Background and Objective: The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. M...
详细信息
Background and Objective: The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. machinelearning (ML) algorithms were also selected aimed at providing predictions to suggest practical recommendations for public awareness and clinical ***: We included 21,279 eligible participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Weighted ordinal logistic regression (LR) was utilized with further sensitivity and dose-response analysis, and ML algorithms were analyzed with SHapley Additive exPlanations (SHAP) applied to make interpretable results and ***: Our studies demonstrated an inverse relationship between LE8 and elevated depressive levels, with robustness confirmed through subgroup and interaction analysis. Age-specific findings revealed middle-aged and older adults (aged 40-60 and over 60) which showed higher depresion severity, highlighting the need for greater awareness and targeted interventions. Eight ML algorithms were selected to provide predictive results, and further SHAP would become ideal supplement to increase model ***: Our studies demonstrated a negative association between LE8 and elevated depressive levels and provided a suite of ML predictive models, which would generate recommendations toward clinical implications and subjective interventions.
BackgroundGastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to ide...
详细信息
BackgroundGastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this *** data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic ***, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma ***1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.
The outward appearance of human tongue can reflect changes in blood circulation caused by pathological states, and it has been used as an assisted method for clinical diseases diagnosis for thousands of years in China...
详细信息
The outward appearance of human tongue can reflect changes in blood circulation caused by pathological states, and it has been used as an assisted method for clinical diseases diagnosis for thousands of years in China. The purpose of this study is to observe the changes in the tongue of patients with menstrual-related diseases in hyperspectral imaging and to explore the value of hyperspectral tongue imaging combining with machine learning algorithm (HSI-ML) in the diagnosis of menstrual diseases (MD). Hyperspectral tongue images are collected from 429 patients with five different kinds of MD and 52 participants with normal menstruation. Tongue coating and tongue body spectral characteristics are extracted and used as model input variables to investigate the influence on the modeling *** (Norm), first derivative (1st D), second derivative (2nd D), savitzky-golay smoothing (S-G), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) are used as preprocessing method. Four model algorithms, k-nearest neighbor (KNN), random forest (RF), support vector machines (SVM) and artificial neural network(ANN) are used and compared. Experimental results show that patients of each MD group exhibit significantly lower spectral reflectance of tongue coating and tongue body (P < 0.05) than participants of normal menstruation group. And the modeling results indicate that the "2nd D + S-G + ANN" identification model based on tongue body spectral characteristics yields the optimal performance. Specifically, its accuracy, macro-precision, macro-recall, and macro-F1 score are 0.9729, 0.9697, 0.9703, and 0.97, respectively. It indicates that HSI-ML method with hyperspectral tongue images can provide a rapid and noninvasive detection method for MD screening.
Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current wor...
详细信息
Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machinelearning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions.
We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of m...
详细信息
We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machinelearning. EOS patients from our centre between 2007 and 2019 were retrospectively reviewed. Radiographic parameters include the cervical lordosis (CL), T1 slope, C2-C7 sagittal vertical axis (C2-7 SVA), primary curve Cobb angle, thoracic kyphosis (TK), C7-S1 sagittal vertical axis (C7-S1 SVA) and proximal junctional angle (PJA) were evaluated preoperatively, postoperatively and at the final follow-up. The parameters were analyzed using a t-test and chi 2 test. The machinelearning methodology of a sparse additive machine (SAM) was applied to identify the risk factors that caused the cervical imbalance. 138 patients were enrolled in this study (96 male and 42 female). The mean thoracic curve Cobb angle was 67.00 +/- 22.74 degrees. The mean age at the first operation was 8.5 +/- 2.6yrs. The mean follow-up was 38.48 +/- 10.87 months. CL, T1 slope, and C2-7 SVA increased significantly in the final follow-up compared with the pre-operative data. (P < 0.05). The CL and T1 slope increased more significantly in the group of patients who had proximal junctional kyphosis (PJK) compared with the patients without PJK (P < 0.05). The location of the upper instrumented vertebrae (UIV) and single/dual growing rod had no significant influence on the sagittal cervical parameters (P > 0.05). According to the SAM analysis of machine learning algorithms, Postoperative PJK, more improvement of kyphosis, and T1 slope angle were identified as the risk factors of cervical sagittal imbalance during the treatment of growing rod surgery. The growing rod surgery in EOS significantly affected the cervical sagittal alignment. Postoperative PJK and more improvement of kyphosis and T1 slope angle would lead to a higher incidence of cervical sagittal imbalance.
Purpose: The relationship between macrophages and the progression of abdominal aortic aneurysms (AAA) remains unclear, and effective biomarkers are lacking. In this study, we elucidated the mechanism whereby macrophag...
详细信息
Purpose: The relationship between macrophages and the progression of abdominal aortic aneurysms (AAA) remains unclear, and effective biomarkers are lacking. In this study, we elucidated the mechanism whereby macrophages promote AAA development and identified associated biomarkers, with the goal of developing new targeted therapies and improving patient outcomes. Patients and Methods: Differential expression analysis, weighted gene co-expression network analysis, and single-cell analysis were used to identify macrophage-related genes in an AAA dataset. machine learning algorithms identified THBS1, HCLS1, DMXL2, and ZEB2 as key macrophage-related genes upregulated in AAA;these four hub genes were then used to construct a nomogram as an auxiliary tool for clinical diagnosis. Subsequent downstream single-cell and CellChat analyses were conducted to observe the interactions between macrophages and fibroblasts and analyze potential pathways. Results: Single-cell validation confirmed enhanced THBS1 expression in macrophages in AAA. CellChat analysis revealed enhanced interactions between macrophages and fibroblasts in AAA through THBS1-CD47 signaling. Finally, an analysis of clinical samples from patients with AAA confirmed the high expression of THBS1 and CD47 in AAA and that THBS1 promotes the progression of AAA through the TNF-NF kappa B signaling pathway. Our findings reveal the THBS1-CD47 signaling pathway as a critical mechanism in macrophage-driven AAA progression, highlighting THBS1's potential as a therapeutic target. Conclusion: Our findings highlight THBS1 as a potential driver of macrophage-mediated AAA formation and an important biomarker for AAA diagnosis. The study results would help in improving treatment outcomes in patients with AAA. These findings provide a foundation for the development of diagnostic tools and targeted therapies for AAA, potentially improving early detection and patient outcomes.
Purpose: The aim of this study was to develop and train a machinelearning(ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate ...
详细信息
Purpose: The aim of this study was to develop and train a machinelearning(ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an arthroscopic Bankart repair (ABR). Methods: Data from 14 previously published studies were collected. Inclusion criteria were (1) patients treated with ABR without remplissage for traumatic anterior shoulder instability and (2) a minimum of 2 years follow-up. Risk factors associated with recurrence were identified using bivariate logistic regression analysis. Subsequently, four ML algorithms were developed and internally validated. The predictive performance was assessed using discrimination, calibration and the Brier score. Results: In total, 5591 patients underwent ABR with a recurrence rate of15.4% (n= 862). Age <35 years, participation in contact and collision sports, bony Bankart lesions and full-thickness rotator cuff tears increased the risk of recurrence (all p< 0.05). A single shoulder dislocation (compared to multiple dislocations) lowered the risk of recurrence (p< 0.05). Due to the unavailability of certain variables in some patients, a portion of the patient data had to be excluded before pooling the data set to create the algorithm. A total of 797 patients were included providing information on risk factors associated with recurrence. The discrimination (area under the receiver operating curve)ranged between 0.54 and 0.57 for prediction of recurrence. Conclusion: ML was not able to predict the recurrence following ABR with the current available predictors. Despite a global coordinated effort, the heterogeneity of clinical data limited the predictive capabilities of the algorithm, emphasizing the need for standardized data collection methods in future studies.
Methyldiethanolamine (MDEA) is a widely used solvent in carbon capture processes owing to its high absorption capacity. However, there is a lack of comprehensive predictive tools for estimating CO2 solubility in MDEA-...
详细信息
Methyldiethanolamine (MDEA) is a widely used solvent in carbon capture processes owing to its high absorption capacity. However, there is a lack of comprehensive predictive tools for estimating CO2 solubility in MDEA-based solution. To fulfil this research gap, in the current study, 2969 experimental data pertinent to the CO2 dissolution in MDEA solutions blended with various co-solvent, including water, amines, ionic liquids, electrolytes, etc., have been collected from the literature. The foregoing databank envelop a widespread range of pressures and temperatures. In order to construct robust models, three heuristic soft computing methods, including radial basis function neural network (RBF-NN), gaussian process regression (GPR) and multilayer perceptron neural network (MLP-NN) were employed. Despite the satisfactory performance of all intelligent models, the one designed based on the GPR method gave the superior accuracy with average absolute relative error (AARE) and R2 values of 4.94 % and 97.5 %, respectively, for the testing dataset. Moreover, it estimated more than 91 % of the analyzed samples within +/- 15 error margin. A statistical investigation through the William's plot implied the fact that both the collected databank and the suggested predictive tools benefit from high credibility. The novel models also favorably described the absorption capacity of diverse MDEA-based solutions under a wide range of operating conditions. Finally, the order of significance of influential factors in controlling solubility was determined based on a sensitivity analysis.
PurposeThe choice of the best management for Adult Spine Deformity (ASD) is challenging. Health-related quality of life (HRQoL), comorbidities, symptoms and spine geometry, along with surgical risk and potential resid...
详细信息
PurposeThe choice of the best management for Adult Spine Deformity (ASD) is challenging. Health-related quality of life (HRQoL), comorbidities, symptoms and spine geometry, along with surgical risk and potential residual disability play a role, and a definite algorithm for patient management is lacking. machinelearning allows to analyse complex settings more efficiently than other available statistical tools. Aim of this study was to develop a machine-learningalgorithm that, based on baseline data, would be able to predict whether an ASD patient would undergo surgery or *** evaluation of prospectively collected data. Demographic data, HRQoL and radiographic parameters were collected. Two clustering methods were performed to differentiate groups of patients with similar characteristics. Three models were then used to identify the most relevant variables for management *** from 1319 patients were available. Three clusters were identified: older subjects with sagittal imbalance and high PI, younger patients with greater coronal deformity and no sagittal imbalance, older patients with moderate sagittal imbalance and lower PI. The group of younger patients showed the highest error rate for the prediction (37%), which was lower for the other two groups (20-27%). For all groups, quality of life parameters such as the ODI and the SRS 22 and the Cobb angle of the major curve were the strongest predictors of surgical indication, albeit with different odds ratios in each *** clusters could be identified along with the variables that, in each, are most likely to drive the choice of management.
Research on sentiment analysis has proven to be very useful in public health, particularly in analyzing infectious diseases. As the world recovers from the onslaught of the COVID-19 pandemic, concerns are rising that ...
详细信息
Research on sentiment analysis has proven to be very useful in public health, particularly in analyzing infectious diseases. As the world recovers from the onslaught of the COVID-19 pandemic, concerns are rising that another pandemic, known as monkeypox, might hit the world again. Monkeypox is an infectious disease reported in over 73 countries across the globe. This sudden outbreak has become a major concern for many individuals and health authorities. Different social media channels have presented discussions, views, opinions, and emotions about the monkeypox outbreak. Social media sentiments often result in panic, misinformation, and stigmatization of some minority groups. Therefore, accurate information, guidelines, and health protocols related to this virus are critical. We aim to analyze public sentiments on the recent monkeypox outbreak, with the purpose of helping decision-makers gain a better understanding of the public perceptions of the disease. We hope that government and health authorities will find the work useful in crafting health policies and mitigating strategies to control the spread of the disease, and guide against its misrepresentations. Our study was conducted in two stages. In the first stage, we collected over 500,000 multilingual tweets related to the monkeypox post on Twitter and then performed sentiment analysis on them using VADER and TextBlob, to annotate the extracted tweets into positive, negative, and neutral sentiments. The second stage of our study involved the design, development, and evaluation of 56 classification models. Stemming and lemmatization techniques were used for vocabulary normalization. Vectorization was based on CountVectorizer and TF-IDF methodologies. K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest, Logistic Regression, Multilayer Perceptron (MLP), Naive Bayes, and XGBoost were deployed as learningalgorithms. Performance evaluation was based on accuracy, F1 Score, Precision, and Recall. Our
暂无评论