Deep learning of heart-sound signals for efficient prediction of obstructive coronary artery disease.

Background: Due to the limitations of current methods for detecting obstructive coronary artery disease (CAD), many individuals are mistakenly or unnecessarily referred for coronary angiography (CAG). Objectives: Our goal is to create a comprehensive database of heart sounds in CAD and develop accurate deep learning algorithms to efficiently detect obstructive CAD based on heart sound signals. This will enable effective screening before undergoing CAG. Methods: We included 320 subjects suspected of CAD who underwent CAG. We employed advanced filtering techniques and state-of-the-art deep learning models (VGG-16, 1D CNN, and ResNet18) to analyze the heart sound signals and identify obstructive CAD (defined as at least one ≥50 % stenosis). To assess the performance of our models, we prospectively recruited an additional 80 subjects for testing. Results: In the test set, VGG-16 exhibited the highest performance with an area under the ROC curve (AUC) of 0.834 (95 % CI, 0.736-0.930), while ResNet-18 and CNN-7 achieved AUCs of only 0.755 (95 % CI, 0.614-0.819) and 0.652 (95 % CI, 0.554-0.770) respectively. VGG-16 demonstrated a sensitivity of 80.4 % and specificity of 86.2 % in the test set. The combined diagnostic model of VGG and DF scores achieved an AUC of 0.915 (95 % CI: 0.855-0.974), and the AUC for VGG combined with PTP scores was 0.908 (95 % CI: 0.845-0.971). The sensitivity and specificity of VGG-16 exceeded 0.85 in patients with coronary artery occlusion and those with 3 vascular lesions. Conclusions: Our deep learning model, based on heart sounds, offers a non-invasive and efficient screening method for obstructive CAD. It is expected to significantly reduce the number of unnecessary referrals for downstream screening.

Combination Therapy and Dual-Target Inhibitors Based on LSD1: New Emerging Tools in Cancer Therapy.

Lysine specific demethylase 1 (LSD1), a transcriptional modulator that represses or activates target gene expression, is overexpressed in many cancer and causes imbalance in the expression of normal gene networks. Over two decades, numerous LSD1 inhibitors have been reported, especially some of which have entered clinical trials, including eight irreversible inhibitors (TCP, ORY-1001, GSK-2879552, INCB059872, IMG-7289, ORY-2001, TAK-418, and LH-1802) and two reversible inhibitors (CC-90011 and SP-2577). Most clinical LSD1 inhibitors demonstrated enhanced efficacy in combination with other agents. LSD1 multitarget inhibitors have also been reported, exampled by clinical dual LSD1/histone deacetylases (HDACs) inhibitors 4SC-202 and JBI-802. Herein, we present a comprehensive overview of the combination of LSD1 inhibitors with various antitumor agents, as well as LSD1 multitarget inhibitors. Additionally, the challenges and future research directionsare also discussed, and we hope this review will provide new insight into the development of LSD1-targeted anticancer agents.

Machine learning algorithm can provide assistance for the diagnosis of non-ST-segment elevation myocardial infarction.

INTRODUCTION: Our aim was to use the constructed machine learning (ML) models as auxiliary diagnostic tools to improve the diagnostic accuracy of non-ST-elevation myocardial infarction (NSTEMI). MATERIALS AND METHODS: A total of 2878 patients were included in this retrospective study, including 1409 patients with NSTEMI and 1469 patients with unstable angina pectoris. The clinical and biochemical characteristics of the patients were used to construct the initial attribute set. SelectKBest algorithm was used to determine the most important features. A feature engineering method was applied to create new features correlated strongly to train ML models and obtain promising results. Based on the experimental dataset, the ML models of extreme gradient boosting, support vector machine, random forest, naïve Bayesian, gradient boosting machines and logistic regression were constructed. Each model was verified by test set data, and the diagnostic performance of each model was comprehensively evaluated. RESULTS: The six ML models based on the training set all play an auxiliary role in the diagnosis of NSTEMI. Although all models taken for comparison performed differences, the extreme gradient boosting ML model performed the best in terms of accuracy rate (0.95±0.014), precision rate (0.94±0.011), recall rate (0.98±0.003) and F-1 score (0.96±0.007) in NSTEMI. CONCLUSIONS: The ML model constructed based on clinical data can be used as an auxiliary tool to improve the accuracy of NSTEMI diagnosis. According to our comprehensive evaluation, the performance of the extreme gradient boosting model was the best.

Detailed curriculum vitae of HER2-targeted therapy.

With the booming development of precision medicine, molecular targeted therapy has been widely used in clinical oncology treatment due to a smaller number of side effects and its superior accuracy compared to that of traditional strategies. Among them, human epidermal growth factor receptor 2 (HER2)-targeted therapy has attracted considerable attention and has been used in the clinical treatment of breast and gastric cancer. Despite excellent clinical effects, HER2-targeted therapy remains in its infancy due to its resulting inherent and acquired resistance. Here, a comprehensive overview of HER2 in numerous cancers is presented, including its biological role, involved signaling pathways, and the status of HER2-targeted therapy.

Association of red cell distribution width (RDW) and the RDW to platelet count ratio with cardiovascular disease among US adults: a cross-sectional study based on the National Health and Nutrition Examination Survey 1999-2020.

OBJECTIVE: To investigate the association between red cell distribution width (RDW) and the RDW to platelet count ratio (RPR) and cardiovascular diseases (CVDs) and to further investigate whether the association involves population differences and dose-response relationships. DESIGN: Cross-sectional population-based study. SETTING: The National Health and Nutrition Examination Survey (1999-2020). PARTICIPANTS: A total of 48 283 participants aged 20 years or older (CVD, n=4593; non-CVD, n=43 690) were included in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the presence of CVD, while the secondary outcome was the presence of specific CVDs. Multivariable logistic regression analysis was performed to determine the relationship between RDW or the RPR and CVD. Subgroup analyses were performed to test the interactions between demographics variables and their associations with disease prevalence. RESULTS: A logistic regression model was fully adjusted for potential confounders; the ORs with 95% CIs for CVD across the second to fourth quartiles were 1.03 (0.91 to 1.18), 1.19 (1.04 to 1.37) and 1.49 (1.29 to 1.72) for RDW (p for trend <0.0001) compared with the lowest quartile. The ORs with 95% CIs for CVD across the second to fourth quartiles were 1.04 (0.92 to 1.17), 1.22 (1.05 to 1.42) and 1.64 (1.43 to 1.87) for the RPR compared with the lowest quartile (p for trend <0.0001). The association of RDW with CVD prevalence was more pronounced in females and smokers (all p for interaction <0.05). The association of the RPR with CVD prevalence was more pronounced in the group younger than 60 years (p for interaction=0.022). The restricted cubic spline also suggested a linear association between RDW and CVD and a non-linear association between the RPR and CVD (p for non-linear <0.05). CONCLUSION: There are statistical heterogeneities in the association between RWD, RPR distributions and the CVD prevalence, across sex, smoking status and age groups.

Detecting Single-Molecule Dynamics on Lipid Membranes with Quenchers-in-a-Liposome FRET.

Tracking membrane-interacting molecules and visualizing their conformational dynamics are key to understanding their functions. It is, however, challenging to accurately probe the positions of a molecule relative to a membrane. Herein, a single-molecule method, termed LipoFRET, is reported to assess interplay between molecules and liposomes. It takes advantage of FRET between a single fluorophore attached to a biomolecule and many quenchers in a liposome. This method was used to characterize interactions between α-synuclein (α-syn) and membranes. These results revealed that the N-terminus of α-syn inserts into the membrane and spontaneously transitions between different depths. In contrast, the C-terminal tail of α-syn is regulated by calcium ions and floats in solution in two conformations. LipoFRET is a powerful tool to investigate membrane-interacting biomolecules with sub-nanometer precision at the single-molecule level.