Least Absolute Shrinkage And Selection Operator Research Articles

Development and validation of a predicting model for risk of mortality in heart failure patients with mildly reduced ejection fraction post discharge

Abstract Background Prognostic prediction for heart failure with mildly reduced ejection fraction (HFmrEF) patients remains challenging. We aimed to create and validate a mortality risk prediction model for HFmrEF patients at 6-month, 1-year, and 3-year post discharge. Methods Clinical data of 1691 HFmrEF patients registered in the heart failure registry from 2015 to 2020 at the Heart Center of our hospital were analyzed. Patients were assigned into a training (1183 patients) and validation cohort (508 patients) at 7:3 ratio. Predictive variables for mortality at various period post discharge were identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The predicting model was then established based on these variables. Model performance was evaluated with ROC curve and Decision Curve Analysis (DCA). Results Eight predictors were identified including age, NT-proBNP, hemoglobin levels, use of beta-blockers, ACEI/ARB, invasive ventilation, PCI procedures, and pulmonary artery systolic pressure values. The model achieved a C-index of 0.748 (training set) and 0.755 (validation set). Area Under the Curve (AUC) for training set at 6 months, 1 year, and 3 years were 0.813, 0.784, and 0.774, and AUC for validation set were 0.775, 0.744, and 0.770, respectively. The DCA analysis confirmed the favorable net benefit of the established nomogram model. Conclusion This predicting model might be used for post-discharge risk stratification and individual decision-making of post-discharge management in HFmrEF patients.

Periodontal conditions and salivary microbiota are potential indicators to distinguish silicosis: an exploratory study

BackgroundSilicosis has always been a serious global occupational health problem. Oral microbiota plays important roles in the development of lung disease. However, few studies have investigated the relationship between periodontal conditions, oral bacteria and silicosis disease.MethodA single-center and cross-sectional study was conducted in 2019 in Sichuan Province, China, including a small sample of silicosis patient group and healthy control group. Demographic data and periodontal examinations measured by clinical attachment loss (CAL), bleeding on probing (BOP) and periodontal pocket (PD) were collected from each participant. Phenotypic changes were detected by histopathological staining. Next-generation sequencing targeting 16S ribosomal RNA was targeted to decipher the salivary microbiome of the two groups. Random forest, Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and multivariable logistic regression analysis were conducted to find potential indicators to distinguish silicosis.ResultsIn general, 29 male healthy controls and 24 male silicosis patients were included. The proportion of CAL ≥ 3 mm in silicosis group was greater than control group, while the proportion of BOP (+) and PD ≥ 4 mm was reduced in silicosis group. The α-smooth muscle actin and fibronectin expression increased in gingiva of patients. The composition of salivary microbiota exhibited significant differences between the two groups, with silicosis patients demonstrating a lower diversity of salivary microbiota. Genus of Aggregatibacter [odds ratio (OR) = 0.000, p = 0.003] and Catonella (OR = 0.000, p = 0.049) were identified as biomarkers to distinguish silicosis.ConclusionsThe silicosis group exhibited worse CAL, improved BOP and PD, which may be related to the gingival fibrosis found in this study. The composition of the oral microbiota underwent significant changes, accompanied by a decrease in diversity, in patients with silicosis. Our study indicates that respirable crystalline silica exposure affects oral health, and alterations of oral microbiota might be implicated in silicosis. We primarily identified Aggregatibacter and Catonella as the potential indicators to distinguish silicosis patients from healthy controls.

Expression of lipid-metabolism genes is correlated with immune microenvironment and predicts prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. This study was aimed to identify a lipid metabolism-related signature associated with the HCC microenvironment to improve the prognostic prediction of HCC patients. Clinical information and expression profile data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, including the GEO dataset GSE76427. The gene expression profile of lipid metabolism was downloaded from Molecular Signatures Database (MSigDB) database. The infiltrating immune cells were estimated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE), MCP-counter, and TIMER algorithms. Functional analysis, including Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) were performed to elucidate the underlying mechanisms. The prognostic risk model was performed by Least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis. Two distinct subgroups of survival were identified. Better prognosis was associated with high immune score, high abundance of immune infiltrating cells, and high immune status. GO and KEGG analysis showed that differentially expressed genes (DEGs) between the two subgroups were mainly enriched in immune related pathways. GSEA analysis suggested that the expression of lipid metabolism related genes (LMRGs) was related to dysregulation of immune in the high-risk group. Risk models and clinical features based on LMRGs predicted HCC prognosis. This study indicated that the lipid metabolism-related signature was important for the prognosis of HCC. The expression of LMRGs was related to the immune microenvironment of HCC patients and could be used to predict the prognosis of HCC.

A practical risk model based on Lasso-Cox regression for angina with nonobstructive coronary arteries (ANOCA) patients

Abstract Background Coronary flow reserve (CFR) reduction is currently one of the main criteria for risk-stratifying patients with angina and nonobstructive coronary artery disease (ANOCA). However, CFR depends on resting and vasodilated coronary hemodynamics, and a theoretical concern remains that physiological changes may inadvertently affect the result. Purpose We aim to fit different risk models for patients with ANOCA, which further provides applicable guidance to clinical doctors for their patients. Methods Between May 2018 and October 2023, 251 consecutive patients who were diagnosed as ANOCA were enrolled from Qilu Hospital of Shandong University, and the median follow-up was 800 (493, 1221) days. Demographic characteristics, cardiovascular risk factors, cardiac structure and function index, echocardiography-derived coronary flow velocity, coronary flow velocity-time integral, and CFR were collected. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses were used to screen for significant parameters that were included to develop a novel predictive nomogram model. Receiver operating characteristic curve and calibration curve were used to evaluate the performance and clinical utility of the nomogram. Results In LASSO regression selection, five variables, diabetes, global work efficiency (GWE), global wasted work (GWW), decreased hyperemic coronary flow velocity (<0.63m/s), and peak flow velocity-derived CFR<2.5 (Figure 1) showed their correlation to the occurrence of major adverse cardiovascular events (MACE). Univariate Cox regression analysis shows that diabetes (hazard ratio [HR] = 4.108; 95% confidence interval [CI], 2.296–7.350; p = 0.001), GWE (HR = 0.890; 95% CI, 0.856–0.926; p = 0.001), GWW (HR = 1.005; 95% CI, 1.004–1.007; p = 0.012), decreased hyperemic coronary flow velocity (HR =3.405; 95% CI, 1.730–6.702; p = 0.001) and peak flow velocity-derived CFR<2.5 (HR =3.456; 95% CI, 1.898–6.292; p = 0.001) were predictors of recurrent chest pain. Multivariate analysis showed that the association of diabetes (HR = 2.735; 95% CI, 1.452–5.152; p = 0.002), GWW (HR = 1.004; 95% CI, 1.001–1.007; p = 0.010), and peak flow velocity-derived CFR<2.5 (HR = 2.650; 95% CI, 1.306–5.380; p = 0.007) with MACE remained statistically significant after adjusting for other variables. A nomogram incorporated the predictors mentioned above; it is presented in Fig.2. Internal validation showed that the model has good predictive performance. The areas under the curve (AUCs) for prediction of the risk of MACE was 0.849 (Fig.3). The calibration curve for predicting chest pain recurrence shows a good agreement between prediction and observation (Fig.4). Conclusions Diabetes, GWE, GWW, decreased hyperemic coronary flow velocity (<0.63m/s), and peak flow velocity-derived CFR<2.5 are significant in identifying the occurrence risk of MACE for ANOCA patients.

A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study

ObjectiveTo develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation.MethodsIn this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of established ML models was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley additive explanation (SHAP) was used to analyze the interpretability of ML models.ResultsA total of 1218 radiomics features were extracted from the nephrographic phase CT images, and 11 features were filtered for constructing ML models. In the test set, the AUCs of LR, SVM, KNN, XGBoost, and RF were 0.803, 0.709, 0.679, 0.794, and 0.815, with corresponding accuracies of 71.7%, 69.8%, 60.4%, 75.5%, and 75.5%, respectively. RF was identified as the optimal classifier. SHAP analysis showed that texture features (gray level size zone matrix and gray level co-occurrence matrix) were significant predictors of HER2 status.ConclusionsThe radiomics-based interpretable ML model provides a noninvasive tool to predict the HER2 status of BCa with satisfactory discriminatory performance.Critical relevance statementAn interpretable radiomics-based machine learning model can preoperatively predict HER2 status in bladder cancer, potentially aiding in the clinical decision-making process.Key PointsThe CT radiomics model could identify HER2 status in bladder cancer.The random forest model showed a more robust and accurate performance.The model demonstrated favorable interpretability through SHAP method.Graphical

Machine Learning techniques for predicting inpatient probability and LOS from EU Injury Database

Abstract Background Unlike statistical inference Machine Learning (ML) makes repeatable predictions without prior assumptions about the underlying relationships among variables. The Full Data Set (FDS) of the EU-IDB (European Injury DataBase) consent to explore risk of hospitalization due to injuries using many predictors. The Length Of Stay of patients in hospital (LOS) is a relevant proxy of complexity of treatment and resources consumption. Methods The IDB-FDS provides more than 3.800.000 ED records, in years 2008-19 for 19 Countries. LASSO (Least Absolute Shrinkage and Selection Operator) cross-validated linearized regression technique (linear for LOS) was used for variable selection and parameter regularization. Inpatients are those admitted or transferred to hospital. Days of hospitalization were used for LOS. A cross-validated Generalized Linear Model was performed on 5 folds randomly sampled assigning 80% of records to training and 20% to testing samples. Results The strongest predictors of hospital admission risk, selected by the model were in order of importance: EUROCOST-39 diagnoses categories, Age Group, Intent, Mechanism Of Injury, Activity When Injured, Transport Injury Event, Sex Of Patient, Place Of Occurrence. EUROCOST-39 represents 61,9% of explained variability and age group 19,4%. The strongest predictors of LOS were substantially the same: EUROCOST-39 86,2% of explained variability and age group 8,6%. Conclusions The main part of variability in the ML model is explained by diagnoses reclassified according to a disability standardization method. For instance, in the maximum training sample risk of hospitalization ranges from odd 0,76% for hand/fingers sprain up to 154,02% for brain concussion. In the median sample LOS ranges from 0,45 days for strain of hand/fingers up to 11,65 for multi-trauma. A combination of more disabling injury, older age and mechanism of injury (i.e. threat to breathing) increases enormously the risk of hospitalization and LOS. Key messages • Machine Learning techniques applied on EU-IDB can provide identification of relevant risk factors of hospital admission from injuries for targeting preventive measures and organizing health care. • Artificial Intelligence consents to analyse big amount of data as dimension or analytical detail of information for identifying patterns and predictors of specific indicators for injuries or diseases.

Construction of an inflammatory, nutritional, and metabolic prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning-based approach

Abstract Background Chronic inflammation, malnutrition, and metabolic syndrome contribute to the underlying mechanisms of heart failure with preserved ejection fraction (HFpEF). Inflammatory, nutritional, and metabolic biomarkers often interact with each other and affect the prognosis of patients. Purpose We aimed to identify the most predictive indicators from a pool of 21 biomarkers encompassing inflammation, nutritional status, as well as lipid, glucose, thyroid, and uric acid metabolism. Subsequently, predictive biomarkers were used to develop a risk score to enhance risk prediction for patients with HFpEF. Methods We included patients diagnosed with HFpEF and without infective or systemic disease. The primary outcome was all-cause mortality. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression to screen the biomarkers. A machine learning-based approach was utilized to fine-tune the optimal model parameters. Additionally, we generated 1000 bootstrapping datasets to identify biomarkers recurring above a 95% frequency in repetitions, which were then employed to formulate the biomarker risk score. The discrimination of incorporating the risk score into the basic model was evaluated using 100 iterations of 5-fold cross-validation to assess the predictive efficacy. Results Among the 1311 included patients (with a median age of 64 years and 42.6% female), 363 patients (27.7%) experienced mortality over a median follow-up period of 2.7 years. The final risk score model was derived from 5 biomarkers—albumin (ALB), red blood cell distribution width-standard deviation (RDW-SD), lymphocytes, triiodothyronine (T3), and uric acid—each selected in over 95% of 1000 bootstrapping iterations. Integration of this risk score into the basic model notably enhanced discrimination (∆C-index=0.015, 95% CI 0.005-0.023) and reclassification (IDI: 3.3%, 95% CI 1.7%-5.8%; NRI: 15.2%, 95% CI 6.5%-22.5%) for risk stratification. In time-dependent receiver operating characteristic (ROC) analysis, the mean area under the curve (AUC) of the risk score was 0.688, 0.710, and 0.738 at 1, 3, and 5 years post-discharge, respectively, in the cross-validation sets. Incorporating the risk score into the basic model significantly improved the AUC at 1, 3, and 5 years (DeLong test P-value<0.05). Furthermore, in multivariable Cox regression, the risk score demonstrated an independent association with all-cause mortality (HR: 1.81, 95% CI 1.51-2.16, P<0.001 per 1 score increase). Conclusions A risk score derived from 5 commonly used inflammatory, nutritional, thyroid and uric acid metabolic biomarkers can effectively identify high-risk patients with HFpEF. This approach supports the development of potential individualized management strategies for patients with HFpEF.Study population and model constructionPredictive performance of the risk score

Screening of m6A-associated ferroptosis-related genes in atherosclerosis based on WGCNA

BackgroundN6-methyladenosine (m6A) has been shown to mediate ferroptosis but its role in atherosclerosis (AS) is unclear.MethodsDifferentially expressed m6A-associated ferroptosis-related genes (DE-m6A-Ferr-RGs) were obtained using differential expression analysis and Pearson correlation analysis. Weighted gene co-expression network analysis (WGCNA) was also performed. The intersection of the module genes and the DE-m6A-Ferr-RGs were recorded as candidate m6A-Ferr-related signature genes. Finally, the m6A-Ferr-related signature genes were screened using least absolute shrinkage and selection operator (LASSO) analysis. Expression validation, receiver operating characteristic ( mapping, and immune correlation analysis were also performed based on the m6A-Ferr-related signature genes. The expression of m6A-Ferr-related signature genes was further validated using a real-time polymerase chain reaction (RT-qPCR).ResultsIn total, 6,167 differentially expressed genes were intersected with 24 m6A- and 259 ferroptosis-related genes, respectively, resulting in 113 DE-m6A-Ferr-RGs obtained using Pearson’s correlation analysis. The module genes obtained from the WGCNA and the 113 DE-m6A-Ferr-RGs were intersected to obtain 48 candidate m6A-Ferr-related signature genes. LASSO analysis was performed and six m6A-Ferr-related signature genes were screened. In addition, the area under the curve values of all six m6A-Ferr-related signature genes were greater than 0.7, indicating that they had potential diagnostic value. Furthermore, the RT-qPCR results revealed that the expression of SLC3A2, NOX4, and CDO1 was consistent with the transcriptome level. Moreover, there was a significant difference in two types of immune cells between the AS and control groups. Naive B cells, CD8+ T cells, regulatory T cells, and activated natural killer cells were positively correlated with CDO1 and NOX4 but negatively correlated with ATG7, CYBB, and SLC3A2.ConclusionIn total, three m6A-Ferr-related signature genes (NOX4, CDO1, and SLC3A2) were obtained through a series of bioinformatics analyses and an RT-qPCR.

Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013–2023

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

Development and validation of a novel endoplasmic reticulum stress-related lncRNAs signature in osteosarcoma

Osteosarcoma (OS) is a cancerous tumor, and its development is greatly influenced by long non-coding RNA (lncRNA). Endoplasmic reticulum stress (ERS) is an essential biological defense process in cells and contributes to the progression of tumors. However, the exact mechanisms remain elusive. This study aims to develop a signature of lncRNAs associated with ERS in OS. This signature will guide the prognosis prediction and the determination of appropriate treatment strategies. The UCSC Xena database collected transcriptional and clinical data of OS and muscle, after identifying ERS differentially expressed genes, we utilized correlation analysis to determine the endoplasmic reticulum stress lncRNAs (ERLs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analysis were utilized to develop an ERLs signature. To clarify the fundamental mechanisms controlling gene expression in low and high-risk groups, Gene Set Variation Analysis (GSVA) were conducted. In addition, the distinction between the two groups regarding drug sensitivity and immune-related activity was investigated to determine the immunotherapy effects. Utilizing RT-qPCR, the expression of model lncRNAs in OS cell lines was ascertained. The functional analysis of LINC02298 was carried out through in vitro experiments and pan-cancer analysis. This study successfully constructed an ERLs prognostic signature for OS, which comprised 5 lncRNAs (AC023157.3, AL031673.1, LINC02298, LINC02328, SNHG26). The risk signature predicted overall survival in patients with OS and was confirmed by assessing the validation and whole cohorts. Further, it was discovered that individuals classified as high-risk displayed suppressed immune activation, decreased infiltration of immune cells, and decreased responsiveness to immunotherapy. The RT-qPCR showed that the constructed risk prognosis model is reliable. Experimental validation has demonstrated that LINC02298 can promote OS cells’ invasion, migration, and proliferation. In addition, LINC02298 exhibited significant differential expression in many types of cancer. Moreover, LINC02298 is an important biomarker in a variety of tumors. This study established a novel ERLs signature, which successfully predicted the prognosis of OS. The function of LINC02298 in OS was elucidated via in vitro experiments. Therefore, it offers new opportunities for predicting the clinical prognosis of OS and establishes the basis for targeted therapy in OS.

Characterizing adipocytokine-related signatures for prognosis prediction in prostate cancer

BackgroundProstate cancer (PCa) is a prevalent malignant tumor in males, with a significant incidence of biochemical recurrence (BCR) despite advancements in treatment. Adipose tissue surrounding the prostate, known as periprostatic adipose tissue (PPAT), contributes to PCa invasion through adipocytokine production. However, the relationship between adipocytokine-related genes and PCa prognosis remains understudied. This study was conducted to provide a theoretical basis and serve as a reference for the use of adipocytokine-related genes as prognostic markers in PCa.MethodsTranscriptome and survival data of PCa patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential gene expression analysis was conducted using the DESeq2 and limma packages. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A prognostic model was developed and validated utilizing receiver operating characteristic (ROC) and Kaplan-Meier (K-M) curves. Assessments of immune cell infiltration and drug sensitivity were also carried out. Subsequently, the function of BNIP3L gene in PCa was verified.ResultsA total of 47 adipocytokine-related differentially expressed genes (DEGs) were identified. Five genes (PPARGC1A, APOE, BNIP3L, STEAP4, and C1QTNF3) were selected as prognostic markers. The prognostic model demonstrated significant predictive accuracy in both training and validation cohorts. Patients with higher risk scores exhibited poorer survival outcomes. Immune cell infiltration analysis revealed that the high-risk group had increased immune and ESTIMATE scores, while the low-risk group had higher tumor purity. In vitro experiments confirmed the suppressive effects of BNIP3L on PCa cell proliferation, migration, and invasion.ConclusionThe prognostic model independently predicts the survival of patients with PCa, aiding in prognostic prediction and therapeutic efficacy. It expands the study of adipocytokine-related genes in PCa, presenting novel targets for treatment.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer

ObjectiveTo establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.MethodsA total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA).ResultsITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models.ConclusionThe clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis

Pulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. In this study, plasma samples were collected from 17 patients diagnosed with idiopathic pulmonary arterial hypertension (IPAH) and 20 healthy controls. Plasma metabolomic profiling was performed by high-performance liquid chromatography-mass spectrometry. Gene profiles of PAH patients were obtained from the GEO database. Key differentially expressed metabolites (DEMs) and metabolism-related genes were subsequently identified using machine learning algorithms. Twenty differential plasma metabolites associated with IPAH were identified (VIP score > 1 and p < 0 0.05), and enrichment analysis revealed the arginine biosynthesis pathway as the most altered pathway. Using machine learning models, including least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM), we extracted key metabolites that correlated with clinical phenotypes. Our results suggested that five metabolites, kynurenine, homoserine, tryptophan, AMP, and spermine, are potential biomarkers for IPAH. Bioinformatics analysis also identified 3 metabolism-related genes, MAPK6, SLC7A11 and CDC42BPA, that are strongly correlated with pulmonary hypertension, demonstrating strong predictive power and clinical relevance. Our findings revealed some key genes associated with metabolism in PH, and provided crucial information about complex metabolic reprogramming signals and may lead to the identification of useful metabolic biomarkers for the diagnosis of PAH.

Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics

BackgroundSentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.MethodsThis retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.ConclusionThe PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.

Association between visceral lipid accumulation indicators and gallstones: a cross-sectional study based on NHANES 2017–2020

BackgroundObesity is a major contributing factor to the formation of gallstones. As early identification typically results in improved outcomes, we explored the relationship between visceral lipid accumulation indicators and the occurrence of gallstones.MethodsThis cross-sectional study involved 3,224 adults. The researchers employed multivariable logistic regression, smoothed curve fitting (SCF), threshold effects analysis, and subgroup analysis to examine the relationship between metabolic scores for visceral fat (METS-VF), waist circumference (WC), lipid accumulation products (LAP), and visceral adiposity index (VAI) and gallstones. A Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify key factors which were then used in the construction of a nomogram model. The diagnostic efficacy of this model in detecting gallstones was then determined using receiver operating characteristic curves.ResultsVisceral lipid accumulation indicators were strongly linked to the likelihood of having gallstones. Specific saturation effects for METS-VF, WC, LAP, and VAI and gallstones were determined using SCF. The inflection points for these effects were found to be 8.565, 108.400, 18.056, and 1.071, respectively. Subgroup analyses showed that associations remained consistent in most subgroups. The nomogram model, which was developed using critical features identified by LASSO regression, demonstrated excellent discriminatory ability, as indicated by an area under the curve value of 0.725.ConclusionsStudies have shown that increases in METS-VF, WC, LAP, and VAI are linked to increased prevalences of gallstones. The nomogram model, designed with critical parameters identified using LASSO regression, exhibits a strong association with the presence of gallstones.

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.

Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making. We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space (P > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data (N = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making. This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to -10.4 (-29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker. This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making. Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

Diagnostic Value of Magnetocardiography to Detect Abnormal Myocardial Perfusion: A Pilot Study.

Magnetocardiography (MCG) is a novel non-invasive technique that detects subtle magnetic fields generated by cardiomyocyte electrical activity, offering sensitive detection of myocardial ischemia. This study aimed to assess the ability of MCG to predict impaired myocardial perfusion using single-photon emission computed tomography (SPECT). A total of 112 patients with chest pain underwent SPECT and MCG scans, from which 65 MCG output parameters were analyzed. Using least absolute shrinkage and selection operator (LASSO) regression to screen for significant MCG variables, three machine learning models were established to detect impaired myocardial perfusion: random forest (RF), decision tree (DT), and support vector machine (SVM). The diagnostic performance was evaluated based on the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Five variables, the ratio of magnetic field amplitude at R-peak and positive T-peak (RoART+), R and T-peak magnetic field angle (RTA), maximum magnetic field angle (MAmax), maximum change in current angle (CCAmax), and change positive pole point area between the T-wave beginning and peak (CPPPATbp), were selected from 65 automatic output parameters. RTA emerged as the most critical variable in the RF, DT, and SVM models. All three models exhibited excellent diagnostic performance, with AUCs of 0.796, 0.780, and 0.804, respectively. While all models showed high sensitivity (RF = 0.870, DT = 0.826, SVM = 0.913), their specificity was comparatively lower (RF = 0.500, DT = 0.300, SVM = 0.100). Machine learning models utilizing five key MCG variables successfully predicted impaired myocardial perfusion, as confirmed by SPECT. These findings underscore the potential of MCG as a promising future screening tool for detecting impaired myocardial perfusion. ChiCTR2200066942, https://www.chictr.org.cn/showproj.html?proj=187904.