Shrinkage Technique Research Articles

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China.

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer-Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer–Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

Which is the better polyp detection metric: adenomas per colonoscopy or adenoma detection rate? A simulation modelling study

Background We compared the ability of ADR and APC to assess an endoscopist’s detection ability using statistical principles and simulations. Methods We simulated a population of endoscopists and patients to compare the ability of ADR versus APC for capturing the true endoscopist ability (TEA). We compared these rates with and without adjustment for patient and exam factors using multivariable models, and adjustment for imprecision due to low volume using empirical Bayes (shrinkage). Power calculations were used to compare the ability of ADR and APC to distinguish higher from lower rates over two time periods for an endoscopist. Results APC and ADR had similar discriminatory ability for assessing TEA. This increased with higher volumes and after adjusting for risk factors and low volume using shrinkage. Higher APC and ADRs had higher power for comparing an endoscopist’s detection over two time periods, but APC was superior to ADR. For example, there was 29% power to distinguish (n=200 colonoscopies) APCs:0.10 from 0.15, similar to the power (28%) to distinguish corresponding ADRs:10% and 14%. However, at same volume (n=200), the power to distinguish higher APC rates (0.50vs.0.75) was greater (89%) than the power (78%) for corresponding ADRs (39%vs.53%). Conclusions Adjusting for patient and exam factors and/or using shrinkage techniques for lower volume endoscopists can increase the correlation between TEA for both ADR and APC. For higher detection rates, APC offers more power than ADR in distinguishing differences in the detection ability.

REXPACO ASDI: joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

ABSTRACT Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. By leveraging the relative angular motion and spectral scaling between on-axis and off-axis sources, post-processing techniques can separate residual star light from light emitted by surrounding objects such as circumstellar discs or point-like objects. This paper introduces a new algorithm that jointly unmixes these components and deconvolves disc images. The proposed algorithm is based on a statistical model of the residual star light, accounting for its spatial and spectral correlations. These correlations are crucial yet remain inadequately modelled by existing reconstruction algorithms. We employ dedicated shrinkage techniques to estimate the large number of parameters of our correlation model in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, enabling its efficient suppression. We apply our method to data sets from the Very Large Telescope/Spectro-Polarimetry High-contrast Exoplanet REsearch instrument and compare its performance with standard algorithms (median subtraction, PCA, PACO). We demonstrate that considering the multiple correlations within the data significantly improves reconstruction quality, resulting in better preservation of both disc morphology and photometry. With its unique joint spectral modelling, the proposed algorithm can reconstruct discs with circular symmetry (e.g. rings, spirals) at intensities one million times fainter than the star, without needing additional reference data sets free from off-axis objects.

The m6A and immune regulatory gene signature predicts the prognosis and correlates with immune infiltration of head and neck squamous cell carcinoma

Recent investigations have underscored the epigenetic modulation of the immune response; however, the interplay between RNA N6-methyladenosine (m6A) modification and immunomodulation in head and neck squamous cell carcinoma (HNSC) remains relatively unexplored. To bridge this knowledge gap, we undertook an extensive examination of the potential contributions of m6A modification and immunomodulation in HNSC. We amalgamated and deduplicated 27 m6A -related genes (m6AGs) and 1342 immune regulation-related genes (IMRGs), resulting in a comprehensive dataset encompassing 1358 genes. This dataset was scrutinized for m6A modification and immunomodulatory patterns within HNSC specimens. Employing Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) technique, we developed a prognostic risk model for m6A regulator-mediated methylation modification and immunomodulation-related differentially expressed genes (m6A&IMRDEGs). Our differential expression analysis delineated 29 m6A&IMRDEGs, and Weighted Gene Co-expression Network Analysis (WGCNA) elucidated two module genes (IL11 and MMP13) subjected to correlation analysis. The prognostic prediction models revealed that the clinical predictive efficacy peaked for 1-year forecasts, followed sequentially by 3-year and 5-year predictions. The risk scores derived from the model adeptly categorized HNSC patients into high- and low-risk cohorts, with the high-risk group exhibiting a more unfavorable prognosis. Protein-Protein Interaction (PPI) analysis identified 7 hub genes implicated in m6A and immune regulation, namely BPIFB1, BPIFB2, GP2, MUC5B, MUC7, PIP, and SCGB3A1. Furthermore, we noted marked disparities in the expression profiles of 18 immune cell types between the high- and low-risk groups. Our results substantiate that the clustering subpopulations and risk models associated with m6A and immune regulatory genes portend a poor prognosis in HNSC. The risk score emerges as a potent prognostic biomarker and predictive metric for HNSC patients. A thorough assessment of m6A and immune regulatory genes in HNSC will augment our comprehension of the tumor immune microenvironment and facilitate the advancement of HNSC therapeutics.

Identifying modifiable factors associated with neuroimaging markers of brain health.

Brain structural alterations begin long before the presentation of brain disorders; therefore, we aimed to systematically investigate a wide range of influencing factors on neuroimaging markers of brain health. Utilizing data from 30,651 participants from the UK Biobank, we explored associations between 218 modifiable factors and neuroimaging markers of brain health. We conducted an exposome-wide association study using the least absolute shrinkage and selection operator (LASSO) technique. Restricted cubic splines (RCS) were further employed to estimate potential nonlinear correlations. Weighted standardized scores for neuroimaging markers were computed based on the estimates for individual factors. Finally, stratum-specific analyses were performed to examine differences in factors affecting brain health at different ages. The identified factors related to neuroimaging markers of brain health fell into six domains, including systematic diseases, lifestyle factors, personality traits, social support, anthropometric indicators, and biochemical markers. The explained variance percentage of neuroimaging markers by weighted standardized scores ranged from 0.5% to 7%. Notably, associations between systematic diseases and neuroimaging markers were stronger in older individuals than in younger ones. This study identified a series of factors related to neuroimaging markers of brain health. Targeting the identified factors might help in formulating effective strategies for maintaining brain health.

Improved estimation of the correlation matrix using reinforcement learning and text-based networks

We propose a data-driven methodology to shrink the correlation matrix and, hence, the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumptions on the stock returns and can be applied to any covariance matrix target. It focuses on the special case of the global minimum variance portfolio and investigates the economic value of our methodology by utilizing text-based networks (Hoberg and Phillips, 2016). The portfolio selection problem, hence, boils down to determining the optimal shrinkage policy using RL. The empirical analysis utilizes a large universe of stocks covering more than 400 assets and 20 years as a testing period. Overall, the proposed portfolio rule outperforms state-of-the-art shrinkage techniques in terms of out-of-sample volatility, Sharpe ratio, and downside risk net of transaction costs. Our research highlights the effectiveness of our RL-driven approach and underscores the value of alternative data sources in ex-ante forming robust portfolio rules.

A Fast iterative shrinkage and threshold algorithm-based least-squares deconvolution technique with application to bearing fault signals

Bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown. The low energy level of fault-induced impulsive signals, however, renders it vulnerable to being masked by environmental noise and other disturbances present in the vibration signal. To cope with this issue, this paper investigates the application of the finite impulse response (FIR) filter for bearing fault detection. In this work, the estimation of the FIR is formulated as a least squares optimization problem, aiming to minimize the l 2 norm of the FIR and effectively suppress noise and interference sources. The solutions of the least squares optimization problem are obtained using the fast iterative shrinkage–thresholding (FISTA) algorithm. Furthermore, to better select the regularization parameter in our method, an improved gray wolf optimizer (I–GWO) is employed to conduct the parameter selection. Based on these improvements, the proposed FISTA–LSD technique enables rapid and precise detection of fault characteristics upon their occurrence, thereby mitigating the propagation of faults and preventing accidents from happening. Furthermore, with this automated selection process, engineers can save valuable time that would otherwise be spent on trial-and-error approaches. Through its rapid response capabilities and precise fault detection abilities enabled by the FISTA–LSD technique along with I–GWO integration for automatic parameter selection; this FISTA–LSD technique greatly enhances its practical value. The proposed technique is validated through the utilization of both numerical and experimental data.

Advanced Ocular Artifact Removal In EEG For Enhanced Schizophrenia Diagnosis Using Wavelet Shrinkage And LSTM Deep Learning Techniques

Advanced Ocular Artifact Removal In EEG For Enhanced Schizophrenia Diagnosis Using Wavelet Shrinkage And LSTM Deep Learning Techniques

Development and Validation of Nosocomial Bacterial Infection Prediction Models for Patients With Systemic Lupus Erythematosus.

Hospital-acquired bacterial infections are associated with high morbidity and mortality rates in patients with systemic lupus erythematosus (SLE). This study aimed to develop and validate predictive models for the risk of hospital-acquired bacterial infections in patients with SLE. A historical cohort study was designed for development, and another bidirectional cohort study was used for external validation. The risk of bacterial infection was assessed upon admission and after 5 days of hospitalization. Predictor selection employed the least absolute shrinkage and selection operator (LASSO) techniques. Multiple imputations were used to handle missing data. Logistic regression models were applied, and the properties of discrimination, calibration, and decision curve analysis were evaluated. The development cohort comprised 1686 patients and 237 events (14.1%) from 3 tertiary hospitals. The external validation cohort included 531 patients and 84 infection outcomes (15.8%) from 10 hospital centers in Colombia (secondary and tertiary level). The models applied at admission and after 120 hours of stay exhibited good discrimination (AUC > 0.74). External validation demonstrated good performance among patients from the same tertiary institutions where the models were developed. However, geographic validation at other institutions has been suboptimal. Two predictive models for nosocomial bacterial infections in patients with SLE are presented. All infection prevention recommendations should be maximized in patients at moderate/high risk. Further validation studies in diverse contexts, as well as clinical impact trials, are necessary before potential applications in research and clinical care.

Can assessment of human assumed central sensitisation improve the predictive accuracy of the STarT Back screening tool in acute low back pain?

BackgroundThe STarT Back Screening Tool (SBT) is recommended to provide risk-stratified care in low back pain (LBP), yet its predictive value is moderate for disability and low for pain severity. Assessment of human assumed central sensitisation (HACS) in conjunction with the SBT may improve its predictive accuracy. ObjectivesTo examine whether assessment of HACS in acute LBP improves the predictive accuracy of the SBT for LBP recovery at six months in people with acute non-specific LBP. DesignA prospective longitudinal study. MethodData were drawn from the UPWaRD study. One hundred and twenty people with acute non-specific LBP were recruited from the community. Baseline measures included SBT risk status, nociceptive flexor withdrawal reflex, pressure and heat pain thresholds and conditioned pain modulation. Primary outcome was the presence of LBP (pain numeric rating scale ≥1 and Roland Morris Disability Questionnaire score ≥3) at six-month follow-up. Regression coefficients were penalised using the least absolute shrinkage and selection operator technique to select predictor variables. Internal validation was performed using ten-fold cross-validation. Results/findingsSBT risk status alone did not predict the presence of LBP at six months (area under receiver operating characteristic curve [AUC] = 0.58). Adding measures of HACS to the SBT did not improve discrimination for whether LBP was present at six months (AUC = 0.59). ConclusionsThis study confirmed the suboptimal predictive accuracy of the SBT, administered during acute LBP, for LBP recovery at six months. Assessment of HACS in acute LBP does not improve the predictive accuracy of the SBT.

Identification of E3 ubiquitin ligase-associated prognostic genes and construction of a prediction model for uterine cervical cancer based on bioinformatics analysis

E3 ligases are engaged in a variety of physiological processes within cells and use ubiquitin-labeled substrates to control their activity and stability. Although some research has indicated that E3 ligases or particular substrates have an impact on the treatment that cervical cancer patients get after their diagnosis, The exact purpose of these enzymes in the occurrence and evolution of cancer of the cervical region (CC) is not clear. In order to extract and analyze relevant mRNA gene expression data as well as clinical patient data, we used open databases. A reliable risk prediction model was developed by applying the least absolute shrinkage and selection operator (LASSO) technique in conjunction with Cox regression analysis. Column-line plots were combined to analyze the predictive model, and the GSE44001 dataset served as an external validation.Four gene models:proteasome (prosome, macropain) 26S subunit, non-ATPase, 14(PSMD14),proteasome (prosome, macropain) subunit, alpha type, 4(PSMA4,),zinc finger and BTB domain containing 16(ZBTB16),and ankyrin repeat domain 9(ANKRD9). Gene expression levels in both healthy and cancerous tissues have been confirmed by the HPA database. Next, the investigation focused on immunological state and tumor mutation load. The high-risk group and Cluster B had distinct levels of immune cell infiltration and a worse prognosis. Additionally, KEGG and GO analyses of differentially expressed genes (DEGs) between the high- and low-risk groups were performed, as well as tumor microenvironment (TME) investigations. Targeting E3 ligases may be an efficient strategy to treat cervical cancer (CC), according to a novel and comprehensive E3 ubiquitination ligase-associated gene model that has been presented.

A Medical Decision Support Platform for Identifying Thrombospondin 1 in Non-alcoholic Fatty Liver Disease via Immuno-Infiltration Analysis.

Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1). THBS1 demonstrated strong performance in distinguishing NAFLD patients (AUC = 0.981) and MI patients (AUC = 0.900). Immuno-infiltration analysis revealed significantly lower CD8+ T cells and higher neutrophil levels in patients with NAFLD and MI. CD8+ T cells and neutrophils were effective in distinguishing NAFLD/MI from healthy controls. Correlation analysis showed that THBS1 was positively correlated with CCR (chemokine receptor), MHC class (major histocompatibility complex class), neutrophils, parainflammation, and Tfh (follicular helper T cells), and negatively correlated with CD8+ T cells, cytolytic activity, and TIL (tumor-infiltrating lymphocytes) in NAFLD and MI patients. THBS1 emerged as a novel biomarker for diagnosing NAFLD/MI in comparison to healthy controls. The results indicate that CD8+ T cells and neutrophils could serve as inflammatory immune features for differentiating patients with NAFLD/MI from healthy individuals.

Predicting oil price fluctuations: Integrating external indicators and advanced regression techniques

The study utilizes external factors to improve the precision of predicting fluctuations in oil prices. Based on the pertinent research, the present research gathers 62 external factors from 2000 to 2021, that indicate the changes in oil need, oil supplies, oil stock, economic basics, monetary metrics, and estimations of unpredictability. The experimental findings suggest that shrinkage techniques provide better predictions for all times, the research indicates that principle component analysis (PCA) regression precisely forecasts oil price variations one month in advance. Shrinkage approaches, in contrast, surpass comparable methods when it comes to prediction for all type timeframes. Moreover, an uncontrolled learning technique known as Principal Component Analysis (PCA) demonstrates better predictive ability when oil prices are falling, while supervised learning methods such as shrinkage methods notably enhance the precision of variability estimation. These results suggest that using sophisticated regression methods will significantly improve the accuracy of oil price projections, hence supporting improved decision-making for legislators and traders.

Association of sociodemographic and lifestyle factors and dietary intake with overweight and obesity among U.S. children: findings from NHANES

Childhood overweight/obesity is a serious problem that has not been adequately addressed. As a key factor affecting weight gain, the association between dietary intake with childhood overweight and obesity is still unclear. The objective of this study was to analyze the association between sociodemographic, lifestyle factors and dietary intake with overweight or obesity. We used data from a large cross-sectional National Health and Nutrition Examination Survey (NHANES). The U.S. children aged 6–15 years with both weight data and dietary data were included. For univariate analysis of sociodemographic data, t tests was performed for continuous variables and chi-square tests was performed for discrete variables. Dietary intakes were described by median and quartile, and differences in dietary intake between children with normal weight and children with overweight or obesity were compared by rank sum tests. A modern statistical shrinkage technique, LASSO regression was used to examine the association between dietary intake and childhood obesity. Our study confirms that Hispanic ethnicity, increasing age, passive exposure to smoking, higher protein intake, and higher caffeine intake were positively associated with child overweight or obesity. Additionally, non-Hispanic White race, higher physical activity levels, higher household income, and higher vitamin A intake were negatively associated with child overweight or obesity.

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3

BackgroundThis study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer. MethodsWe included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data.Results After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set.ConclusionThe study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

Development of a Predictive Nomogram for Sepsis in Patients with Urolithiasis-Related Obstructive Pyelonephritis.

Background and Objectives: In patients with urolithiasis-related obstructive pyelonephritis (UROP), sepsis represents a critical and concerning complication that can substantially increase the mortality rate. This study aimed to identify the risk factors for sepsis in UROP patients and to develop a predictive nomogram model. Materials and Methods: We analyzed data from 148 patients who met the UROP criteria and were admitted to Chang Gung Memorial Hospital between 1 January 2016 and 31 December 2021. The primary outcome evaluated was the incidence of sepsis, as defined by the most recent Sepsis-3 guidelines. To identify potential risk factors for sepsis, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, we utilized multivariable logistic regression to construct the predictive model. Results: There was a total of 102 non-sepsis cases and 46 sepsis cases. Risk factors for sepsis in multivariable analysis were a history of diabetes mellitus (DM) (OR = 4.24, p = 0.007), shock index (SI) (×10-1) (OR = 1.55, p < 0.001), C-reactive protein (CRP) (mg/dL) (OR = 1.08, p = 0.005), and neutrophil to lymphocyte ratio (NLR) (×10) (OR = 1.58, p = 0.007). The nomogram exhibited an area under the receiver operating characteristic curve of 0.890 (95% CI 0.830-0.949). Conclusions: Our study demonstrated that patients with UROP who have DM, higher SI, higher NLR, and elevated CRP levels are significantly more likely to develop sepsis. These insights may aid in risk stratification, and it is imperative that clinicians promptly initiate treatment for those identified as high risk.

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs.

Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management. Transcriptomic and clinical datasets for patients with BLCA were obtained from The Cancer Genome Atlas. Using multivariate Cox regression and least absolute shrinkage and selection operator techniques, a risk prognostic signature defined by DRLs was developed. The model's accuracy and prognostic relevance were assessed through Kaplan-Meier survival plots, receiver operating characteristic curves, concordance index, and principal component analysis. Functional and pathway enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, were conducted to elucidate the underlying biological processes. Immune cell infiltration was quantified using the CIBERSORT algorithm. Differences and functions of immune cells in different risk groups were evaluated through single-sample Gene Set Enrichment Analysis. The Tumor Immune Dysfunction and Exclusion predictor and tumor mutational burden (TMB) assessments were utilized to gauge the likelihood of response to immunotherapy. Drug sensitivity predictions were made using the Genomics of Drug Sensitivity in Cancer database. A robust 8-DRL risk prognostic model, comprising LINC00513, SMARCA5-AS1, MIR4435-2HG, MIR4713HG, AL122035.1, AL359762.3, AC006160.1, and AL590428.1, was identified as an independent prognostic indicator. This model demonstrated strong predictive power for overall survival in patients with BLCA, revealing significant disparities between high- and low-risk groups regarding tumor microenvironment, immune infiltration, immune functions, TMB, Tumor Immune Dysfunction and Exclusion scores, and drug susceptibility. This study introduces an innovative prognostic signature of 8 DRLs, offering a valuable prognostic tool and potential therapeutic targets for bladder carcinoma. The findings have significant implications for TMB, the immune landscape, and patient responsiveness to immunotherapy and targeted treatments.

Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers

**Ischemic stroke remains a leading cause of morbidity and mortality globally. Despite the advances in thrombolytic therapy, notably recombinant tissue plasminogen activator (rtPA), patient outcomes are highly variable. This study aims to introduce a novel predictive model, the Acute Stroke Thrombolysis Non-Responder Prediction Model (ASTN-RPM), to identify patients unlikely to benefit from rtPA within the critical early recovery window. We conducted a retrospective cohort study at Baoding No.1 Central Hospital including 709 adult patients diagnosed with acute ischemic stroke and treated with intravenous alteplase within the therapeutic time window. The ASTN-RPM was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression technique, incorporating a wide range of biomarkers and clinical parameters. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA). ASTN-RPM effectively identified patients at high risk of poor response to thrombolysis, with an AUC of 0.909 in the training set and 0.872 in the validation set, indicating high sensitivity and specificity. Key predictors included posterior circulation stroke, high admission NIHSS scores, extended door to needle time, and certain laboratory parameters like homocysteine levels. The ASTN-RPM stands as a potential tool for refining clinical decision-making in ischemic stroke management. By anticipating thrombolytic non-response, clinicians can personalize treatment strategies, possibly improving patient outcomes and reducing the burden of ineffective interventions. Future studies are needed for external validation and to explore the incorporation of emerging biomarkers and imaging data.