A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity

Background and Objective: Using echocardiogram data for cardiovascular disease (CVD) can lead to difficulties due to imbalanced datasets, leading to biased predictions. Machine learning models can enhance prognosis accuracy, but their effectiveness is influenced by optimal feature selection and robust classification techniques. This study introduces an event-based self-similarity approach to enhance automatic feature selection approach for imbalanced echocardiogram data. Critical features correlated with disease progression were identified by leveraging self-similarity patterns. This study used an echocardiogram dataset, visual presentations of high-frequency sound wave signals, and data of patients with heart disease who are treated using three treatment methods: catheter ablation, ventricular defibrillator, and drug control-over the course of three years. Methods: The dataset was classified into nine categories and Recursive Feature Elimination (RFE) was applied to identify the most relevant features, reducing model complexity while maintaining diagnostic accuracy. Machine learning classification models, including XGBoost and CATBoost, were trained and evaluated. Results: Both models achieved comparable accuracy values, 84.3% and 88.4%, respectively, under different normalization techniques. To further optimize performance, the models were combined into a voting ensemble, improving feature selection and predictive accuracy. Four essential features-age, aorta (AO), left ventricular (LV), and left atrium (LA)-were identified as critical for prognosis and were found in Random Forest (RF)-voting ensemble classifier. The results underscore the importance of feature selection techniques in handling imbalanced datasets, improving classification robustness, and reducing bias in automated prognosis systems. Conclusions: Our findings highlight the potential of machine learning-driven echocardiogram analysis to enhance patient care by providing accurate, data-driven assessments.

Clues to revising the conventional diagnostic algorithm for endometriosis

Endometriosis is a complex gynecologic disorder characterized primarily by symptoms of pelvic pain, infertility, and altered quality of life. National and international guidelines highlight the diagnostic difficulties and lack of conclusive diagnostic tools for endometriosis. Furthermore, guidelines are becoming questionable at an increasingly rapid rate as new diagnostic techniques emerge. This work aims to provide a knowledge synthesis of the relevance of various diagnostic tools and to assess areas of improvement of conventional algorithms. MEDLINE and Cochrane Library databases were searched from January 2021 to December 2023 using relevant key words. Articles evaluating the diagnostic relevance and performance of various tools were included and independently reviewed by the authors for eligibility. Included studies were assessed using the GRADE and QUADAS-2 tools. Of the 4204 retrieved articles, 26 were included. While anamnesis and clinical examination do contribute to diagnostic accuracy, their level of evidence and impact on the diagnostic process remains limited. Although imaging techniques are recommended to investigate endometriosis, ultrasonography remains highly operator dependent. Magnetic resonance imaging appears to exhibit higher sensitivities than ultrasound. However, concerns persist with regards to the terminology, anatomical definition of lesions, and accuracies of both ultrasound and magnetic resonance imaging. Recently, several biological markers have been studied and cumulative evidence supports the contribution of noncoding RNAs to the diagnosis of endometriosis. Marginal improvements have been suggested for anamnesis, clinical examination, and imaging examinations. Conversely, some biomarkers, including the saliva microRNA signature for endometriosis, have emerged as diagnostic tools which inspire reflection on the revision of conventional diagnostic algorithms.

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning

BACKGROUND

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD.

METHODS

A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive model.

RESULTS

Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables.

CONCLUSIONS

Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.

Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome

BACKGROUND

Neonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease.

METHODS

A total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children's Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise.

RESULTS

Twenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001).

CONCLUSION

The RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Background

Calcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.

Methods

Three microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.

Results

We identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.

Conclusion

Three pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts

Introduction

Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor.

Methods

Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of 137Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.4 Gy/h, were analyzed for radiation-induced short- and long-term effects. Two sample cohorts, i.e., discovery (n = 30) and validation (n = 12), were subjected to RNA sequencing. The pool of the results from those six experiments with shared conditions (1.6 mGy/h; 24 h), together with an earlier time point (0 h), constituted a third cohort (n = 12).

Results

The 100 mGy-exposed cells at all abovementioned dose rates, harvested at 0/24 h and 21 days after exposure, showed no strong gene expression changes. DMXL2, involved in the regulation of the NOTCH signaling pathway, presented a consistent upregulation among both the discovery and validation cohorts, and was validated by qPCR. Gene set enrichment analysis revealed that the NOTCH pathway was upregulated in the pooled cohort (p = 0.76, normalized enrichment score (NES) = 0.86). Apart from upregulated apical junction and downregulated DNA repair, few pathways were consistently changed across exposed cohorts. Concurringly, cell viability assays, performed 1, 3, and 6 days post irradiation, and colony forming assay, seeded just after exposure, did not reveal any statistically significant early effects on cell growth or survival patterns. Tendencies of increased viability (day 6) and reduced colony size (day 21) were observed at 12 mGy/h and 23.4 Gy/min. Furthermore, no long-term changes were observed in cell growth curves generated up to 70 days after exposure.

Discussion

In conclusion, low doses of gamma radiation given at low dose rates had no strong cytotoxic effects on radioresistant VH10 cells.

Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet's disease by leveraging gene microarray data

Behcet's disease (BD) is a chronic inflammatory vasculitis and clinically heterogeneous disorder caused by immunocyte aberrations. Comprehensive research on gene expression patterns in BD illuminating its aetiology is lacking. E-MTAB-2713 downloaded from ArrayExpress was analysed to screen differentially expressed genes (DEGs) using limma. Random forest (RF) and neural network (NN) classification models composed of gene signatures were established using the E-MTAB-2713 training set and subsequently verified using GSE17114. Single sample gene set enrichment analysis was used to assess immunocyte infiltration. After identifying DEGs in E-MTAB-2713, pathogen-triggered, lymphocyte-mediated and angiogenesis- and glycosylation-related inflammatory pathways were discovered to be predominant in BD episodes. Gene signatures from the RF and NN diagnostic models, together with genes enriched in angiogenesis and glycosylation pathways, well discriminated the clinical subtypes of BD manifesting as mucocutaneous, ocular and large vein thrombosis involvement in GSE17114. Moreover, a distinctive immunocyte profile revealed T, NK and dendritic cell activation in BD compared to the findings in healthy controls. Our findings suggested that EPHX1, PKP2, EIF4B and HORMAD1 expression in CD14+ monocytes and CSTF3 and TCEANC2 expression in CD16+ neutrophils could serve as combined gene signatures for BD phenotype differentiation. Pathway genes comprising ATP2B4, MYOF and NRP1 for angiogenesis and GXYLT1, ENG, CD69, GAA, SIGLEC7, SIGLEC9 and SIGLEC16 for glycosylation also might be applicable diagnostic markers for subtype identification.