Least Absolute Shrinkage And Selection Operator Model Research Articles

Clinical Significance of Combined Detection of CCL22 and IL-1 as Potential New Bronchial Inflammatory Mediators in Children's Asthma.

Severe asthma is a significant health burden because children with severe asthma are vulnerable to medication-related side effects, life-threatening deterioration, and impaired quality of life. However, there is a lack of data to elucidate the role of inflammatory variables in asthma. This study aimed to compare the levels of inflammatory factors in serum and sputum in children with acute and stable asthma to those in healthy children and the ability to predict clinical response to azithromycin therapy. This study recruited 95 individuals aged 1-3 years old and collected data from January 2018 to 2020. We examined serum and sputum inflammatory factors and constructed the least absolute shrinkage and selection operator (LASSO) model. Predictive models were constructed through multifactor logistic regression and presented in the form of column-line plots. The performance of the column-line diagrams was measured by subject work characteristics (ROC) curves, calibration plots, and decision curve analysis (DCA). Then, filter-paper samples were collected from 45 children with acute asthma who were randomly assigned to receive either azithromycin (10 mg/kg, n = 22) or placebo (n = 23). Pretreatment levels of immune mediators were then analyzed and compared with clinical response to azithromycin therapy. Of the 95 eligible participants, 21 (22.11%) were healthy controls, 29 (30.53%) had stable asthma, and 45 (47.37%) had acute asthma. The levels of interferon-γ (IFN-γ), tumor necrosis factor-a (TNF-α), chemokine CCL22 (CCL22), interleukin 12 (IL-12), chemokine CCL4 (CCL4), chemokine CCL2 (CCL2), and chemokine CCL13 (CCL13）were significantly higher in the acute asthma group than in the stable asthma group. A logistic regression analysis was performed using CCL22 and IL-1 as independent variables. Additionally, IFN-γ, TNF-α, IL-1, IL-13, and CCL22 were identified in the LASSO model. Finally, we found that CCL22 and IL-1 were more responsive in predicting the response to azithromycin treatment. Our results show that CCL22 and IL-1 are both representative markers during asthma symptom exacerbations and an immune mediator that can predict response to azithromycin therapy.

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.

Plasma proteomics and carotid intima-media thickness in the UK biobank cohort.

Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later. We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R 2). The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R 2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R 2 (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models. Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

Establishment and verification of a prognostic immune cell signature-based model for breast cancer overall survival.

Breast cancer (BRCA) is a prevalent and aggressive disease. Despite various treatments being applied, a significant number of patients continue to experience unfavorable prognoses. Accurate prognosis prediction in BRCA is crucial for tailoring individualized treatment plans and improving patient outcomes. Recent studies have highlighted the significance of immune cell infiltration in the tumor microenvironment (TME), but predicting survival remains challenging due to the heterogeneity of BRCA. The aim of this study was thus to produce an immune cell signature-based framework capable of predicting the prognosis of patients with BRCA. The GSE169246 dataset was from the Gene Expression Omnibus (GEO) database, comprising single-cell RNA sequencing (scRNA-seq) data from 95 individuals with BRCA. Seurat, principal component analysis (PCA), the unified matrix polynomial approach (UMAP) algorithm, and linear dimensionality reduction were used to determine the heterogeneity of T cells. Overlapping analysis of differentially expressed genes (DEGs), genes associated with prognosis, and T-cell pharmacodynamics-related genes were used to obtain the T-cell core pharmacodynamics-related genes. The dimensionality of the T-cell core pharmacodynamics-related genes was reduced employing the least absolute shrinkage and selection operator (LASSO) Cox regression model and the LASSO model. The prognostic model was built via a Cox analysis of the overall survival (OS) information. The clinical sample included 95 patients with BRCA who underwent surgical treatment from October 2018 to October 2021 at the Second Affiliated Hospital of Qiqihar Medical University. Patients were divided into a good prognosis group and a poor prognosis group based on their prognostic outcomes. The predictive value of tumor characteristics and immune responses was validated through correlation analysis, logistic regression analysis, and receiver operating characteristic (ROC) analysis. A group of 95 genes was used to establish a prognostic model. In the GEO clinical sample, with a high-risk group demonstrating shorter median survival times (2,447 vs. 6,498 days, P=4.733e-12). Area under the curve (AUC) values of 0.75, 0.75, and 0.72 were obtained for 2-, 4-, and 6-year OS predictions, respectively. Clinical validation found that the 6-year OS of the favorable prognosis group was significantly higher than that of the unfavorable prognosis group (92.06% vs. 65.62%; P=0.005). Poor prognosis was positively correlated with age, tumor size, B-cell level, and CTLA4 level and negatively correlated with tumor stage (T1/T2), lymph node metastasis stage (N0), clinical stage I-II, CD3+T-cell, CD4+T-cell, CD8+T-cell, neutrophil, lymphocyte, natural kill cell, TIGIT expression and OS. The combined model of clinical parameters had an AUC value of 0.898. This study established a prognostic model that demonstrated excellent predictive value for OS of BRCA. The predictive model developed offers valuable insights into prognosis and treatment planning, emphasizing the importance of tumor characteristics and immune cell infiltration.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Development and validation of a nomogram for predicting the interstitial fibrosis and tubular atrophy in patients with lupus nephritis.

Interstitial fibrosis and tubular atrophy (IFTA) were frequent histologic features of lupus nephritis (LN), and LN patients with IFTA have poor renal outcomes. In this study, we aimed to construct prediction models for the IFTA in LN patients. This retrospective study included 303 patients with biopsy proven LN at the Affiliated Hospital of Qingdao University and Union Hospital of Fujian Medical University. The participants were randomly divided into development and validation cohorts. They were further divided into IFTA and non-IFTA groups. The least absolute shrinkage and selection operator (LASSO) regression model with laboratory test results collected at the time of kidney biopsy was used to optimize feature selection for the risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the LASSO regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and ROC curve analysis. Internal validation was assessed using the bootstrapping validation. A nomogram for individual assessment was constructed based on the preferable model. Predictors contained in the prediction nomogram included age, body mass index (BMI), mean arterial pressure (MAP), logANA, C3, eGFR and serum uric acid. The model displayed good discrimination with a C-index of 0.794 (95% CI 0.734-0.854) and good calibration. High C-index value of 0.857 (95% CI 0.776-0.938) could still be reached in the interval validation. A nomogram model based on the LASSO model was created for producing a probability score of IFTA in LN patients. With excellent predictive abilities, the nomogram may provide a simple and reliable tool to distinguish LN patients with IFTA and helps physicians make clinical decisions in their comprehensive assessment.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort.

While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD. Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis. The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD. A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

Comparative Predictive Modeling for PICC Line Complications in Oncology: A Retrospective Study.

Aims/Background Peripherally inserted central catheter (PICC) are increasingly used in cancer treatment, offering significant therapeutic benefits while also posing risks for complications such as infection, thrombosis, and catheter migration. Effective prediction and management of these complications are crucial to optimizing patient outcomes and reducing healthcare costs. Methods This retrospective study analyzed PICC line insertion in 266 cancer patients implemented from January 2019 to December 2023 at a regional healthcare facility in China. Using least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key factors influencing PICC line complications and developed a tailored nomogram for individual risk assessment. The efficacy of the model was compared with support vector machine (SVM), random forest, and gradient boosting machine (GBM) using receiver operating characteristic (ROC) and decision curve analysis (DCA) metrics. Results Factors such as body mass index (BMI), diabetic status, and age were found to be significant predictors of PICC line complications. The LASSO model demonstrated superior predictive capability (area under the curve, AUC = 0.79) over SVM (AUC = 0.40), random forest (AUC = 0.70), and GBM (AUC = 0.64). A tailored nomogram was developed for clinical use, enabling personalized risk evaluation. Conclusion The study underscores the utility of LASSO logistic regression in the personalized risk evaluation of PICC line complications, recommending its integration into clinical practice. The tailored nomogram provides a practical tool for clinicians to enhance care customization for patients requiring PICC lines, thereby improving treatment outcomes and patient safety.

Assessing the transportability of radiomic models for lung cancer diagnosis: commercial vs. open-source feature extractors.

Radiomics has shown promise in improving malignancy risk stratification of indeterminate pulmonary nodules (IPNs) with many platforms available, but with no head-to-head comparisons. This study aimed to evaluate transportability of radiomic models across platforms by comparing performances of a commercial radiomic feature extractor (HealthMyne) with an open-source extractor (PyRadiomics) on diagnosis of lung cancer in IPNs. A commercial radiomic feature extractor was used to segment IPNs from computed tomography (CT) scans, and a previously validated radiomic model based on commercial features was used as baseline (ComRad). Using same segmentation masks, PyRadiomics, an open-source feature extractor was used to build three open-source radiomic models (OpenRad) using different methods: de novo open-source model derived using least absolute shrinkage and selection operator (LASSO) for feature selection, selecting open-source features matched to ComRad features based upon Imaging Biomarker Standardization Initiative (IBSI) nomenclature, and selecting open-source features most highly correlated to ComRad features. Radiomic models were trained on an internal cohort (n=161) and externally validated on 3 cohorts (n=278). We added Mayo clinical risk score to OpenRad and ComRad models, creating integrated clinical radiomic (ClinRad) models. All models were compared using area under the curve (AUC) and evaluated for clinical improvement using bias-corrected clinical net reclassification indices (cNRIs). ComRad AUC was 0.76 [95% confidence interval (CI): 0.71-0.82], and OpenRad AUC was 0.75 (95% CI: 0.69-0.81) for LASSO model, 0.74 (95% CI: 0.68-0.79) for Spearman's correlation, and 0.71 (95% CI: 0.65-0.77) for IBSI. Mayo scores were added to OpenRad LASSO model, which performed best, forming open-source ClinRad model with AUC of 0.80 (95% CI: 0.74-0.86), identical to commercial ClinRad's AUC. Both ClinRad models showed clinical improvement compared to Mayo alone, with commercial ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.07 (95% CI: 0.00-0.13) for malignant, and open-source ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.06 (95% CI: 0.00-0.12) for malignant. Transportability of radiomic models across platforms directly does not conserve performance, but radiomic platforms can provide equivalent results when building de novo models allowing for flexibility in feature selection to maximize prediction accuracy.

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at herd-level using mid-infrared spectral information from routine milk control

As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome

BackgroundMultiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections.MethodsBuffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study (“Glue Grant”) multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction.ResultsThe LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890–0.987] (training) and 0.833 [0.699–0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997–1.000] (training) and 0.907 [0.816–0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884–0.985] (training) and 0.836 [0.703–0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex—0.649 [0.537–0.762] (training) and 0.493 [0.301–0.685] (test); ISS + sex—0.630 [0.516–0.744] (training) and 0.482 [0.293–0.670] (test); NISS + sex—0.651 [0.540–0.763] (training) and 0.525 [0.335–0.714] (test)).ConclusionsThe accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.

Lactylation signature identifies liver fibrosis phenotypes and traces fibrotic progression to hepatocellular carcinoma.

Precise staging and classification of liver fibrosis are crucial for the hierarchy management of patients. The roles of lactylation are newly found in the progression of liver fibrosis. This study is committed to investigating the signature genes with histone lactylation and their connection with immune infiltration among liver fibrosis with different phenotypes. Firstly, a total of 629 upregulated and 261 downregulated genes were screened out of 3 datasets of patients with liver fibrosis from the GEO database and functional analysis confirmed that these differentially expressed genes (DEGs) participated profoundly in fibrosis-related processes. After intersecting with previously reported lactylation-related genes, 12 DEGs related to histone lactylation were found and narrowed down to 6 core genes using R algorithms, namely S100A6, HMGN4, IFI16, LDHB, S100A4, and VIM. The core DEGs were incorporated into the Least absolute shrinkage and selection operator (LASSO) model to test their power to distinguish the fibrotic stage. Advanced fibrosis presented a pattern of immune infiltration different from mild fibrosis, and the core DEGs were significantly correlated with immunocytes. Gene set and enrichment analysis (GSEA) results revealed that core DEGs were closely linked to immune response and chemokine signaling. Samples were classified into 3 clusters using the LASSO model, followed by gene set variation analysis (GSVA), which indicated that liver fibrosis can be divided into status featuring lipid metabolism reprogramming, immunity immersing, and intermediate of both. The regulatory networks of the core genes shared several transcription factors, and certain core DEGs also presented dysregulation in other liver fibrosis and idiopathic pulmonary fibrosis (IPF) cohorts, indicating that lactylation may exert comparable functions in various fibrotic pathology. Lastly, core DEGs also exhibited upregulation in HCC. Lactylation extensively participates in the pathological progression and immune infiltration of fibrosis. Lactylation and related immune infiltration could be a worthy focus for the investigation of HCC developed from liver fibrosis.

Contrasting methods to operationalize antibiotic exposure in clinical research: a real-world application on healthcare-associated Clostridioides difficile infection.

The goal of this article is to summarize common methods of antibiotic operationalization used in clinical research and demonstrate methods for exposure variable selection. We demonstrate three methods for modeling exposure, using data from a case-control study on Clostridioides difficile infection in hospitalized patients: 1) factor analysis, 2) logistic regression models, and 3) Least Absolute Shrinkage and Selection Operator (LASSO) regression. The factor analysis identified 8 variables contributing the most variation in the dataset: any antibiotic exposure; number of antibiotic classes; number of antibiotic courses; dose; and specific classes monobactam, 𝛽-lactam 𝛽-lactamase inhibitors, rifamycin, and cephalosporin. The logistic regression models resulting in the best model fit used predictors representing any antibiotic exposure and the proportion of a patient's hospitalization on antibiotics. The LASSO model selected 22 variables for inclusion in the predictive model, of which 10 were antibiotic exposure variables, including: any antibiotic exposure; classes 𝛽-lactam 𝛽-lactamase inhibitors, carbapenem, cephalosporin, fluoroquinolone, monobactam, rifamycin, sulfonamides, and miscellaneous; and proportion of hospitalization on antibiotics. Investigators studying antibiotic use should consider multiple characteristics of exposure informed by their research question and the theory on how antibiotics may impact the distribution of the outcome in their target population.

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke

BackgroundThe objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.MethodsRetrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission − 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model’s performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram.ResultsThis study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen.ConclusionMachine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study

BackgroundFrailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.MethodsWe conducted a cross-sectional and longitudinal study using data from the “Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),” which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).ResultsAt baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model’s AUC was 0.943 (95%CI 0.913–0.974), indicating good discrimination, with Hosmer–Lemeshow (H–L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model’s AUC was 0.722 (95%CI 0.656–0.788), indicating moderate discrimination, with H–L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.ConclusionFrailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

Transcriptomic signature of CD4-expressing T-cell abundance developed in healthy peripheral blood predicts strong anti-retroviral therapeutic response in HIV-1: A retrospective and proof-of-concept study

CD4-expressing T-cells (CD4Ts) play a crucial role in maintaining the normal functioning of the mammalian immune system and overall systemic health. Diseased individuals, such as those infected with the human immunodeficiency virus type I (HIV-1), experience progressive and eventual depletion of CD4T leading to uncurable conditions and ultimate death if left untreated. Although much is known about the role of CD4T-mediated immunity, the understanding of CD4T-related transcriptomic patterns remains incomplete. This proof-of-concept study aims to identify a transcriptome-wide gene signature for CD4T abundance by Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling in 340 healthy peripheral blood samples. The optimized LASSO model demonstrated computational robustness (tenfold average Pearson’s r = 0.89) and biological relevance evidenced by four significant Gene Ontology terms (all odds ratio [OR] ≥ 4.5 and false discovery rate ≤0.05). Subsequently, in an independent cohort with 24 HIV-1-infected men who received anti-retroviral therapies, there is a significant, positive association between the gene signature and a strong anti-retroviral response before (OR = 13.6, P < 0.05) and after adjusting for subject age, sex, and race (OR = 14.4, P < 0.05). Taken together, the gene expression pattern associated with CD4T abundance is predictable, generalizable, and biologically relevant, shedding new light on the importance of CD4T abundance.

Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index

Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798–0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.

The development of a prediction model based on random survival forest for the prognosis of non- Hodgkin lymphoma: A prospective cohort study in China

Background and objectiveThe pathological staging of non-Hodgkin lymphoma (NHL) is complex, the clinical manifestations are varied, and the prognosis differ considerably. To provide a useful reference for early detection and effective treatment of NHL, we developed a random survival forest (RSF) prognostic model based on machine learning (ML) algorithms using prospective cohort data collected from Chongqing Cancer Hospital from Jan 1, 2017 to Dec 31, 2019 (n = 1449) to compare with the traditional cornerstone method Cox proportional hazards (CPH) model and evaluate the predictability of the model. MethodsPatients were randomly split into a training cohort (TC) and validation cohort (VC) based on 65/35 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extracted the important features. And the RSF was modeled to explore the prognostic factors impacting the overall survival (OS) of patients with NHLs in the TC and validated in the VC. The C-index, the Integrated Brier Score (IBS), Kaplan-Meir method, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were selected to measure performances and discriminations of the models. In addition, individual survival probability predicted for NHL patients. ResultsAccording to the features extracted by LASSO model and univariable Cox model, 16 variables were selected to develop the RSF model with log-rank splitting rule, which were age, ethnicity, medical insurance, Ann Arbor stage, pathology, targeted-therapy, chemo-therapy, peripheral blood neutrophil count to lymphocyte count ratio (NLR), peripheral blood platelet count to lymphocyte count ratio (PLR), serum lactate dehydrogenase (LDH), CD4/CD8, platelet (PLT), absolute neutrophil count (ANC), lymphocyte (LYM), B-symptoms, and (CPR) were important prognostic factors. Compared to the CPH model (C-index = 0.748, IBS = 0.166), the RSF model (C-index = 0.786, IBS = 0.165) is outperformed in predictability and accuracy. The AUC of the RSF model to estimate the 1-, 3-, and 5-year OS in TC were 0.847, 0.847, and 0.809, respectively; while those in the CPH were 0.816, 0.803, and 0.750, respectively. ConclusionsTo provide practical implications for the implementation of individualized therapy, the study constructed a high-performed RSF model and reveal that it outperformed the traditional model CPH. And the RSF model ranked the risk variables. In addition, we stratified the risk of NHL patients and estimated individual survival probability based on the RSF model.

Occurrence of rare earth elements in umbilical cord serum and association with thyroid hormones and birth outcomes in newborns

Rare earth elements (REEs) are emerging contaminants that are increasingly used in high technology products. However, limited information is available regarding exposure to REEs and associated health effects in neonates. This study aimed to investigate the association between REE concentrations and thyroid hormone levels, as well as birth outcomes in 109 newborns in Beijing, China. We measured the concentrations of 16 REEs and thyroid hormones in umbilical cord serum. To assess the impact of exposure to individual REEs and REE mixtures on thyroid hormone levels and birth outcomes, we employed univariate linear regression, least absolute shrinkage and selection operator (LASSO), and weighted quantile sum (WQS) models. We detected 14 REEs at high rates (92.6%–100%), with yttrium exhibiting the highest median (interquartile range) concentration [43.94 (0.33–172.55) ng/mL], followed by scandium [3.64 (0.46–11.15) ng/mL]. Univariate analyses showed that per logarithmic (ln)-unit change of neodymium (Nd) and samarium (Sm) was associated with 0.039 [95% confidence interval (CI): 0.001, 0.007] and 0.031 (95% CI: 0.003, 0.060) increases in free thyroxine (FT4) levels, respectively. Moreover, 14 REEs exhibited significant associations with triiodothyronine (T3) levels, resulting in increases ranging from 0.066 to 0.307. Elevated concentrations of terbium (Tb) [per ln-unit change: −0.021 (95% CI: −0.041, −0.01)] and lutetium (Lu) [-0.023 (95% CI: −0.043, −0.002)] were inversely correlated with birth length in newborns. A further multiple exposure analysis employing the LASSO model identified Sm, Nd, Y, Sc, and Lu as critical factors influencing FT4 and T3 levels. Additionally, WQS analyses showed positive associations between exposure to a mixture of 14 REEs and FT4 (P = 0.046), T3 (P < 0.001), and birth length (P = 0.049). These findings suggest that in utero exposure to REEs might disrupt thyroid hormone homeostasis and impact intrauterine growth. Further studies are warranted to validate these findings and elucidate the underlying mechanisms.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study.

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.