. Taken together, our data provide evidence that development of tamoxifen resistance may be driven by HIF-1α hyperactivation via modulation of Akt/mTOR and/or AMPK signaling pathways. Therefore, we suggest that the HIF-1α hyperactivation is a critical marker of increased aerobic glycolysis in accordance with tamoxifen resistance and thus restoration of aerobic glycolysis may be novel therapeutic target for treatment of tamoxifen-resistant breast cancer.