Model Validation Research Articles

Maintenance of long-term transposable element activity through regulation by nonautonomous elements.

Transposable elements are DNA sequences that can move and replicate within genomes. Broadly, there are 2 types: autonomous elements, which encode the necessary enzymes for transposition, and nonautonomous elements, which rely on the enzymes produced by autonomous elements for their transposition. Nonautonomous elements have been proposed to regulate the numbers of transposable elements, which is a possible explanation for the persistence of transposition activity over long evolutionary times. However, previous modeling studies indicate that interactions between autonomous and nonautonomous elements usually result in the extinction of one type. Here, we study a stochastic model that allows for the stable coexistence of autonomous and nonautonomous retrotransposons. We determine the conditions for this coexistence and derive an analytical expression for the stationary distribution of their copy numbers, showing that nonautonomous elements regulate stochastic fluctuations and the number of autonomous elements in stationarity. We find that the stationary variances of each element can be expressed as a function of the average copy numbers and their covariance, enabling data comparison and model validation. These results suggest that continued transposition activity of transposable elements, regulated by nonautonomous elements, is a possible evolutionary outcome that could for example explain the long coevolutionary history of autonomous LINE1 and nonautonomous Alu element transposition in the human ancestry.

Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging. A total of 49 responses were obtained and analysed to identify trends and patterns. While TGs valued transparency and traceability the most, CGs pointed out the importance of explainability. Among the topics where TGs may benefit from further exposure are stability and robustness checks, and mitigation of fairness issues. On the other hand, CGs seemed more reluctant towards synthetic data for validation and would benefit from exposure to cross-validation techniques, or segmentation metrics. Topics emerging from the open questions were utility, capability, adoption and trustworthiness. These findings on current trends in AI validation strategies may guide the creation of guidelines necessary for training the next generation of professionals working with AI in healthcare and contribute to bridging any technical-clinical gap in AI validation. RELEVANCE STATEMENT: This study recognised current gaps in understanding and applying AI validation strategies in cancer imaging and helped promote trust and adoption for interdisciplinary teams of technical and clinical researchers. KEY POINTS: Clinical and technical researchers emphasise interpretability, externalvalidation with diverse data, and biasawarenessin AI validation for cancer imaging. In cancer imaging AI research, clinical researchers prioritise explainability, whiletechnical researchers focus ontransparency and traceability, and see potential insynthetic datasets. Researchers advocate for greater homogenisation of AI validation practices in cancer imaging.

Evaluating a clinically available artificial intelligence model for intracranialaneurysm detection: a multi-reader study and algorithmic audit.

We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine clinical settings. Two distinct cohorts of head CT angiography (CTA) data were assembled to validate an AI model. Cohort 1, comprising gold-standard consecutive CTA cases, was used in an MRMC study involving six board-certified general radiologists. Cohort 2, representing clinical CTA cases, was used to simulate a routine clinical setting. Following these evaluations, an algorithmic audit was conducted to identify any unusual or unexpected behaviors exhibited by the model. Cohort 1 consisted of 131 CTA cases, while Cohort 2 included 515 CTA cases. In the MRMC study, the AI-assisted strategy demonstrated a significant improvement in aneurysm diagnostic performance, with the area under the receiver operating characteristic curve increasing from 0.815 (95%CI: 0.754-0.875) to 0.875 (95%CI: 0.831-0.921; p = 0.008). In the AI-based first-reader study, 60.4% of the CTA cases were identified as negative by the AI, with a high negative predictive value of 0.994 (95%CI: 0.977-0.999). The algorithmic audit highlighted two issues for improvement: the accurate detection of tiny aneurysms and the effective exclusion of false-positive lesions. This study highlights the clinical utility of a high-performance AI model in detecting IAs, significantly improving general radiologists' diagnostic performance with the potential to reduce their workload in routine clinical practice. The algorithmic audit offers insights to guide the development and validation of future AI models.

Efficacy of Spray Cryotherapy on Airway Secretions in Canine Models of Chronic Bronchitis and Mechanism Insights.

Introduction Bronchoscopic spray cryotherapy (SCT) is a novel treatment showing promise for chronic bronchitis (CB), characterized by excessive mucus secretion and productive cough. A large animal model for preclinical research of SCT is lacking, and its treatment's efficacy and mechanisms for CB are not well understood. Methods Eight Labradors were exposed to 200 ppm SO2 for 6 months to develop a CB model. Evaluations included pulmonary resistance, bronchoalveolar lavage fluid (BALF), CT images, and pathology. After model validation, six dogs received SCT and were observed for short-term (7 days) and long-term (30 days) outcomes. Metrics included pulmonary resistance, bronchoscopy findings, and BALF analysis for inflammatory factors, acetylcholine, and mucins. Bronchial tissue was assessed via HE staining, electron microscopy, and IHC staining. BEAS-2B cells were used to study MUC5AC expression in response to LPS and acetylcholine. Results SO2 exposure led to persistent cough, increased pulmonary resistance, goblet cell hyperplasia, and inflammation. Mucin, MUC5AC, and MUC5B levels in BALF increased over time, which validated the CB model. SCT treatment reduced mucus and pulmonary resistance, improved bronchial structure, and decreased goblet cells. SCT significantly reduced BALF mucin, MUC5AC, MUC5B, acetylcholine, IL-6, INF-γ, TNF-α, and IL-10, and bronchial MUC5AC and CHRM3. In the LPS treatment BEAS-2B cells, MUC5AC expression increased when acetylcholine pretreatment concentration increased. Conclusion The SO2 inhalation protocol effectively establishes a CB model in dogs. SCT effectively treats CB by reducing mucin levels and may lower MUC5AC expression by decreasing acetylcholine and CHRM3.

Development and Validation of a GULP-Based Predictive Model for Dehydration in Elderly Patients with Post-Stroke Dysphagia

Aims/Background The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients. Based on GULP criteria, the study aims to establish and validate a dehydration prediction model for elderly patients with dysphagia following a stroke. Methods Two hundred patients with post-stroke dysphagia treated at Beijing Rehabilitation Hospital Affiliated with Capital Medical University, from January 2020 to December 2023, were selected retrospectively. The patients were randomly matched at a ratio of 1:4 to establish a verification group (n = 40) and a modelling group (n = 160). Based on the occurrence of dehydration, the modelling group patients were divided into two groups: the dehydration group (n = 55) and the non-dehydration group (n = 105). Univariate and multivariate logistic regression analyses were used to identify the influencing factors of dehydration in elderly patients with dysphagia after a stroke, and to establish a predictive model based on GULP. The predictive value of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Results The results of univariate and multivariate logistic regression analyses showed significant differences in age, lesion location, muscle strength grade, homocysteine (Hcy), and swallowing function score (p < 0.05). When these influencing factors were included in the model, the slope of the calibration curve in both the training set and the validation set was close to 1, indicating that the predicted dehydration risk was consistent with the actual risk. ROC analysis results revealed that in the training set, the model predicted dehydration in elderly post-stroke patients with dysphagia with an area under the curve (AUC) of 0.934, a standard error of 0.034, and a 95% confidence interval (CI) of 0.916 to 0.981. The optimal cutoff value was 0.78, yielding a sensitivity of 88.84% and a specificity of 90.00%. In the validation set, the AUC was 0.867 with a standard error of 0.025 and a 95% CI of 0.694 to 0.934. The optimal cutoff value here was 0.66, with a sensitivity of 80.16% and a specificity of 85.94%. Conclusion This study successfully established and validated a GULP-based dehydration prediction model for elderly patients with dysphagia following a stroke, demonstrating high application value.

Machine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.

Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort. Data of aneurysmal SAH patients treated by surgical clipping or endovascular coiling between 2003 and 2019 were obtained from the Japanese Stroke Databank (derivation cohort, n = 9,657) and "Predict for Outcome Study of Aneurysmal Subarachnoid Hemorrhage" (validation cohort, n = 5,085). Generalized additive models (GAMs) for poor outcome (modified Rankin Scale score ≥ 3 at discharge) were fitted with age transformation using spline curves for each World Federation of Neurological Societies grade. The discrimination property and calibration plot of unadjusted and adjusted models were assessed using the validation cohort. The derivation and validation cohorts included 3,610 and 3,251 patients, respectively. Regarding discrimination, areas under the receiver operating characteristic curve for the derivation and validation cohorts were 0.835 and 0.827, respectively, in the unadjusted model and 0.844 and 0.836, respectively, in the adjusted model. An unbiased correlation was confirmed between predicted and observed probabilities of poor outcomes. GAM could help visualize the correlation between age and clinical outcomes. Our prediction model can quantitatively aid in treatment decision-making and can be effective for most diseases and treatment settings. Trial Registration: UMIN Clinical Trials Registry (Date 2/22/2022/ ID, UMIN000046282 number, R000052809 URL, https//www.umin.ac.jp/ctr/index.htm) and the Japan Registry of Clinical Trials (Date 3/28/2022 /No. jRCT1060210092 URL, https//jrct.niph.go.jp/).

An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks

The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

Development of a Genetic Algorithm Tool for the Optimization of the Methanol Oxidation

AbstractThis work presents an automatic optimization tool using the genetic algorithm (GA) for the chemical kinetic model of methanol (CH3OH) oxidation. A total of 54 parameters of 40 important reactions of the reaction model have been optimized. Ignition delay times measured in shock tubes, concentration profiles measured in plug flow reactors, and laminar flame speeds were used for the model validation. Compared to the results of the initial model, the optimized model exhibits a significantly improved predictive capability for the experimental targets. The GA tool developed in this study has been proven effective for optimizing detailed chemical kinetics models.

A Hysteresis Model Incorporating Varying Pinching Stiffness and Spread for Enhanced Structural Damage Simulation

The widely used Bouc–Wen–Baber–Noori (BWBN) hysteresis model, although effective in simulating hysteresis behaviors, does not account for variations in the pinching region of hysteretic behaviors. This can negatively impact the accuracy of the BWBN model in simulating structural responses and damage mechanisms in structures such as reinforced concrete (RC) and timber, which exhibit highly pinched hysteresis behavior when damaged by earthquakes. This paper introduces a BWBN model with varying pinching region characteristics (BWBN-VP model) which can degrade pinching stiffness and increase pinching effects under seismic loads. Unlike the original BWBN model using constant pinching stiffness (kp), this modified new model, inspired by real-world structural damage, improves structural damage detection, identifiability, and analysis in real-world scenarios. Model validation uses experimental data from three RC column tests with different failure modes and hysteresis loop shapes, resulting in an ~0.98 correlation coefficient between the experimental and simulated responses. Further validation uses real-world seismic data from a six-story RC building and achieves an average correlation of ~0.97 with a minor 2.5% difference in the peak restoring forces compared to direct measurements. The proposed BWBN-VP model also accurately and realistically captures damage to both the elastic and pinching stiffness values of the building, with an average difference of ~4%. Results confirm that the BWBN-VP model, compared to the original, more accurately predicts hysteretic responses, especially in Shear Failure (SF) modes. Therefore, the BWBN-VP model, superior in simulating highly pinched behaviors in RC and timber structures, would be an advanced tool for resilient seismic design and Structural Health Monitoring (SHM).

Reply: Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer Specific Mortality, and All-Cause Mortality.

Reply: Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer Specific Mortality, and All-Cause Mortality.

Establishment and validation of predictive model of ARDS in critically ill patients

BackgroundAcute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers. This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters.MethodsThis prospective cohort study in China was conducted on critically ill patients to derivate and validate the prediction model. The derivation cohort, consisting of 400 patients admitted to the ICU of the Peking University Third Hospital(PUTH) between December 2020 and August 2023, was separated for training and internal validation, and an external data set of 160 patients at the FU YANG People's Hospital from August 2022 to August 2023 was employed for external validation. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen predictor variables. Multiple ML classification models were integrated to analyze and identify the best models. Several evaluation indexes were used to compare the model performance, including the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). SHapley Additive ex Planations (SHAP) is used to interpret ML models.Results400 critically ill patients were included in the analysis, with 117 developing ARDS during follow-up. The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.762–0.910). For external validation, comprising 160 patients, 44 of whom developed ARDS, the AUC was 0.799 (95% CI: 0.723–0.875), significantly outperforming the LIPS score alone.ConclusionCombining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

Development and Validation of a Predictive Model of Prostate Screening Compliance: A Nationwide Population-Based Study.

Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers. This study aims to develop and validate a predictive model for PCa screening compliance in Mexican men. Retrospective observational design with data from the Mexican Health and Aging Study (MHAS). Participants were men aged 50-69 from three cohorts: development/internal validation, temporal validation, and external validation. Key predictors were identified using relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression, and model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, along with calibration and decision curve analysis (DCA). A web nomogram was also developed. The final model included seven key predictors. AUC values indicated good predictive performance: 0.783 for the training subgroup, 0.722 for the test subgroup, 0.748 for the time cohort, and 0.756 for the external cohort, with sensitivities of 73.5%. The DCA demonstrated the superior clinical utility of the model compared to the reference strategies. The predictive model developed for performance to PCa screening is robust across different cohorts and highlights critical factors influencing performance. The accompanying web-based nomogram enhances clinical applicability and supports interventions aimed at improving PCa screening rates among Mexican men.

Expression and prognosis of cellular senescence genes in head and neck squamous cell carcinoma

BackgroundCellular senescence refers to cells entering a relatively stable state of cell cycle arrest, which is a barrier that tumor cells must cross to achieve immortalization and plays an extremely important role in preventing the occurrence and development of tumors. In recent years, numerous studies have shown that inducing tumor cells to enter a senescent state has become a feasible tumor control strategy. At present, cellular senescence has become a research hotspot in tumor prevention and treatment, as well as in cell biology. However, the expression and prognostic values of cellular senescence genes in head and neck squamous cell carcinoma (HNSC) remain unclear.Material/MethodsWe analyzed the expression patterns and prognostic values of cellular senescence genes in HNSC from TCGA and GEO. The TCGA-HNSC data were used as the training group and were divided into high- and low-risk groups, and the GEO database was used as the test group. Analyses included survival analysis, ROC curve analysis, risk curve analysis, independent prognostic analysis and model validation for clinical grouping. We used the HPA database for protein-level validation of the genes.ResultsWe identified 5 cellular senescence genes associated with HNSC, namely, BTG3, EHF, EZH2, TACC3 and TXN. These cellular senescence genes were analyzed in the training and test groups and were found to be significantly associated with the prognosis of HNSC patients.ConclusionsThe tumor immune microenvironment of HNSC appears to have correlations with certain cellular senescence-related features. Genes associated with cellular senescence, such as BTG3, EHF, EZH2, TACC3, and TXN, show promise as potential diagnostic and prognostic biomarkers for HNSC.

Pharmacophore modeling and QSAR analysis of anti-HBV flavonols.

Due to its global burden, Targeting Hepatitis B virus (HBV) infection in humans is crucial. Herbal medicine has long been significant, with flavonoids demonstrating promising results. Hence, the present study aimed to establish a way of identifying flavonoids with anti-HBV activities. Flavonoid structures with anti-HBV activities were retrieved. A flavonol-based pharmacophore model was established using LigandScout v4.4. Screening was performed using the PharmIt server. A QSAR equation was developed and validated with independent sets of compounds. The applicability domain (AD) was defined using Euclidean distance calculations for model validation. The best model, consisting of 57 features, was generated. High-throughput screening (HTS) using the flavonol-based model resulted in 509 unique hits. The model's accuracy was further validated using a set of FDA-approved chemicals, demonstrating a sensitivity of 71% and a specificity of 100%. Additionally, the QSAR model with two predictors, x4a and qed, exhibited predictive solid performance with an adjusted-R2 value of 0.85 and 0.90 of Q2. PCA showed essential patterns and relationships within the dataset, with the first two components explaining nearly 98% of the total variance. Current HBV therapies tend to fail to provide a complete cure, emphasizing the need for new therapies. This study's importance was to highlight flavonols as potential anti-HBV medicines, presenting a supplementary option for existing therapy. The QSAR model has been validated with two separate chemical sets, guaranteeing its reproducibility and usefulness for other flavonols by utilizing the predictive characteristics of X4A and qed. These results provide new possibilities for discovering future anti-HBV drugs by integrating modeling and experimental research.

Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions.

Modeling Mechanical Properties of Concrete with Bida Natural Stones as Coarse Aggregate

This paper presents the results of statistical models for the mechanical properties of concrete made using Bida natural stones (pebbles) as coarse aggregate. Fresh and hardened concrete properties were analyzed using response surface methodology (RSM). The research employed central composite design using three (3) independent variables with equivalent ranges as Water to Cement ratio (W/C) = 0.4, 0.5, 0.6, Coarse Aggregate to Total Aggregate ratio (CA/TA) = 0.55, 0.615, 0.68 and Total Aggregate to Cement ratio (TA/C) = 3.0, 4.5 and 6.0. Minitab 14 (2004) selected 20 possible mix proportions for the experiment. The concrete specimens that were used for this research work include 150mm x 150mm x 150mm concrete cube, Ø150mm x 300mm concrete cylinder and 100mm x 100mm x 500mm concrete prism for compressive strength, elastic modulus/splitting tensile strength and flexural strength respectively. For each mix, five numbers of specimens were cast and cured for 7, 14, 21 and 28days. The twenty-eight days measured responses were used to develop models for the mechanical properties. Statistical and experimental model validations were employed. The p-value for all individual terms were less than 0.05, p-values for lack of fit were also greater than 0.05 in all cases tested, coefficient of determination (R2), adjusted coefficient of determination (adjR2) were in the range of 89.7% to 100% for compressive strength, splitting tensile strength, elastic modulus, flexural strength, and slump respectively which indicated adequate model validity. The predicted responses closely agree with experimental values, this further confirms the validity of the models developed. The study therefore suggested interaction, pure quadratic, reduced full quadratic, interaction models for the mechanical properties and slump respectively for the concrete made using Bida natural stones as coarse aggregate. The concrete with mix constituent mentioned above can be used for structural application such as reinforced concrete slabs, beams, columns and foundations.

Development and validation of a predictive model and tool for functional recovery in patients after postero-lateral interbody fusion

ObjectiveThe postoperative recovery of patients with lumbar disc herniation (LDH) requires further study. This study aimed to establish and validate a predictive model for functional recovery in patients with LDH and explore associated risk factors.MethodPatients with LDH undergoing PLIF admitted from January 1, 2018 to December 31, 2022 were included, and patient data were prospectively collected through follow-up. The training and validation cohorts were randomly assigned in a 7:3 ratio. To pool data variables LASSO regression was used. The pooled variables were subsequently included in binary logistic regression analyses, construct risk prediction models, and plot nomograms. Additionally, recovery prediction models and interactive web page calculators were developed using R Shiny.ResultsOverall, 1,097 patients with LDH following PLIF were included in this study. Regarding patients’ economic and functional scores, 927 (84.5%) received excellent scores. Key indicators significantly were screened. Multivariate analysis showed that age, season, occupation, HDL-C, smoking, weekly exercise time, and osteoporosis were independent risk factors for postoperative recovery. The C-index of the model was 0.776 (95% CI: 0.7312–0.8208) and 0.804 (95% CI: 0.7408–0.8673) for the training and validation cohorts, respectively. The H–L test showed good fitting of the model (all P > 0.05). The DCA curve showed the best clinical efficacy when the threshold probability was in the ranges of 0–0.71 and 0.79–0.84. The interactive web calculator is accessed at https://postoperativerecoveryofldh.shinyapps.io/DynNomapp/.ConclusionThe predictive tools derived from this study can provide realistic and personalized expectations of postoperative outcomes for patients undergoing lumbar spine surgery.

Validation of a Kinetic Model for Methanation of COx Gases with Literature Data

Validation of a Kinetic Model for Methanation of CO<i>_x</i> Gases with Literature Data

Identification and validation of soft tissue sarcoma-specific transcriptomic model for predicting radioresistance

Purpose We aimed to identify the transcriptomic signatures of soft tissue sarcoma (STS) related to radioresistance and establish a model to predict radioresistance. Materials and Methods Nine STS cell lines were cultured. Adenosine triphosphate-based viability was determined 5 days after irradiation with 8 Gy of X-rays in a single fraction. Radiosensitive and radioresistant groups were stratified according to the survival rates. Whole transcriptomic sequencing analysis was performed and differentially expressed genes (DEGs) were identified between the radiosensitive and radioresistant groups. For model generation, a cohort of 59 patients with sarcomas from The Cancer Genome Atlas (TCGA) was used. DEGs of the responder and non-responder groups according to the radiotherapy-best response were identified. The overlapping DEGs between those from TCGA data and the STS cell line were subjected to linear regression to develop a formula, namely the STS-specific radioresistance index (STS-RRI), and its performance was compared with that of the previously established radiosensitivity index (RSI). Results We selected thirteen overlapping DEGs and established STS-RRI using seven of them: STS-RRI = 1.5185 × MYO16–0.01575 × MYH11 + 3.900375 × KCTD16 + 0.105375 × SYNPO2–0.777375 × MYPN–0.849875 × PCSK6–0.700125 × LTK + 39.4635. Delong’s test revealed that the STS-RRI performed better at stratifying responder and non-responder in TCGA cohort than the RSI (p = .002). The progression-free survival curves of the TCGA cohort were significantly discriminated by STS-RRI (p = .013) but not by RSI (p = .241). Conclusion We developed the STS-RRI to predict the radioresistance of patients with STS in the TCGA dataset, showing a higher performance than RSI.

Development and validation of a risk prediction model for acute kidney injury in coronary artery disease

BackgroundAcute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. This study focuses on developing accurate predictive models to improve the early detection and prognosis of AKI in CAD patients.MethodsWe used Electronic Health Records (EHRs) from a nationwide CAD registry including 54 429 patients. Initially, univariate analysis identified potential predictors. Subsequently, a stepwise multivariate logistic model integrated clinical significance and data distribution. To refine predictor selection, we applied a random forest algorithm. The top 10 variables, including admission to the surgical department, EGFR, hemoglobin, and others, were incorporated into a logistic regression-based prediction model. Model performance was assessed using the area under the curve (AUC) and calibration analysis, and a nomogram was developed for practical application.ResultsDuring hospitalization, 2,112 (3.88%) patients in the overall population of both the development and validation groups experienced AKI within 30 days. The final prediction model exhibited strong discrimination with an AUC of 0.867 (95% CI: 0.858 to 0.876) and well calibration capability in both the development and validation groups. Key predictors included surgical department admission, eGFR, hemoglobin, chronic kidney disease history, male sex, white blood cell count, age, left ventricular ejection fraction, acute myocardial infarction at admission, and congestive heart failure history. Bootstrap resampling confirmed model stability (Harrell's optimism-correct AUC = 0.866). The nomogram provided a practical tool for AKI risk assessment.ConclusionThis study introduced a refined AKI risk prediction model for CAD patients. This model showed adaptability to subgroups and held the potential for early AKI alerts and personalized interventions, thereby enhancing patient care.