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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.

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.

Although postoperative delirium is a common complication in older patients, few papers have described risk factors after of spinal surgery. The purpose of this study was to analyze various perioperative risk factors for delirium after spinal surgery in older patients. This study was performed on retrospective data collection with prospective design. We analyzed 138 patients over 65 years of age who underwent spinal surgery. Preoperative factors were cognitive function (Mini-Mental State Examination-Korean (MMSE-K) and the Korean version of the Delirium Rating Scale-Revised-98 (K-DRS 98)), age, sex, type of admission, American Society of Anesthesiologist classification, metabolic equivalents, laboratory findings, visual analog scale, and Oswestry Disability Index. Intraoperative factors were operation time, blood loss, and type of procedure. Postoperative factors were blood transfusion and type of postoperative pain control. Postoperative delirium developed in 25 patients (18.16%). Patients were divided into two groups: Group with delirium (group A) and group without delirium (group B). MMSE-K scores in Group A were significantly lower than in Group B (p < 0.001). K-DRS 98 scores were significantly higher in Group A than Group B (p < 0.001). The operation time was longer in Group A than Group B (p = 0.059). On multivariate regression analysis, the odds ratio of K-DRS 98 was 2.43 (p = 0.010). After correction for the interaction between age and MMSE-K, patients younger than 73 years old had a significantly lower incidence of delirium with higher MMSE-K score (p = 0.0014). Older age, low level of preoperative cognitive function, long duration of surgery, and transfusion were important risk factors of postoperative delirium after spinal surgery. It is important to recognize perioperative risk factors and manage appropriately.

Recently, functional connections between S-adenosylhomocysteine hydrolase (AHCY) activity and cancer have been reported. As the properties of AHCY include the hydrolysis of S-adenosylhomocysteine and maintenance of the cellular methylation potential, the connection between AHCY and cancer is not obvious. The mechanisms by which AHCY influences the cell cycle or cell proliferation have not yet been confirmed. To elucidate AHCY-driven cancer-specific mechanisms, we pursued a multi-omics approach to investigate the effect of AHCY-knockdown on hepatocellular carcinoma cells. Here, we show that reduced AHCY activity causes adenosine depletion with activation of the DNA damage response (DDR), leading to cell cycle arrest, a decreased proliferation rate and DNA damage. The underlying mechanism behind these effects might be applicable to cancer types that have either significant levels of endogenous AHCY and/or are dependent on high concentrations of adenosine in their microenvironments. Thus, adenosine monitoring might be used as a preventive measure in liver disease, whereas induced adenosine depletion might be the desired approach for provoking the DDR in diagnosed cancer, thus opening new avenues for targeted therapy. Additionally, including AHCY in mutational screens as a potential risk factor may be a beneficial preventive measure.

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.

Early tumor recurrence in hepatocellular carcinoma (HCC) remains a challenging area, as the mechanisms involved are not fully understood. While microvascular invasion is linked to early recurrence, established biomarkers for diagnosis and prognostication are lacking. In this study, our objective was to identify DNA methylation sites that can predict the outcomes of liver cancer patients and elucidate the molecular mechanisms driving HCC aggressiveness. Using DNA methylome data from HCC patient samples from the CGRC and TCGA databases, we pinpointed hypermethylated CpG sites in HCC. Our analysis revealed that cg02746869 acts as a crucial regulatory site for VIM-AS1 (vimentin antisense RNA1), a 1.8 kb long noncoding RNA. RNA sequencing of HCC cells with manipulated VIM-AS1 expression revealed EPHA3 as a pathogenic target of VIM-AS1 , which performs an oncogenic function in HCC. Hypermethylation-induced suppression of VIM-AS1 significantly impacted HCC cell dynamics, particularly impairing motility and invasiveness. Mechanistically, reduced VIM-AS1 expression stabilized EPHA3 mRNA by enhancing the binding of IGF2BP1 to EPHA3 mRNA, leading to increased expression of EPHA3 mRNA and the promotion of HCC progression. In vivo experiments further confirmed that the VIM-AS1‒EPHA3 axis controlled tumor growth and the tumor microenvironment in HCC. These findings suggest that the downregulation of VIM-AS1 due to hypermethylation at cg02746869 increased EPHA3 mRNA expression via a m6A-dependent mechanism to increase HCC aggressiveness. Hypermethylation drives aggressiveness in liver cancer Despite advancements in treatment, cancer remains a life-threatening disease that can recur (come back) and metastasize. Researchers found a knowledge gap in understanding how DNA methylation affects cancer progression. Researchers conducted an experiment to identify DNA methylation markers related to liver cancer prognosis. They used human liver cancer cell lines and analyzed DNA methylation and gene expression. The researchers discovered that hypermethylation of a specific DNA region in the VIM-AS1 gene is linked to poor prognosis in liver cancer. They concluded that DNA methylation affects gene expression and cancer cell behavior. This finding could lead to new diagnostic and treatment strategies for liver cancer. Future research may explore how to target these epigenetic changes for better cancer therapies. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Typically, eddy currents induced in a laminated core are counted as losses, and only their magnitude is considered. On the other hand, as the operating frequency increases, current sensors using laminated cores need to calculate the eddy currents directly to compensate for the phase errors caused by them. A surrogate model based on a deep learning algorithm that uses the output of finite element analysis for training was proposed to compensate for the phase error caused by eddy currents. The proposed method is expected to have higher precision than the existing first-order interpolation function. The proposed method was applied to inverter control and showed superior performance than the existing methods.

Equalized rectangular extension and flexion gaps are considered desirable to ensure proper kinematics in total knee arthroplasty (TKA). We compared soft tissue balancing in TKAs performed using navigation-assisted gap-balancing (60 knees) and conventional measured resection (56 knees). The outlier of soft tissue balancing was defined as a gap difference >3 mm between the medial and lateral sides in 90° flexion and extension. Medial or lateral outliers in extension or flexion were observed in 12% (7 of 60) navigation TKAs and 25% (14 of 56) conventional TKAs ( p  = 0.028). There were more outliers in flexion–extension gap difference on the medial side in the conventional (23%) than in the navigation-assisted (5%) group ( p  = 0.025). However, the proportion of flexion gap difference, extension gap difference, and lateral gap difference outliers did not differ significantly between the two groups ( n.s. ). Additionally, clinicoradiologic outcomes were similar for the two groups except for the postoperative mechanical axis outlier ( p  = 0.012). Navigation-assisted soft tissue balancing in TKA reduced not only the postoperative alignment outlier, but also the medial gap difference and achieved a more rectangular flexion and extension gap compared with conventional TKA.

Fabricating inorganic–organic hybrid perovskite solar cells (PSCs) on plastic substrates broadens their scope for implementation in real systems by imparting portability, conformability and allowing high-throughput production, which is necessary for lowering costs. Here we report a new route to prepare highly dispersed Zn 2 SnO 4 (ZSO) nanoparticles at low-temperature (<100 °C) for the development of high-performance flexible PSCs. The introduction of the ZSO film significantly improves transmittance of flexible polyethylene naphthalate/indium-doped tin oxide (PEN/ITO)-coated substrate from ∼75 to ∼90% over the entire range of wavelengths. The best performing flexible PSC, based on the ZSO and CH 3 NH 3 PbI 3 layer, exhibits steady-state power conversion efficiency (PCE) of 14.85% under AM 1.5G 100 mW·cm −2 illumination. This renders ZSO a promising candidate as electron-conducting electrode for the highly efficient flexible PSC applications. There has been impressive progress in the development of perovskite solar cells in recent years, but the best performing systems tend to be fabricated on glass surfaces. Here, the authors present a cell built on a polymer substrate, allowing flexibility whilst maintaining high efficiency.

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.