Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Endocrine disrupting chemical Bisphenol A and its association with cancer mortality: a prospective cohort study of NHANES.

Introduction: There is evidence suggesting that Bisphenol A (BPA) is associated with increased all-cause mortality in adults. However, the specific nature of the relationship between BPA exposure and cancer mortality remains relatively unexplored. Methods: The National Health and Nutrition Examination Survey (NHANES) dataset was used to recruit participants. Urinary BPA was assessed using liquid chromatography-mass spectrum (LC-MS). Through the use of multivariable Cox proportional hazard regressions and constrained cubic splines, the relationships between urine BPA and death from all causes and cancer were investigated. Results: This study has a total of 8,035 participants, and 137 died from cancers after a 7.5-year follow-up. The median level of BPA was 2.0 g/mL. Urinary BPA levels were not independently associated with all-cause mortality. For cancer mortality, the second quartile's multivariable-adjusted hazard ratio was 0.51 (95% confidence interval: 0.30 to 0.86; p = 0.011) compared to the lowest quartile. The restricted cubic splines showed that the association was nonlinear (p for nonlinearity = 0.028) and the inflection point was 1.99 ng/mL. Conclusion: Urinary BPA exposure was U-shaped associated with the risk of cancer mortality, and a lower level of BPA less than 1.99 ng/mL was associated with a higher risk of cancer mortality.

AI- and IoT-Enabled Solutions for Healthcare.

Patient care and management have entered a new arena, where intelligent technology can assist clinicians in both diagnosis and treatment [...].

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

Detection of Coronary Artery Disease Based on Clinical Phonocardiogram and Multiscale Attention Convolutional Compression Network.

Heart sound is an important physiological signal that contains rich pathological information related with coronary stenosis. Thus, some machine learning methods are developed to detect coronary artery disease (CAD) based on phonocardiogram (PCG). However, current methods lack sufficient clinical dataset and fail to achieve efficient feature utilization. Besides, the methods require complex processing steps including empirical feature extraction and classifier design. To achieve efficient CAD detection, we propose the multiscale attention convolutional compression network (MACCN) based on clinical PCG dataset. Firstly, PCG dataset including 102 CAD subjects and 82 non-CAD subjects was established. Then, a multiscale convolution structure was developed to catch comprehensive heart sound features and a channel attention module was developed to enhance key features in multiscale attention convolutional block (MACB). Finally, a separate downsampling block was proposed to reduce feature losses. MACCN combining the blocks can automatically extract features without empirical and manual feature selection. It obtains good classification results with accuracy 93.43%, sensitivity 93.44%, precision 93.48%, and F1 score 93.42%. The study implies that MACCN performs effective PCG feature mining aiming for CAD detection. Further, it integrates feature extraction and classification and provides a simplified PCG processing case.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

Causal Inference for Hypertension Prediction.

Hypertension is a leading cause of cardiovascular disease and premature death worldwide and it puts a heavy burden on the healthcare system. It is, therefore, very important to detect and evaluate hypertension and related cardiovascular events as to for efficient diagnosis, treatment and management. Hypertension can be evaluated with noninvasive cardiac signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Most of the previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors. In this study, we propose a causal inference based approach to identify feature variables from ECG and PPG signals that are potentially causally related with hypertension. The method of greedy equivalence search was employed to construct the causal graph of features and hypertension. With causal features identified from the causal graph, we used machine learning models to diagnose hypertension. The machine learning classification models achieve great classification performance, among which random forest model has the best classification performance, with accuracy being 0.987, precision being 0.990, recall being 0.981, and F1-score being 0.985. The results show that the causal inference based approach can effectively predict hyper-tension.Clinical relevance- This paper proposes a new hypertension risk prediction method, which uses causality instead of correlation as the feature screening criteria to establish a causal graph of hypertension, which can predict the hypertension more reliably.

Continual Learning for Cuffless Blood Pressure Measurement using PPG and ECG Signals.

Although numerous studies have been conducted on cuffless blood pressure (BP) estimation using machine learning methods, most of the data-driven models are static, with model parameters fixed after training is complete. However, BP is dynamic and the performance would degrade for a static model when the to-be predicted BP distribution deviates from the training BP distribution. In this paper, we propose a continual learning (CL) framework in which deep learning models are developed to learn dynamically and continuously for arterial BP (ABP) estimation with photoplethysmography (PPG) and electrocardiogram (ECG) waveforms. The effectiveness of the CL model is validated on UCI Repository and MIMIC-III database with a total of 132 individual samples, and compared with conventional training method. It was found that the CL model improved the ABP estimation accuracy in terms of mean absolute error (MAE) by 17.47% on average compared with conventional training model. Furthermore, the improvement increased with the variability of ABP. These results demonstrate that CL model has potential to estimate dynamic ABP, which has been challenging with conventional training.

Exercise intervention for patients with chronic low back pain: a systematic review and network meta-analysis.

Purpose: Chronic low back pain (CLBP) is an aging and public health issue that is a leading cause of disability worldwide and has a significant economic impact on a global scale. Treatments for CLBP are varied, and there is currently no study with high-quality evidence to show which treatment works best. Exercise therapy has the characteristics of minor harm, low cost, and convenient implementation. It has become a mainstream treatment method in clinics for chronic low back pain. However, there is insufficient evidence on which specific exercise regimen is more effective for chronic non-specific low back pain. This network meta-analysis aimed to evaluate the effects of different exercise therapies on chronic low back pain and provide a reference for exercise regimens in CLBP patients. Methods: We searched PubMed, EMBASE, Cochrane Library, and Web of Science from inception to 10 May 2022. Inclusion and exclusion criteria were used for selection. We collected information from studies to compare the effects of 20 exercise interventions on patients with chronic low back pain. Results: This study included 75 randomized controlled trials (RCTs) with 5,254 participants. Network meta-analysis results showed that tai chi [standardized mean difference (SMD), -2.11; 95% CI, -3.62 to -0.61], yoga (SMD, -1.76; 95% CI -2.72 to -0.81), Pilates exercise (SMD, -1.52; 95% CI, -2.68, to -0.36), and sling exercise (SMD, -1.19; 95% CI, -2.07 to -0.30) showed a better pain improvement than conventional rehabilitation. Tai chi (SMD, -2.42; 95% CI, -3.81 to -1.03) and yoga (SMD, -2.07; 95% CI, -2.80 to -1.34) showed a better pain improvement than no intervention provided. Yoga (SMD, -1.72; 95% CI, -2.91 to -0.53) and core or stabilization exercises (SMD, -1.04; 95% CI, -1.80 to -0.28) showed a better physical function improvement than conventional rehabilitation. Yoga (SMD, -1.81; 95% CI, -2.78 to -0.83) and core or stabilization exercises (SMD, -1.13; 95% CI, -1.66 to -0.59) showed a better physical function improvement than no intervention provided. Conclusion: Compared with conventional rehabilitation and no intervention provided, tai chi, toga, Pilates exercise, sling exercise, motor control exercise, and core or stabilization exercises significantly improved CLBP in patients. Compared with conventional rehabilitation and no intervention provided, yoga and core or stabilization exercises were statistically significant in improving physical function in patients with CLBP. Due to the limitations of the quality and quantity of the included studies, it is difficult to make a definitive recommendation before more large-scale and high-quality RCTs are conducted.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Causal inference based cuffless blood pressure estimation: A pilot study.

Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years. However, causality has been overlooked in most of current studies. In this study, we aim to examine the feasibility of causal inference for cuffless BP estimation. We first attempt to detect wearable features that are causally related, rather than correlated, to BP changes by identifying causal graphs of interested variables with fast causal inference (FCI) algorithm. With identified causal features, we then employ time-lagged link to integrate the mechanism of causal inference into the BP estimated model. The proposed method was validated on 62 subjects with their continuous ECG, PPG and BP signals being collected. We found new causal features that can better track BP changes than pulse transit time (PTT). Further, the developed causal-based estimation model achieved an estimation error of mean absolute difference (MAD) being 5.10 mmHg and 2.85 mmHg for SBP and DBP, respectively, which outperformed traditional model without consideration of causality. To the best of our knowledge, this work is the first to study the causal inference for cuffless BP estimation, which can shed light on the mechanism, method and application of cuffless BP measurement.

Physical activity and asthma symptom control in children during COVID-19 lockdown: A feasibility study.

Objective: The aim was to evaluate the impact of the COVID-19 lockdown on physical activity (PA) and asthma symptom control in children. Methods: We conducted a single-cohort observational study on 22 children with a median age of 9 (8-11) years with a diagnosis of asthma being included in the study. Participants were asked to wear a PA tracker for 3 months; during the same 3-month period, the Paediatric Asthma Diary (PAD) was administered daily and the Asthma Control (AC) Questionnaire together with the mini-Paediatric Asthma Quality of Life (AQoL) Questionnaire administered at weekly intervals. Results: Compared with the pre-lockdown period, there was a significant reduction in PA levels after the lockdown began. Daily total steps reduced by about 3000 steps (p < 0.001), very active minutes by 9 min (p < 0.001) and fairly active minutes almost halved (p < 0.001); while asthma symptom control marginally improved, with the AC and AQoL score improving by 0.56 (p < 0.005) and 0.47 (p < 0.05), respectively. Further, for those with AC score higher than 1, PA was positively associated with asthma control both before and after the lockdown. Conclusions: This feasibility study suggests that PA engagement of children with asthma is negatively affected during the pandemic, but the beneficial effect of PA on asthma symptom control potentially sustains even during a lockdown period. These findings emphasize the importance of wearable device to monitor longitudinal PA and thus better management of PA for achieving the best outcome of asthma symptom control.

Current situation and influencing factors of the nursing practice environment in five tertiary general hospitals in Shenzhen: a cross-sectional study.

Objective: This study investigates the current situation and influencing factors of the nursing practice environment in Shenzhen, China, and provides suggestions for improving it. Background: Nursing shortage is an urgent global problem and also of concern in China. Studies have shown that better work environments are related to high job satisfaction and better patient outcomes. Methods: The 37-item Practice Environment Scale was used to assess the nursing practice environment. Respondents were 1116 nurses from five general tertiary hospitals in Shenzhen. Results: The mean satisfaction score for the nursing practice environment was 3.63 ± 0.72 (where 5 is the highest possible score). Position, being a specialist nurse, choice of nursing major, educational attainment, and night shifts significantly affected nurses' working environment satisfaction. Conclusion: The practice environment of nurses was satisfactory. We recommend reducing the workload and encouraging nurses to complete specialist training, and supporting nurses to expand their roles in hospitals and society to improve the nursing practice environment.

Bayesian Model Averaging for Improving the Accuracy of Cuffless Blood Pressure Estimation.

In recent decades, many researches have proposed various models for continuous, cuffless blood pressure (BP) estimation. However, due to aleatoric uncertainty and epistemic uncertainty existing in the problem, it is very challenging to evaluate cuffless BP with acceptable accuracy. This paper innovatively proposes a cuffless BP ensemble estimation model based on Bayesian Model Average (BMA) method to reduce the epistemic uncertainty. We combine four most frequently cited physiological models and four regression models based on Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals, and use the BMA method to assign weights to each model to achieve accurate cuffless BP prediction. The proposed method was validated on 17 healthy and 13 hypertensive subjects with continuous Finometer BP as a reference. The results showed that the error mean ± SD (standard deviations) of both SBP and DBP predicted by the proposed method were 2.13 ± 5.68 mmHg and 1.42 ± 5.11 mmHg, respectively, which were both lower than each of the model. And the MAE was 6% and 8% lower than the best member of the model ensemble. We also analyzed the relationship between the number of training epochs and model prediction performance. When 15 cardiac cycles were choosed for training, it could get a good balance between model prediction accuracy and algorithm complexity. Therefore, the proposed BMA method can solve the model uncertainty problem, providing robust and deterministic BP prediction. Clinical relevance- This paper proposes a new method for wearable BP estimation which enables BP monitoring in both clinical settings and home settings. It offers a stable way to monitor BP to help patients detect disease early.

Roles of GFPT2 Expression Levels on the Prognosis and Tumor Microenvironment of Colon Cancer.

Background: Recently, increasing evidence has suggested that Glutamine-fructose-6-phosphate transaminase 2 (GFPT2) is related to carcinogenesis. However, the potential roles of GFPT2 in colon cancer still need to be fully investigated. Methods: We examined the protein levels of GFPT2 by immunohistochemistry (IHC) in tissues collected from 83 patients with colon cancer. We further detected GFBPT2 protein levels by Western Blot assay. We checked the relationship between GFPT2 expression levels and overall survival (OS), stromal and immune scores and immune components from The Cancer Gene Atlas (TCGA) database. GFBP2-related pathways were validated in the Cancer Cell Line Encyclopedia (CCLE) database. Expression of GFPT2 in single cell subpopulations was calculated from The Tumor Immune Single Cell Center (TISCH). The levels of GFPT2 and drug sensitivity data were performed from CellMiner dataset. Results: GFPT2 was highly expressed and correlated with poor pathological features in 83 colon cancer patients. Moreover, increased GFPT2 expression was significantly associated with poorer OS in 329 colon adenocarcinoma (COAD) patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the differentially expressed genes of GFPT2 were mostly enriched in focal adhesion, ECM receptor interaction, JAK/STAT signaling pathway and immune related pathways. In addition, GFPT2 expression was correlated with the tumor microenvironment (TME). GFPT2 expression was linked to cancer-associated fibroblasts (CAFs)-associated factors and epithelial-mesenchymal transition (EMT)-related factors. GFPT2 was positively correlated with immunosuppressive cells and regulated immunosuppressive factors and T-cell exhaustion. Finally, our data suggested that the expression of GFPT2 may be a judgment of the sensitivity of a certain class of drugs. Conclusions: Our work reveals the roles of GFPT2 in tumorigenesis, particularly in immune response, TME and drug resistance, which are crucial for the development of customized cancer therapies.

Effect of occupational exposure to antineoplastic drugs on DNA damage in nurses: a cross-sectional study.

BACKGROUND: Although the therapeutic effect of antineoplastic drugs is incontestable, these agents can also potentially act as carcinogens, mutagens and/or teratogens in people. The aim of this study was to assess the effect of occupational exposure to antineoplastic drugs on DNA damage, assessed by the comet assay and cytokinesis-block micronucleus (CBMN) assay, in nurses. METHODS: The cross-sectional study enrolled 305 nursing staff members from 7 public hospitals in Shenzhen who handled antineoplastic drugs, and 150 healthy nursing staff members who were not exposed to antineoplastic drugs as the control group. DNA damage was assessed by the comet and CBMN assay. Multiple linear regressions and logistic regressions models were used to analyse the effect of occupational exposure to antineoplastic drugs on DNA damage. RESULTS: After adjustment for confounding factors, compared with non-exposure to antineoplastic drugs, exposure to antineoplastic drugs was positively related to tail moment, olive moment, tail length and tail DNA per cent, and adjusted ß or OR (95% CI) was 0.17 (0.08 to 0.26), 0.18 (0.10 to 0.27), 1.03 (0.47 to 1.60) and 1.16 (1.04 to 1.29) (all p<0.05). Moreover, similar significant relationships were observed for the biomarkers of the CBMN assay. Additionally, other than age, there was no interaction between antineoplastic drug exposure and other variables for the levels of biomarkers of the CBMN assay and the comet assay. CONCLUSIONS: The present results showed that exposure to antineoplastic drugs was positively related to the risk of DNA damage in nurses. The results imply that occupational exposure to antineoplastic agents is an important global public health problem that requires urgent attention.

Risk factors for falls among community-dwelling older adults: A systematic review and meta-analysis.

Background and objective: The prevalence of falls among older adults living in the community is ~30% each year. The impacts of falls are not only confined to the individual but also affect families and the community. Injury from a fall also imposes a heavy financial burden on patients and their families. Currently, there are different reports on the risk factors for falls among older adults in the community. A retrospective analysis was used in this study to identify risk factors for falls in community-dwelling older adults. This research aimed to collect published studies to find risk factors for falls in community-dwelling older adults. Methods: We searched for literature from the founding of PubMed, EMBASE, the Cochrane Library, the Web of Science, the China National Knowledge Infrastructure (CNKI), the China Science and Technology Periodicals Database (VIP), and the Wanfang database until September 2022. The studies were selected using inclusion and exclusion criteria. We collected information from relevant studies to compare the impact of potential risk factors such as age, female gender, fear of falling, history of falls, unclear vision, depression, and balance disorder on falls among community-dwelling older adults. Results: A total of 31 studies were included with 70,868 community seniors. A significant risk factor for falls in the community of older adults was dementia (2.01, 95% CI: 1.41-2.86), age (1.15, 95% CI: 1.09-1.22), female gender (1.52, 95% CI: 1.27-1.81), fear of falling (2.82, 95% CI: 1.68-4.74), history of falls (3.22, 95% CI: 1.98-5.23), vision unclear (1.56, 95% CI: 1.29-1.89), depression (1.23, 95% CI: 1.10-1.37), and balance disorder (3.00, 95% CI: 2.05-4.39). Conclusion: This study provides preliminary evidence that falls among community-dwelling older adults are associated with factors such as age, female gender, fear of falling, history of falls, unclear vision, depression, and balance disorders. The results of this research may help improve clinician awareness, risk stratification, and fall prevention among community-dwelling older adults. Systematic review registration: identifier INPLASY2022120080.

Feasibility Study of Pulse Width at Half Amplitude of Camera PPG for Contactless Blood Pressure Estimation.

Non-contact blood pressure (BP) estimation with imaging photoplethysmogram (PPG) that can be acquired by camera is a promising alternative to cuff-based technology because of its nature of pervasive, low-cost, and being continuous. Most of the non-contact BP estimation methods are based on the principle of pulse transit time (PTT) as being used for wearable cuffless BP measurement. However, PTT-based method on the one hand requires simultaneous capture of images of multiple skin sites with the sites being at a distance from each other; and on the other hand, it can only partially reflect BP changes according to previous studies. In this paper, we propose to use a different camera PPG feature that has not yet been fully studied - pulse width at half amplitude (PWHA) for the evaluation of BP in a non-contact way. PWHA can be obtained from a single-site camera PPG, and it can indicate BP changes. The relationship of PWHA and BP was analyzed on 16 healthy subjects with BP changes induced by deep breathing and stepping exercise. The results showed that beat-to-beat PWHA can well track dynamic BP changes, and it is inversely related to BP across the sampled population and within each individual with about 80% individuals having high correlations. The findings suggest that PWHA can reflect the dynamic changes in cardiovascular characteristics and thereby BP changes, demonstrating the feasibility of imaging PWHA for non-contact BP estimation beyond the PTT method.Clinical Relevance- This provides a potential new method for non-contact BP, which allows BP monitoring at home, clinical setting, and public places in a pervasive manner. It reduces contacts between persons during a pandemic and offers an ever-present way to monitor BP.

Seeing is Sensing - External Observation of In Vivo Biological Gradient Field by Tracking Nanoswimmers.

This work aims to demonstrate through computational analysis that, by monitoring the trajectory of externally manipulable nanoswimmers (NS), the in vivo biological gradient field (BGF) interacting with the NS can be indirectly observed. This observability is fundamental to the recently proposed framework of computational nanobiosensing (CONA) for "smart" cancer detection. We first present a novel NS propagation model to emulate the complex and chaotic NS kinetics inside the capillary network. Next, we propose an efficient control method that is able to employ the NS as in vivo sensors for the measurement of a specific BGF such as blood viscosity. The proposed method, based on the Linear Quadratic Regulator (LQR), effectively stabilizes the signal-to-noise ratio (SNR) induced by the Brownian motion of NS at a level above 10 dB to enhance the accuracy of viscosity estimation.

AEBP1 Is One of the Epithelial-Mesenchymal Transition Regulatory Genes in Colon Adenocarcinoma.

Epithelial-mesenchymal transition (EMT) is involved in various tumor processes, including tumorigenesis, tumor cell migration and metastasis, tumor stemness, and therapeutic resistance. Therefore, it is important to identify the genes most associated with EMT and develop them as therapeutic targets. In this work, we first analyzed EMT hallmark gene expression profiles among 10,535 pan-cancer samples from The Cancer Genome Atlas (TCGA) and divided them into EMT high and EMT low groups according to the metagene scores. Then, we identified 12 genes that were most associated with high EMT metagene score (R > 0.9) in 329 colon adenocarcinoma (COAD) patients. Among them, only 4 genes (AEBP1, KCNE4, GFPT2, and FAM26E) had statistically significant differences in prognosis (P < 0.05). Next, we selected AEBP1 as a candidate and showed that AEBP1 mRNA levels and EMT biomarkers strongly coexpressed in 329 COAD samples. In addition, AEBP1 was highly expressed and associated with poor clinical outcomes and prognosis in COAD patients. Finally, to explore whether AEBP1-mediated EMT was related to the tumor microenvironment (TME), we examined AEBP1 expression levels at the single-cell levels. Our results showed that AEBP1 levels were extremely high in tumor-associated fibroblasts, which may induce EMT. AEBP1 expression was also positively correlated with the expression of fibroblast biomarkers and also with EMT metascores, suggesting that AEBP1-mediated EMT may be associated with the stimulation of fibroblast activation. Therefore, AEBP1 may be a promising target for EMT inhibition, which reduces cancer metastasis and drug resistance in COAD patients.