Association of gut microbiota with depression post-myocardial infarction: A systematic evaluation and meta-analysis protocol.

BACKGROUND: Depression post-myocardial infarction (MI) is becoming more prevalent. The gut-brain axis (GBA), influenced by the gut microbiota, is a critical component in understanding depression post-MI. Despite the well-established connection between gut microbiota and depression post-MI, this relationship remains incompletely understood. METHODS AND ANALYSIS: This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol (PRISMA-P) 2020 statement. Beginning from inception to October 2023, a systematic search will be conducted across eight electronic databases, including PubMed, MEDLINE, Scopus, Embase, Cochrane Clinical Trials Database, Web of Science, China National Knowledge Infrastructure, and China Biomedical Literature Database. Pre-selected studies will be independently assessed by two researchers following a standard inclusion, data extraction and quality assessment protocol. The primary outcome measures are differences in the profile of gut microbiota and rating scale scores for depression. Fixed-effects models will be used when both clinical heterogeneity and statistical heterogeneity are low, otherwise random-effects models will be used. Furthermore, subgroup analyses will be conducted on the depression severity of the participants using the same psychiatric scales employed, study type and geographic region. Random forest plot runs and research-related statistical analyses will be carried out using Rev Man V.5.3 software. EXPECTED RESULTS: This study will identify the association between the gut microbiota and the onset of depression post-MI, and provide evidence for the use of probiotics as an adjunctive treatment for depression post-MI. TRIAL REGISTRATION: Prospero registration number: CRD42023444026.

Triglyceride-glucose index as a potential predictor of major adverse cardiovascular and cerebrovascular events in patients with coronary heart disease complicated with depression.

Background: Triglyceride-glucose (TyG) index is a surrogate marker of insulin resistance and metabolic abnormalities, which is closely related to the prognosis of a variety of diseases. Patients with both CHD and depression have a higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) and worse outcome. TyG index may be able to predict the adverse prognosis of this special population. Methods: The retrospective cohort study involved 596 patients with both CHD and depression between June 2013 and December 2023. The primary outcome endpoint was the occurrence of MACCE, including all-cause death, stroke, MI and emergent coronary revascularization. The receiver operating characteristic (ROC) curve, Cox regression analysis, Kaplan-Meier survival analysis, and restricted cubic spline (RCS) analysis were used to assess the correlation between TyG index and MACCE risk of in patients with CHD complicated with depression. Results: With a median follow-up of 31 (15-62) months, MACCE occurred in 281(47.15%) patients. The area under the ROC curve of TyG index predicting the risk of MACCE was 0.765(0.726-0.804) (P<0.01). Patients in the high TyG index group(69.73%) had a significantly higher risk of developing MACCE than those in the low TyG index group(23.63%) (P<0.01). The multifactorial RCS model showed a nonlinear correlation (nonlinear P<0.01, overall P<0.01), with a critical value of 8.80 for the TyG index to predict the occurrence of MACCE. The TyG index was able to further improve the predictive accuracy of MACCE. Conclusions: TyG index is a potential predictor of the risk of MACCE in patients with CHD complicated with depression.

Efficacy and safety of non-pharmacological therapy under the guidance of TCM theory in the treatment of anxiety in patients with myocardial infarction: A protocol for systematic review and meta-analysis.

BACKGROUND: With the increasing pressures of modern life and work, combined with a growing older population, the incidence of comorbid anxiety and myocardial infarction (MI) is increasing. Anxiety increases the risk of adverse cardiovascular events in patients with MI and significantly affects their quality of life. However, there is an ongoing controversy regarding the pharmacological treatment of anxiety in patients with MI. The concomitant use of commonly prescribed selective serotonin reuptake inhibitors (SSRIs) and antiplatelet medications such as aspirin and clopidogrel may increase the risk of bleeding. Conventional exercise-based rehabilitation therapies have shown limited success in alleviating anxiety symptoms. Fortunately, non-pharmacological therapies based on traditional Chinese medicine (TCM) theory, such as acupuncture, massage, and qigong, have demonstrated promising efficacy in treating MI and comorbid anxiety. These therapies have been widely used in community and tertiary hospital settings in China to provide new treatment options for patients with anxiety and MI. However, current studies on non-pharmacological TCM-based therapies have predominantly featured small sample sizes. This study aims to comprehensively analyze and explore the effectiveness and safety of these therapies in treating anxiety in patients with MI. METHOD: We will systematically search six English and four Chinese databases by employing a pre-defined search strategy and adhering to the unique rules and regulations of each database to identify studies that fulfilled our inclusion criteria, to qualify for inclusion, patients must be diagnosed with both MI and anxiety, and they must have undergone non-pharmacological TCM therapies, such as acupuncture, massage, or qigong, whereas the control group received standard treatments. The primary outcome measure will be alterations in anxiety scores, as assessed using anxiety scales, with secondary outcomes encompassing the evaluations of cardiopulmonary function and quality of life. We will utilize RevMan 5.3 to conduct a meta-analysis of the collected data, and subgroup analyses will be executed based on distinct types of non-pharmacological TCM therapies and outcome measures. RESULTS: A narrative summary and quantitative analysis of the existing evidence on the treatment of anxiety patients with MI using non-pharmacological therapies guided by Traditional Chinese Medicine theory. CONCLUSION: This systematic review will investigate whether non-pharmacological interventions guided by TCM theory are effective and safe for anxiety in patients with MI, and provide evidence-based support for their clinical application. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022378391.

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in breast tumor classification using artificial intelligence technology. This paper proposes a method for classifying benign and malignant breast tumors using ABUS sequence based on deep learning. First, Images of Interest (IOI) will be extracted and Region of Interest (ROI) will be cropped in ABUS sequence by two preprocessing deep learning models, Extracting-IOI model and Cropping-ROI model. Then, we propose a Shallowly Dilated Convolutional Branch Network (SDCB-Net). We combine this network with the VGG16 transfer learning network to construct a brand-new Shared Extracting Feature Network (SEF-Net) to extract ROI sequence features. Finally, the correlation features of ABUS images are extracted and integrated by using GRU Classified Network (GRUC-Net) to achieve the accurate breast tumors classification. The final results show that the accuracy of the test set for classifying benign and malignant ABUS sequence is 92.86 %. This method not only has high accuracy but also greatly improves the speed and efficiency of breast tumor classification. It has high clinical application significance that more women can discover breast tumors timely.

Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory.

OBJECTIVE: Carotid atherosclerosis (CAS) is the main reason leading to cardiovascular conditions such as coronary heart disease and cerebrovascular diseases. In the carotid ultrasound images, the carotid intima-media structure can be observed in an annular narrow strip, which its inner contour corresponds to the carotid intima, and the outer contour corresponds to the carotid extima. With the development of carotid atherosclerosis, the carotid intima-media will gradually thicken. Therefore, doctors can observe the carotid intima-media so as to obtain the pathological changes of the internal structure of the patient's carotid arteries. However, due to the presence of artifacts and noises the quality of the ultrasound images are degraded, making it difficult to obtain accurate carotid intima-media structures. This article presents a novel self-adaptive method to enable obtaining the carotid intima-media through carotid intima/extima segmentation. METHOD: After preprocessing the ultrasound images by homomorphic filtering and median filtering, we propose an improved superpixel generation algorithm that employs the fusion of gray-level and luminosity-based information to decompose the image into numerous superpixels and later presents the carotid intima. Meanwhile, based on the features of the carotid artery, the initial position of the carotid extima is located by the normalized cut algorithm and later the fractal theory is employed to segment the carotid extima. RESULTS: The proposed method for segmenting carotid intima obtained mean values of the DICE true positive ratio (TPR), false positive ratio (FPR), precision scores of 97.797%, 99.126%, 0.540%, 97.202%, respectively. Further from the segmentation method of the carotid extima the performance measures such as mean DICE, TPR, accuracy, F-score obtained are 95.00%, 92.265%, 97.689%, 94.997%, respectively. CONCLUSION: Comparing with traditional methods, the proposed method performed better. The experimental results indicated that the proposed method obtained the carotid intima-media both automatically and accurately thus effectively assist doctors in the diagnosis of CAS.

Breast ultrasound lesion classification based on image decomposition and transfer learning.

PURPOSE: In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a major topic of interest. Because obtaining large volumes of breast lesion ultrasound image data is difficult, transfer learning is usually employed to obtain benign and malignant classification of breast lesions. However, because of blurred unclear regions of interest in breast lesion ultrasound images and severe speckle noise interference, convolutional neural networks have proven ineffective in extracting features, thus providing unreliable classification results. METHODS: This study employs image decomposition to obtain fuzzy enhanced and bilateral filtered images to enrich input information of breast lesions. Fuzzy enhanced, bilateral filtered, and original ultrasound images comprise multifeature data, which are presented as inputs to a pre-trained model to realize knowledge fusion. Therefore, effective features of breast lesions are extracted and then used to train fully connected layers with ground truths provided by a doctor to accomplish the classification. RESULTS: A pre-trained VGG16 model was used to extract features from multifeature data, and these features were fused to train the fully connected layers to realize classification. The performance score reported is as follows: accuracy of 93%, sensitivity of 95%, specificity of 88%, F1 score of 0.93, and AUC of 0.97. CONCLUSIONS: Compared with using a single original ultrasound image for feature extraction, multifeature data based on image decomposition enables the pre-trained model to extract more relevant features, thereby providing better classification results than those from traditional transfer learning techniques.

Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation.

In order to realize the visual analysis of cardiac fluid motion, according to the characteristics of cardiac flow field ultrasound image, a method for the cardiac Vector Flow Mapping (VFM) analysis and evaluation based on the You-Only-Look-Once (YOLO) deep learning model and the improved two-dimensional continuity equation is proposed in this paper. Firstly, based on the ultrasound Doppler data, the radial velocity values of the blood particles are obtained; due to the real-time VFM's high requirement on the computing speed, the YOLO deep learning model is combined with an improved block matching algorithm for the localization and tracking of myocardial wall, and then the azimuth velocity of myocardial wall speckles can be obtained; in addition, it is proposed in this paper to use a nonlinear weight function to fuse the radial velocity of the blood particles and azimuth velocity of myocardial wall speckles nonlinearly, and further the vortex streamline diagram in the cardiac flow field can be obtained. The results of the experiments on the evaluation of the Ultrasonic apical long-axis view show that the proposed method not only improves the accuracy of VFM, but also provides a new evaluation basis for cardiac function impairment.