Least Absolute Shrinkage And Selection Operator Algorithm Research Articles

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis.

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis

BackgroundThis study aimed to identify potential biomarkers for the diagnosis and treatment of osteoporosis (OP).MethodsData sets were downloaded from the Gene Expression Omnibus database, and differentially programmed cell death-related genes were screened. Functional analyses were performed to predict the biological processes associated with these genes. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) machine learning algorithms were used to screen for characteristic genes, and receiver operating characteristics were used to evaluate the diagnosis of disease characteristic gene values. Gene set enrichment analysis (GSEA) and single-sample GSEA were conducted to analyze the correlation between characteristic genes and immune infiltrates. Cytoscape and the Drug Gene Interaction Database (DGIdb) were used to construct the mitochondrial RNA-mRNA-transcription factor network and explore small-molecule drugs. Reverse transcription real-time quantitative PCR (RT-qPCR) analysis was performed to evaluate the expression of biomarker genes in clinical samples.ResultsIn total, 25 differential cell death genes were identified. Among these, two genes were screened using the LASSO, SVM, and RF algorithms as characteristic genes, including BRSK2 and VPS35. In GSE56815, the area under the receiver operating characteristic curve of BRSK2 was 0.761 and that of VPS35 was 0.789. In addition, immune cell infiltration analysis showed that BRSK2 positively correlated with CD56dim natural killer cells and negatively correlated with central memory CD4 + T cells. Based on the data from DGIdb, hesperadin was associated with BRSK2, and melagatran was associated with VPS35. BRSK2 and VPS35 were expectably upregulated in OP group compared with controls (all p < 0.05).ConclusionsBRSK2 and VPS35 may be important diagnostic biomarkers of OP.

MRI-Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma.

The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in targeted therapy for urothelial bladder cancer (UBC). The HER2 status is mainly identified by immunohistochemistry (IHC), preoperative and noninvasive methods for determining HER2 status in UBC remain in searching. To investigate whether radiomics features extracted from MRI using machine learning algorithms can noninvasively evaluate the HER2 status in UBC. Retrospective. One hundred ninety-five patients (age: 68.7 ± 10.5 years) with 14.3% females from January 2019 to May 2023 were divided into training (N = 156) and validation (N = 39) cohorts, and 43 patients (age: 67.1 ± 13.1 years) with 13.9% females from June 2023 to January 2024 constituted the test cohort (N = 43). 3 T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin echo). The HER2 status were assessed by IHC. Radiomics features were extracted from MRI images. Pearson correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were applied for feature selection, and six machine learning models were established with optimal features to identify the HER2 status in UBC. Mann-Whitney U-test, chi-square test, LASSO algorithm, receiver operating characteristic analysis, and DeLong test. Three thousand forty-five radiomics features were extracted from each lesion, and 22 features were retained for analysis. The Support Vector Machine model demonstrated the best performance, with an AUC of 0.929 (95% CI: 0.888-0.970) and accuracy of 0.859 in the training cohort, AUC of 0.886 (95% CI: 0.780-0.993) and accuracy of 0.846 in the validation cohort, and AUC of 0.712 (95% CI: 0.535-0.889) and accuracy of 0.744 in the test cohort. MRI-based radiomics features combining machine learning algorithm provide a promising approach to assess HER2 status in UBC noninvasively and preoperatively. 2 TECHNICAL EFFICACY: Stage 3.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

BackgroundPreoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset.MethodsA total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test.ResultsSeven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001).ConclusionsThe combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.

High-precision robotic kinematic parameter identification and positioning error compensation method for industrial robot

Abstract A method based on laser tracing multi-station measurement technology is proposed in this paper. The method identifies the robotic kinematic parameters and compensates for the absolute positioning errors of industrial robots to improve the absolute positioning accuracy further. The position coordinates of industrial robots are typically measured using laser tracking devices. In this study, the measurement accuracy of an industrial robot is further enhanced using laser tracer multi-station measurement technology. Additionally, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the robotic kinematic parameters. Compared with the commonly used least squares algorithm, the LASSO algorithm improved the parameter identification accuracy and the compensation effect on absolute positioning errors. A position error model was established based on the parameters of the modified Denavit–Hartenberg model of an industrial robot. Using the LASSO algorithm, the robotic kinematic parameters were accurately identified, and the original data in the controller were replaced to compensate for the geometric errors of the industrial robot. In the compensation experiments, after implementing the geometric error compensation, the average absolute positioning error of the industrial robot decreased by 41.15%, demonstrating a significant improvement in the absolute positioning accuracy.

Transcriptomic analysis reveals the potential crosstalk genes and immune relationship between Crohn's disease and atrial fibrillation.

At present, there is a paucity of research on the link between Crohn's disease (CD) and atrial fibrillation (AF). Nevertheless, both ailments are thought to entail inflammatory and autoimmune processes, and emerging evidence indicates that individuals with CD may face an elevated risk of AF. To shed light on this issue, our study seeks to explore the possibility of shared genes, pathways, and immune cells between these two conditions. We retrieved the gene expression profiles of both CD and AF from the Gene Expression Omnibus (GEO) database and subjected them to analysis. Afterward, we utilized the weighted gene co-expression network analysis (WGCNA) to identify shared genes, which were then subjected to further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Furthermore, we employed a rigorous analytical approach by screening hub genes through both least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM), and subsequently constructing a receiver operating characteristic (ROC) curve based on the screening outcomes. Finally, we utilized single-sample gene set enrichment analysis (ssGSEA) to comprehensively evaluate the levels of infiltration of 28 immune cells within the expression profile and their potential association with the shared hub genes. Using the WGCNA method, we identified 30 genes that appear to be involved in the pathological progression of both AF and CD. Through GO enrichment analysis on the key gene modules derived from WGCNA, we observed a significant enrichment of pathways related to major histocompatibility complex (MHC) and antigen processing. By leveraging the intersection of LASSO and SVM algorithms, we were able to pinpoint two overlapping genes, namely CXCL16 and HLA-DPB1. Additionally, we evaluated the infiltration of immune cells and observed the upregulation of CD4+ and CD8+ T cells, as well as dendritic cells in patients with AF and CD. By employing bioinformatics tools, we conducted an investigation with the objective of elucidating the genetic foundations that connect AF and CD. This study culminated in the identification of CXCL16 and HLA-DPB1 as the most substantial genes implicated in the development of both disorders. Our findings suggest that the immune responses mediated by CD4+ and CD8+ T cells, along with dendritic cells, may hold a crucial role in the intricate interplay between AF and CD.

Development and validation of a nomogram for predicting pulmonary infection in patients receiving immunosuppressive drugs.

Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients' prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was evaluated. Results: Among the patients taking immunosuppressive medication, PI was observed in 548 (6.9%). The median time of PI occurrence after immunosuppressive therapy was 123.0 (interquartile range: 63.0, 436.0) days. Thirteen statistically significant independent predictors (sex, age, hypertension, DM, malignant tumour, use of biologics, use of CNIs, use of methylprednisolone at 500mg, use of methylprednisolone at 40mg, use of methylprednisolone at 40mg total dose, use of oral glucocorticoids, albumin level, and haemoglobin level) were screened using the LASSO algorithm and multivariate COX regression analysis. The PIP model built on these features performed reasonably well, with the developing C-index of 0.87 (sensitivity: 85.4%; specificity: 81.0%) and validation C-indices of 0.837, 0.829, 0.832 and 0.830 for predicting 90-, 180-, 270- and 360-day PI probability, respectively. The decision curve analysis (DCA) and calibration curves displayed excellent clinical utility and calibration performance of the nomogram. Conclusion: The PIP model presented herein could aid in the prediction of PI risk in individual patients who receive immunosuppressive treatment and help personalise clinical decision-making.

A data-driven B-spline-enhanced Kriging method for uncertainty quantification based on Bayesian compressive sensing

Kriging is a powerful surrogate method for fitting smooth functions, and has been widely used in uncertainty quantification. However, for non-smooth functions, the performance of Kriging may be unsatisfactory. To overcome this drawback, we combine the advantages of spline functions and the Kriging model, and propose a data-driven B-spline-enhanced Kriging method (DDBSEK). Firstly, we establish the data-consistent orthogonal B-spline basis functions with the aid of the sample data of the input variables and replace the trend terms of the traditional Kriging model with these orthogonal B-spline bases. Then, by leveraging the fast marginal likelihood maximization algorithm and the Bayesian compressive sensing technique, an improved analytical Bayesian LASSO (least absolute shrinkage and selection operator) algorithm is developed to retain the active orthogonal B-spline bases. The proposed DDBSEK is validated using two types of numerical examples and one implicit engineering example. Results show that the proposed method is superior to the B-spline function model or the Kriging model alone.

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection.

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

The value of CT-based radiomics in predicting the prognosis of acute pancreatitis

PurposeEarly judgment of the progress of acute pancreatitis (AP) and timely intervention are crucial to the prognosis of patients. The purpose of this study was to investigate the application value of CT-based radiomics of pancreatic parenchyma in predicting the prognosis of early AP.Materials and methodsThis retrospective study enrolled 137 patients diagnosed with AP (95 cases in the progressive group and 42 cases in the non-progressive group) who underwent CT scans. Patients were randomly divided into a training set (n = 95) and a validation set (n = 42) in a ratio of 7: 3. The region of interest (ROI) was outlined along the inner edge of the pancreatic parenchyma manually, and the Modified CT Severity Index (MCTSI) was assessed. After resampling and normalizing the CT image, a total of 2,264 radiomics features were extracted from the ROI. The radiomics features were downscaled and filtered using minimum redundancy maximum correlation (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression, in turn, and the more optimal subset of radiomics features was selected. In addition, the radiomics score (rad-score) was calculated for each patient by the LASSO method. Clinical data were also analyzed to predict the prognosis of AP. Three prediction models, including clinical model, radiomics model, and combined clinical–radiomics model, are constructed. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curve analysis. The DeLong test was employed to compare the differences between the ROC curves. The decision curve analysis (DCA) is used to assess the net benefit of the model.ResultsThe mRMR algorithm and LASSO regression were used to select 13 radiomics features with high values. The rad-score of each texture feature was calculated to fuse MCTSI to establish the radiomics model, and both the clinical model and clinical–radiomics model were established. The clinical–radiomics model showed the best performance, the AUC and 95% confidence interval, accuracy, sensitivity, and specificity of the clinical–radiomics model in the training set were 0.984 (0.964–1.000), 0.947, 0.955, and 0.931, respectively. In the validation set, they were 0.942 (0.870–1.000), 0.929, 0.966, and 0.846, respectively. The Delong test showed that the predictive efficacy of the clinical–radiomics model was higher than that of the clinical model (Z = 2.767, p = 0.005) and the radiomics model (Z = 2.033, p = 0.042) in the validation set. Decision curve analysis demonstrated higher net clinical benefit for the clinical–radiomics model.ConclusionThe pancreatic parenchymal CT clinical–radiomics model has high diagnostic efficacy in predicting the progression of early AP patients, which is significantly better than the clinical or radiomics model. The combined model can help identify and determine the progression trend of patients with AP and improve the prognosis and survival of patients as early as possible.

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2=0.99, RMSE=0.61) and measured dataset (R2=0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Integrating bulk and single-cell RNA sequencing data to establish necroptosis-related lncRNA risk model and analyze the immune microenvironment in hepatocellular carcinoma

BackgroundThe increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC). MethodslncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines. ResultsWe constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score (P < 0.05). Combined with the clinical characteristics and risk score of HCC, Nomogram was drawn for reference in clinical practice. In addition, immune cell infiltration analysis and single cell RNA sequencing analysis showed that a low level of immune infiltration was observed in patients at high risk and that there was a significant correlation between NRlncRNAs and macrophages. The results of RT-qPCR also showed that 8 necroptosis-related lncRNAs were highly expressed in HCC cell lines and human liver cancer tissues. ConclusionThis prognostic signature based on the necroptosis-related lncRNAs may provide meaningful clinical insights for the prognosis and immunotherapy responses in patients with HCC.

Interpretable CT radiomics model for invasiveness prediction in patients with ground-glass nodules

To evaluate the performance of an interpretable computed tomography (CT) radiomic model in predicting the invasiveness of ground-glass nodules (GGNs). The study was conducted retrospectively from 1 August 2017 to 1 August 2022, at three different centres. Two hundred and thirty patients with GGNs were enrolled at centre I as a training cohort. Centres II (n=157) and III (n=156) formed two external validation cohorts. Radiomics features extracted based on CT were reduced by a coarse-fine feature screening strategy. A radiomic model was developed through the use of the LASSO (least absolute shrinkage and selection operator) and XGBoost algorithms. Then, a radiological model was established through multivariate logistic regression analysis. Finally, the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). The radiomic XGBoost model outperformed the radiomic logistic model and radiological model in assessing the invasiveness of GGNs. The area under the curve (AUC) values for the radiomic XGBoost model were 0.885 (95% confidence interval [CI] 0.836-0.923), 0.853 (95% CI 0.790-0.906), and 0.838 (95% CI 0.773-0.902) in the training and the two external validation cohorts, respectively. The SHAP method allowed for both a quantitative and visual representation of how decisions were made using a given model for each individual patient. This can provide a deeper understanding of the decision-making mechanisms within the model and the factors that contribute to its prediction effectiveness. The present interpretable CT radiomics model has the potential to preoperatively evaluate the invasiveness of GGNs. Furthermore, it can provide personalised, image-based clinical-decision support.

Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.

The aim of the study was to investigate the feasibility of discriminating between clear-cell renal cell carcinoma (ccRCC) and non-clear-cell renal cell carcinoma (non-ccRCC) via radiomics models and nomogram. The retrospective study included 147 patients (ccRCC=100, non-ccRCC=47) who underwent enhanced CT before surgery. CT images of the corticomedullary phase (CMP) were collected and features from the images were extracted. The data were randomly grouped into training and validation sets according to 7:3, and then the training set was normalized to extract the normalization rule for the training set, and then the rule was applied to the validation set. First, the T-test, T'-test or Wilcoxon rank-sum test were executed in the training set data to keep the statistically different parameters, and then the optimal features were picked based on the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning (ML) models were trained to differentiate ccRCC from noccRCC, rad+cli nomogram was constructed based on clinical factors and radscore (radiomics score), and the performance of the classifier was mainly measured by area under the curve (AUC), accuracy, sensitivity, specificity, and F1. Finally, the ROC curves and radar plots were plotted according to the five performance parameters. 1130 radiomics features were extracted, there were 736 radiomics features with statistical differences were obtained, and 4 features were finally selected after the LASSO algorithm. In the validation set of this study, three of the five ML models (logistic regression, random forest and support vector machine) had excellent performance (AUC 0.9-1.0) and two models (adaptive boosting and decision tree) had good performance (AUC 0.7-0.9), all with accuracy ≥ 0.800. The rad+cli nomogram performance was found excellent in both the training set (AUC = 0.982,0.963-1.000, accuracy=0.941) and the validation set (AUC = 0.949,0.885-1.000, accuracy=0.911). The random forest model with perfect performance (AUC = 1, accuracy=1) was found superior compared to the model performance in the training set. The rad+cli nomogram model prevailed in the comparison of the model's performance in the validation set. The ML models and nomogram can be used to identify the relatively common pathological subtypes in clinic and provide some reference for clinicians.

Bioinformatics analysis to screen the key genes in pediatric Chronic Active Epstein-Barr Virus Infection.

Chronic active EBV infection (CAEBV) is associated with poor prognosis and high mortality. We performed bioinformatics analysis to screen out key genes associated with CAEBV. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene module which was most correlated with pediatric CAEBV. Furthermore, the differentially expressed genes (DEGs) between pediatric acute infectious mononucleosis (AIM) and pediatric CAEBV were investigated. Least absolute shrinkage and selection operator (LASSO) and random forest then were performed to identify the key variables associated with pediatric CAEBV. We also explored the correlation between these hub genes with EBV infection related pathway and immune cell abundance. Compared with pediatric AIM, 1561 DEGs were up-regulated in pediatric CAEBV, and these genes were mainly enriched in inflammatory response and inflammation-related pathways. WGCNA analysis showed that genes in blue module were mostly related to pediatric CAEBV. Genes in the blue module and DEGs are intersected to get 174 genes and these genes are also enriched in inflammatory response-related pathways. The key CAEBV-related genes were selected from these 174 genes by applying the random Forest and LASSO algorithm, resulting in TPST1, TNFSF8 and RAB3GAP1. These three genes showed good diagnostic performance in distinguishing pediatric CAEBV from pediatric AIM. Furthermore, Cibersort and GSEA analysis indicated that these three genes were positively correlated with myeloid cell enrichment and persistent EBV infection pathway, respectively. Our finding systematically analyzed the difference between AIM and CAEBV and identified TPST1, TNFSF8 and RAB3GAP1 were the key genes in the development of CAEBV.

Differentiation of Sinonasal NKT From Diffuse Large B-Cell Lymphoma Using Machine Learning and MRI-Based Radiomics.

The aim of this study was to construct and validate a noninvasive radiomics method based on magnetic resonance imaging to differentiate sinonasal extranodal natural killer/T-cell lymphoma from diffuse large B-cell lymphoma. We collected magnetic resonance imaging scans, including contrast-enhanced T1-weighted imaging and T2-weighted imaging, from 133 patients with non-Hodgkin lymphoma (103 sinonasal extranodal natural killer/T-cell lymphoma and 30 diffuse large B-cell lymphoma) and randomly split them into training and testing cohorts at a ratio of 7:3. Clinical characteristics and image performance were analyzed to build a logistic regression clinical-image model. The radiomics features were extracted on contrast-enhanced T1-weighted imaging and T2-weighted imaging images. Maximum relevance minimum redundancy, selectKbest, and the least absolute shrinkage and selection operator algorithms (LASSO) were applied for feature selection after balancing the training set. Five machine learning classifiers were used to construct the single and combined sequences radiomics models. Sensitivity, specificity, accuracy, precision, F1score, the area under receiver operating characteristic curve, and the area under precision-recall curve were compared between the 15 models and the clinical-image model. The diagnostic results of the best model were compared with those of 2 radiologists. The combined sequence model using support vector machine proves to be the best, incorporating 7 features and providing the highest values of specificity (0.903), accuracy (0.900), precision (0.727), F1score (0.800), and area under precision-recall curve (0.919) with relatively high sensitivity (0.889) in the testing set, along with a minimum Brier score. The diagnostic results differed significantly ( P < 0.05) from those of radiology residents, but not significantly ( P > 0.05) from those of experienced radiologists. Magnetic resonance imaging based on machine learning and radiomics to identify the type of sinonasal non-Hodgkin lymphoma is effective and has the potential to help radiology residents for diagnosis and be a supplement for biopsy.

Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model

The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears.

A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis.

Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis. A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx. A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results. In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.