Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Background and Objectives: Cardiac resynchronization therapy (CRT) is a treatment option for patients with heart failure and left ventricular (LV) dyssynchrony. However, one-third of CRT patients are non-responders. The objective of this study is to evaluate the feasibility of an in silico electromechanical heart model to predict CRT outcomes. Methods: The three-dimensional heart geometries of 11 patients (age, 64.0 ± 10.7 years, 4 men) with CRT were constructed from cardiac computed tomography images. The finite element method was used to simulate the electrical wave propagation and mechanics in the heart. We coupled cardiac electrical excitation and mechanical contraction with vascular hemodynamics using a lumped parameter model. We predicted the LV end-diastolic and end-systolic volumes and LV ejection fraction (LVEF) after CRT implantation using the in silico CRT simulation model. A CRT responder was defined as a patient with an absolute LVEF increase ≥ 5%. We compared the in silico CRT outcomes with the real CRT outcomes. Results: Two patients were in silico CRT responders, and those 2 patients were also real CRT responders. Nine patients were in silico CRT non-responders, 8 who were real CRT non-responders, and 1 who was a real CRT responder. The in silico CRT outcomes agreed with the real CRT outcomes in 10 of 11 patients. The positive and negative predictive values, accuracy, sensitivity, and specificity of the in silico CRT model were 100%, 88.9%, 90.9%, 66.7%, and 100%, respectively. Conclusion The in silico CRT simulation model is feasible for predicting real CRT outcomes.

Background: Diacylglycerol O-acyltransferase 2 (DGAT2) synthesizes triacylglycerol (TG) from diacylglycerol; therefore, DGAT2 is considered as a therapeutic target for steatosis. However, the consequence of inhibiting DGAT2 is not fully investigated due to side effects including lethality and lipotoxicity. In this article, we observed the role of DGAT2 in hepatocarcinoma. Methods: The role of DGAT2 is analyzed via loss-of-function assay. DGAT2 knockdown (KD) and inhibitor treatment on HepG2 cell line was analyzed. Cumulative analysis of cell metabolism with bioinformatic data were assessed, and further compared with different cohorts of liver cancer patients and non-alcoholic fatty liver disease (NAFLD) patients to elucidate how DGAT2 is regulating cancer metabolism. Results: Mitochondrial function is suppressed in DGAT2 KD HepG2 cell along with the decreased lipid droplets. In the aspect of the cancer, DGAT2 KD upregulates cell proliferation. Analyzing transcriptome of NAFLD and hepatocellular carcinoma (HCC) patients highlights negatively correlating expression patterns of 73 lipid-associated genes including DGAT2. Cancer patients with the lower DGAT2 expression face lower survival rate. DGAT2 KD cell and patients’ transcriptome show downregulation in estrogen- related receptor alpha (ESRRA) via integrated system for motif activity response analysis (ISMARA), with increased dimerization with corepressor prospero homeobox 1 (PROX1). Conclusion DGAT2 sustains the stability of mitochondria in hepatoma via suppressing ESRRA-PROX1 transcriptional network and hinders HCC from shifting towards glycolytic metabolism, which lowers cell proliferation.

Background: Cardiac resynchronization therapy (CRT) is an effective treatment option for patients with heart failure (HF) and left ventricular (LV) dyssynchrony. However, the problem of some patients not responding to CRT remains unresolved. This study aimed to propose a novel in silico method for CRT simulation. Methods: Three-dimensional heart geometry was constructed from computed tomography images. The finite ele‑ ment method was used to elucidate the electric wave propagation in the heart. The electric excitation and mechani‑ cal contraction were coupled with vascular hemodynamics by the lumped parameter model. The model parameters for three-dimensional (3D) heart and vascular mechanics were estimated by matching computed variables with measured physiological parameters. CRT effects were simulated in a patient with HF and left bundle branch block (LBBB). LV end-diastolic (LVEDV) and end-systolic volumes (LVESV), LV ejection fraction (LVEF), and CRT responsiveness measured from the in silico simulation model were compared with those from clinical observation. A CRT responder was defined as absolute increase in LVEF ≥ 5% or relative increase in LVEF ≥ 15%. Results: A 68-year-old female with nonischemic HF and LBBB was retrospectively included. The in silico CRT simu‑ lation modeling revealed that changes in LVEDV, LVESV, and LVEF by CRT were from 174 to 173 mL, 116 to 104 mL, and 33 to 40%, respectively. Absolute and relative ΔLVEF were 7% and 18%, respectively, signifying a CRT responder.In clinical observation, echocardiography showed that changes in LVEDV, LVESV, and LVEF by CRT were from 162 to 119 mL, 114 to 69 mL, and 29 to 42%, respectively. Absolute and relative ΔLVESV were 13% and 31%, respectively, also signifying a CRT responder. CRT responsiveness from the in silico CRT simulation model was concordant with that in the clinical observation. Conclusion This in silico CRT simulation method is a feasible technique to screen for CRT non-responders in patients with HF and LBBB.

raumatic shoulder dislocations are one of the most common major dislocations in the general population. Injury to major vessels is rarely reported as a complication of shoulder dislocations. This case report presents the traumatic dissection of the axillary artery after a simple shoulder dislocation that was managed successfully with the placement of a self-expanding stent. With the clinical manifestations of a brachial plexus injury and progressive vascular compromise in the affected arm, a major vascular injury was detected on an angiogram, and a self-expanding stent was deployed. Through immediate diagnosis and prompt intervention, serious complications, such as hypovolemic shock and even death, were averted, ultimately achieving a favorable patient outcome.

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.
Cardiovascular Diseases ; Diagnosis ; Humans ; Jurisprudence ; Machine Learning ; Models, Theoretical ; Patient-Specific Modeling