Genetic copy number variants in myocardial infarction patients with hyperlipidemia

<p>Abstract</p> <p>Background</p> <p>Cardiovascular disease is the chief cause of death in Taiwan and many countries, of which myocardial infarction (MI) is the most serious condition. Hyperlipidemia appears to be a significant cause of myocardial infarction, because it causes atherosclerosis directly. In recent years, copy number variation (CNV) has been analyzed in genomewide association studies of complex diseases. In this study, CNV was analyzed in blood samples and SNP arrays from 31 myocardial infarction patients with hyperlipidemia.</p> <p>Results</p> <p>We identified seven CNV regions that were associated significantly with hyperlipidemia and myocardial infarction in our patients through multistage analysis (P<0.001), at 1p21.3, 1q31.2 (<it>CDC73</it>), 1q42.2 (<it>DISC1</it>), 3p21.31 (<it>CDCP1</it>), 10q11.21 (<it>RET</it>) 12p12.3 (<it>PIK3C2G</it>) and 16q23.3 (<it>CDH13</it>), respectively. In particular, the CNV region at 10q11.21 was examined by quantitative real-time PCR, the results of which were consistent with microarray findings.</p> <p>Conclusions</p> <p>Our preliminary results constitute an alternative method of evaluating the relationship between CNV regions and cardiovascular disease. These susceptibility CNV regions may be used as biomarkers for early-stage diagnosis of hyperlipidemia and myocardial infarction, rendering them valuable for further research and discussion.</p

Specific Forms of Graphene Quantum Dots Induce Apoptosis and Cell Cycle Arrest in Breast Cancer Cells

Graphene quantum dots (GQDs), nanomaterials derived from graphene and carbon dots, are highly stable, soluble, and have exceptional optical properties. Further, they have low toxicity and are excellent vehicles for carrying drugs or fluorescein dyes. Specific forms of GQDs can induce apoptosis and could be used to treat cancers. In this study, three forms of GQDs (GQD (nitrogen:carbon = 1:3), ortho-GQD, and meta-GQD) were screened and tested for their potential to inhibit breast cancer cell (MCF-7, BT-474, MDA-MB-231, and T-47D) growth. All three GQDs decreased cell viability after 72 h of treatment and specifically affected breast cancer cell proliferation. An assay for the expression of apoptotic proteins revealed that p21 and p27 were up-regulated (1.41-fold and 4.75-fold) after treatment. In particular, ortho-GQD-treated cells showed G2/M phase arrest. The GQDs specifically induced apoptosis in estrogen receptor-positive breast cancer cell lines. These results indicate that these GQDs induce apoptosis and G2/M cell cycle arrest in specific breast cancer subtypes and could potentially be used for treating breast cancers

Discovering Association between Alternative Splicing and Disease

[[abstract]]Alternative splicing is important because more proteins can be made from the relative few genes and. it is also an important mechanism regulating genetic expression. In this thesis, we will try to find out alternative splicing isoforms specific to several cancers and schizophenia and different development stage. The integrated information about diseases, pathways, proteins, and alternative splicing will be present. Methods: Alternative splicing database were constructed by aligning EST to whole genomic sequence using multi-layer unique marker methods. Each splicing site will be queried for their EST expression frequency in specific disease or development stage by the specific splicing isoforms. Significance were tested with Fish?s exact test. Using the position in the contig, splicing site was link to their correlated pretein, gene. By linking the same gene, disease information in OMIM and pathway information in KEGG were integrated. Results: 1263 disease specific splicing sites were identified. 63 exon skipping sites and 231 intron retain sites were correlated with hepatoma. 17 sites were correlated with schizophrenia. 163 splicing site were specific to fetal and infantile development stage. Discussion: Further laboratory investigation will be needed for confirming the real significant splicing site clinically

Novel Preparation of Reduced Graphene Oxide–Silver Complex using an Electrical Spark Discharge Method

This study used an electrical discharge machine (EDM) to perform an electrical spark discharge method (ESDM), which is a new approach for reducing graphene oxide (GO) at normal temperature and pressure, without using chemical substances. A silver (Ag) electrode generates high temperature and high energy during gap discharge. Ag atoms and Ag nanoparticles (AgNP) are suspended in GO, and ionization generates charged Ag+ ions in the Ag plasma with a strong reducing property, thereby carrying O away from GO. A large flake-like structure of GO was simultaneously pyrolyzed to a small flake-like structure of reduced graphene oxide (rGO). When Ag was used as an electrode, GO was reduced to rGO and the exfoliated AgNP surface was coated with rGO, thus forming an rGOAg complex. Consequently, suspensibility and dispersion were enhanced

Specific Forms of Graphene Quantum Dots Induce Apoptosis and Cell Cycle Arrest in Breast Cancer Cells

Graphene quantum dots (GQDs), nanomaterials derived from graphene and carbon dots, are highly stable, soluble, and have exceptional optical properties. Further, they have low toxicity and are excellent vehicles for carrying drugs or fluorescein dyes. Specific forms of GQDs can induce apoptosis and could be used to treat cancers. In this study, three forms of GQDs (GQD (nitrogen:carbon = 1:3), ortho-GQD, and meta-GQD) were screened and tested for their potential to inhibit breast cancer cell (MCF-7, BT-474, MDA-MB-231, and T-47D) growth. All three GQDs decreased cell viability after 72 h of treatment and specifically affected breast cancer cell proliferation. An assay for the expression of apoptotic proteins revealed that p21 and p27 were up-regulated (1.41-fold and 4.75-fold) after treatment. In particular, ortho-GQD-treated cells showed G2/M phase arrest. The GQDs specifically induced apoptosis in estrogen receptor-positive breast cancer cell lines. These results indicate that these GQDs induce apoptosis and G2/M cell cycle arrest in specific breast cancer subtypes and could potentially be used for treating breast cancers

Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

Abstract Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.</p

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet’s 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F’s superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time–frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches