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The distribution characteristics of PD-1 pathway-related immune cells in esophageal cancer tissue and their prognostic significance

Objective This study aims to elucidate the distribution patterns of immune cells associated with the programmed cell death protein 1 (PD-1) pathway within esophageal cancer (EC) tissues and to determine their correlation with patient prognosis. Methods We included tissue samples from 236 EC patients who had undergone surgery at our institution between January 2016 and January 2021. This study examined the correlation between six immunohistochemical markers and the clinical profiles of these patients. Survival analysis was performed using the Kaplan-Meier method and the LOG-rank test to evaluate the impact of immunohistochemical marker expression on patient survival. A clinical predictive model was developed and validated for prognostic assessment. Results Expression levels of PD-1, PD-L1, FOXP3, and CD25 were found to be positively associated with the depth of tumor invasion and lymph node metastasis (P < 0.05). In contrast, CD4 and CD8 expression levels were inversely related to these parameters (P < 0.05). High expression of PD-1, PD-L1, FOXP3, and CD25, along with lymph node metastasis, were identified as independent prognostic risk factors (P < 0.05). Patients with low expression of PD-1, PD-L1, FOXP3, CD25, and high expression of CD4 and CD8 exhibited improved three-year survival rates (P < 0.001). The predictive model, based on these factors, demonstrated high discrimination and accuracy. Conclusion A prognostic model incorporating the expression levels of PD-1, PD-L1, FOXP3, CD25, and lymphocyte infiltration offers robust predictive validity for the prognosis of EC patients.

Social support-based physical activity that exerts beneficial effects for obese older adults with cognitive impairment via increasing participation in leisure-time physical activity

Obesity in older adults increases the risks of diabetes, cardiovascular disease, cognitive decline, and depression. Physical activity has been established as an important strategy in lifestyle interventions for obese people. This study focused on the strategy for increasing participation in physical activity, which is called social support. Although the studies found that social support helps to motivate participation in physical activity of older adults, its beneficial effects for population with obesity are still few investigations. Therefore, this study focused on developing a social support-based physical activity program for obese older adults with cognitive impairment and hypothesized that it can increase level of physical activity and reduce severity of depression and cognitive decline in obese older adults with cognitive impairment. In this study, thirty-nine participants were divided into three groups consisting of 13 obese older adults, 13 obese older adults with cognitive impairment, and 13 obese older adults with cognitive impairment and receiving the physical activity program for 4 weeks. After that, the levels of physical activity, cognition, and depression were tested in all three groups. Results showed that social support-based physical activity decreased cognitive decline and severity of depression in obese older adults with cognitive impairment. Moreover, the level of physical activity in obese older adults with cognitive impairment and receiving the physical activity program was increased by increasing participation in leisure-time physical activity. Thus, it suggested that the social support-based physical activity exerted beneficial effects for obese older adults with cognitive impairment via increasing participation in leisure-time physical activity.

Correction: Correlation between individual thigh muscle volume and grip strength in relation to sarcopenia with automated muscle segmentation

Exploring the torque- velocity relationship in postmenopausal women: Analyzing the influence of data processing

Objective The main aims of this study were to compare the goodness of fit and derived parameters of linear and non-linear models for fitting the torque-velocity (TV) relationship in postmenopausal women, and to examine the influence of data processing on the results obtained. Methods Sixteen physically active postmenopausal women completed the experiment. Knee extensor (KE) and elbow flexor (EF) muscle strength was evaluated in the dominant limb using an isokinetic dynamometer. Isometric and isokinetic tests were conducted at 30, 60, 120, 180, 240, and 300°/s. Peak torque and the corresponding joint angles were recorded for each test. TV data were fitted using linear, quadratic polynomial (PM), and Hill’s (HM) regression models. TV relationships were analyzed using both actual data (i.e., the velocity achieved and its associated torque; TVA) and target data (i.e., the velocity preset on the dynamometer and the torque reported; TVT). TV parameters derived from each model and their goodness of fit were calculated for both TVA and TVT relationships. Results The goodness of fit and the estimated TV parameters derived from the regression models differed significantly between TVA and TVT for both KE and EF (P < 0.05). For TVA, the models with the best fit were HM for KE and PM for EF. However, HM yielded unrealistically high theoretical maximum velocity values (6764.69 ± 11619.09°/s) for KE. Parameter estimates for TVA differed significantly between models (P < 0.001). Conclusion Caution is advised when performing isokinetic assessments at high velocities in middle-aged women. The obtained data should be carefully examined, as TVA and TVT should not be used interchangeably. The choice of model can influence the estimated parameters. We recommend using quadratic polynomial models to fit TV data for both KE and EF in postmenopausal women.

Reading through the eyes of a university student: A double-masked randomised placebo-controlled cross-over protocol investigating coloured spectacle lens efficacy in adults with visual stress

Background Visual stress is a reading disorder characterised by perceptual distortions, asthenopia and headache whilst reading, alongside increased sensitivity to repeated striped patterns (‘pattern glare’), in the absence of underlying ocular pathology. Coloured filters including tinted spectacle lenses and coloured overlays/acetates have been reported to ameliorate visual stress symptoms. However, evidence on coloured spectacle lenses efficacy at managing symptoms of visual stress, particularly in adults, is lacking, with recent systematic reviews advocating the need for large-scale randomised control trials. Methods This is a double-masked randomised placebo-controlled superiority trial. University students identified with symptoms of visual stress, through use of a reading symptom questionnaire and mid-spatial frequency pattern glare test, will be recruited. Sample size for power of 90% at 5% significance, accounting for 10% dropout will be 65. Participants will be randomly assigned experimental and control coloured spectacle lenses to wear for six weeks followed by a two week washout period, prior to wear of the alternate lenses for a further six weeks with a two week washout period. Participants will compare both sets of spectacle lenses in a ‘head-to-head’ comparison after the secondary washout period, prior to choosing the preferred lenses for voluntary future wear. Long-term adherence to the preferred lenses will be assessed three months post-comparison. Researchers and participants will be masked to spectacle lenses worn throughout the duration of the trial. Reading performance will be assessed with both sets of lenses at various time points within the trial. A range of reading tests, reading symptoms and pattern glare evaluation will be used to monitor change in reading performance and visual stress symptoms during the trial. Discussion The study will evaluate the hypothesis that coloured spectacle lenses increase reading speed and reduce severity and frequency of reading symptoms in adults with visual stress. Trial registration The trial is registered at ClinicalTrials.gov: NCT04318106.

Identifying trajectories of joint space width loss among previously injured knees: Data from the Osteoarthritis Initiative

Objectives To identify trajectories of joint space width loss, a proxy measure of tibiofemoral cartilage loss, among previously injured knees. To describe the relationship of trajectory groups with sociodemographic and clinical risk factors. Methods Using data from the Osteoarthritis Initiative, we identified right knees with a history of injury. We used group-based trajectory modeling to identify trajectories of joint space width loss over 96-months. Once trajectories were identified, we compared baseline statistics of key risk factors across trajectory groups. Results Our primary cohort included 772 previously injured right knees. We also analyzed a subset of 251 more recently injured right knees. Across each cohort, we identified three distinct trajectories for men and women separately, differentiated by low, medium, and high baseline joint space width. Rates of JSW loss were similar between trajectories. Those assigned to the high baseline JSW trajectory were younger at study baseline than those assigned to other two trajectories. Among women assigned to the low baseline JSW group, mean age at the time of knee injury was older than the other two trajectories. Among both men and women, the proportion of knees that had undergone a surgery or arthroscopy was highest in the low baseline JSW group. Conclusions Among knees with a history of injury, thinner JSW may be associated with knee surgical history and older age. Moving forward, exploring additional risk factors for OA development among previously injured knees may provide new opportunities to target treatments towards those at the greatest risk for the disease.

RETRACTED: Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT

E-commerce is a vital component of the world economy, providing people with a simple and convenient method for shopping and enabling businesses to expand into new global markets. Improving e-commerce decision-making by utilizing IoT and machine intelligence represents an important area for the impact of these technologies. Our objective is to elevate online shopping to a new level, making it a practical and genuinely delightful experience for customers. Businesses can acquire valuable insights to improve their operations and sales strategies by employing IoT devices to collect customer behavior and preference data and using machine learning (ML) algorithms to analyze them. In addition, companies can make simple recommendations using machine learning on the collected data. Our creative implementation of ML algorithms extends beyond simple recommendations. It also includes demand forecasting, guaranteeing that popular products are constantly in stock, reducing disappointments, and increasing consumer satisfaction. We applied several ML techniques, including logistic regression, Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), AdaBoosting, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). AdaBoosting outperformed the deep learning (DL) techniques LSTM and GRU and four ML techniques, logistic regression, Naïve Bayes, SVM, and RF, regarding F1 scores, accuracy, precision, and recall. It achieved an accuracy of 88%, an F1-score of 0.927, precision-1 of 0.908, and the ability of identifying true negatives and true positives (recall-0 and recall-1) of 0.569 and 0.947 respectively. Except for SVM, the other ML techniques did not exhibit much performance difference when using the count vectorizer and TD-IDF vectorizer. This study advances e-commerce capabilities through IoT and machine learning and paves the way for a new era of customer-centric, efficient, and adaptive retail strategies.

CONCERTO app for pediatric inpatients: A qualitative exploration of user experience and empowerment

Introduction Hospitals increasingly use health information technologies such as websites and apps to foster patient engagement. The Geneva University Hospitals developed CONCERTO, an ecosystem of patient applications for this exact purpose. The objectives of this study were 1) to evaluate how pediatric patients and their parents use the app CONCERTO and 2) to pinpoint functionalities that could facilitate patient empowerment and consequently alleviate the stress of children and their parents related to hospitalization. Materials and methods We interviewed 18 children and their parents during their hospitalization at the pediatric unit from May 20th to November 15th, 2022. Firstly, inspired by the Think-Aloud Protocol, we asked participants to complete a few specific tasks using the application. Then, we asked broader questions to better understand how CONCERTO could enhance patient empowerment. We used thematic analysis to explore the rich data set. Results Children and their parents appreciated the features of the app. Specifically, they enjoyed creating an avatar and were grateful to have access to the interactive meal menu and the agenda. Both patients and parents would have appreciated learning about CONCERTO upon their arrival as it could have improved their hospital experience. Participants shared challenges they faced during their hospitalization and made suggestions about how more personalized content in CONCERTO could help overcome negative moments. Conclusions CONCERTO effectively engaged and empowered patients and parents during their hospitalization. Further research is needed to evaluate the overall impact of such an app and understand the hurdles the care team faces in promoting the use of the app.

Indocyanine green fluorescence lymphography: An exploratory study of superficial lymphatic territories in the head and hind limbs of 33 cat cadavers

To date, animal models for lymphographic studies mainly focused on dog, while lymphography is rarely reported in cats, and even less involving cutaneous lymphatic territories. This study aims to assess the feasibility of cutaneous lymphography using indocyanine green (ICG) fluorescence in cat cadavers and describe predictable lymphatic pathways from cutaneous regions of head and hind limb anatomical districts. Frozen or refrigerated cadavers of adult cats that died for causes unrelated to the study were included. Twenty cutaneous regions (6 from the head; 14 from the hind limb) were selected using easily assessable anatomical landmarks, and expected draining lymphocentrums were presumed based on canine studies since there is no similar information for cats. For each lymphography, a single selected cutaneous region per anatomical district was assessed. After intradermal ICG injections, lymphatic drainage was favored by massage and/or flexion-extension movements. For each lymphography, all expected and detected lymphocentrums were dissected, and lymph nodes extirpated. Variables regarding cadavers and lymphography characteristics were assessed. ICG-lymphography was repeated in 33 cadavers. Out of the 99 selected cutaneous regions available, 15 were excluded following inclusion criteria, therefore lymphographies were performed for a total of 84 selected cutaneous regions (26 from the head and 58 from the hind limbs). A success was recorded in 63/84 (75%) lymphographies, with a median migration time of 8 (1–30) minutes. The ICG drained to the expected lymphocentrum in 28/63 (44%) lymphographies, and to other ones in 35/84 (56%). ICG-lymphography is feasible in cat cadavers, regardless of technique or cadaver characteristics. The observed difference in lymphatic drainage (56% to unexpected lymphocentrums) highlights the importance of specifically mapping lymphatic territories in cats. ICG-lymphography demonstrated as an effective technique and could be used to improve knowledge of feline lymphatic physiology. Further studies may provide a more complete understanding of superficial lymphatic territories in cats.

Comparative meta-analysis of barely transcriptome: Pathogen type determines host preference

Fungi and aphids show mutual interactions on barley pathogenesis. Fungi promote pathogenesis, while aphids either weaken or strengthen the infection. Otherwise, fungi alter aphid behavior and performance, further highlighting their complex interactions. Characterizing these synergistic and antagonistic interactions is crucial for understanding pathogenesis. Therefore, we performed meta-analysis and co-expression gene network analyses of the barley transcriptome in response to fungus and aphid based on hormone signaling pathways. We selected 13 studies, including 380 fungal infection samples, 48 aphid-attack samples, and 34 hormone-treated samples. We showed that 1.1% of DEGs were common between fungal and aphid-related datasets, while only 0.1% of DEGs were shared among all datasets. In addition, 70% of common DEGs were uniquely regulated by JA or SA signaling. In contrast, 30% of DEGs were regulated by both JA and SA simultaneously. Regulatory element analysis revealed that 85% of DEGs contained at least one binding site from AP2/EREBP or C2H2 zinc-finger factors that show substantial roles in SAR/ISR pathways during plant defense. Gene network analysis identified key hub genes, including SSI2, PAD2, RPS1, RPS17, SHM1, CYP5, and RPL21C, which influence plant host preference in response to pathogens. Moreover, we identified novel hub genes with unknown functions that potentially interact with the genes involved in defense responses and host preference. This study presents the first systems biology analysis of barley transcriptomic responses to heterotroph/biotroph cross-talk focusing on the preference and performance of Rhopalosiphum padi. Our findings suggest critical insights into the molecular mechanisms underlying barley defense responses and identify valuable candidate genes to developing pathogen resistance genotypes in agricultural systems.

Discovery of Cenozoic magmatic ridges and tectonics off northern Victoria Land provides new insights into the geodynamics of the Antarctic margin

Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening

In light of global climate change and the objective of carbon neutrality, the carbon market has become an important tool for the international community to combat climate change. Nonetheless, due to the complexity and non-linear nature of the carbon price, its accurate prediction has always been a research difficulty. This work presents a hybrid model incorporating comprehensive feature screening, optimized quadratic decomposition, and the Optuna-Attention-LSTM prediction method, aiming to improve the accuracy and stability of carbon price prediction. First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). Then, the variational mode decomposition (VMD) method is subsequently employed to execute a secondary decomposition on the reconstructed signal, with the decomposition hyperparameters optimized via crested porcupine optimization (CPO). Subsequently, Boruta and least absolute shrinkage and selection operator (Lasso) regression are employed to identify significant external features; finally, a long short-term memory (LSTM) model integrated with an attention mechanism is utilized for prediction, and optuna is introduced to optimize the hyperparameters. This paper evaluates the performance of the proposed model using the carbon markets of Guangdong, Hubei, and Shanghai in China as examples. The experimental results indicate that compared with the traditional model, the proposed model achieves average reductions of 67.30%, 47.68%, 48.42%, and 48.79% in the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively, demonstrating higher prediction accuracy and robustness. Shapley additive explanations (SHAP) analysis results indicate that carbon prices in the Guangdong carbon market are dominated by macroeconomic and regional environmental factors, while those in the Hubei carbon market are mainly driven by changes in the energy market. The Shanghai carbon market, on the other hand, is more significantly influenced by global carbon market dynamics and international trade activities. The research not only verifies the efficacy of the decomposition ensemble prediction framework, but also provides a scientific basis for decision-making for carbon market participants and policymakers.

Study on dynamic sand collapse mechanism of weakly consolidated rock strata in thick bedrock stope in western China

Analysing genome sequences and associated metadata during the COVID-19 pandemic in Iraq revealed points to be improved: An observational retrospective study

The COVID-19 pandemic started in Wuhan China and rapidly transmitted worldwide, the illness is characterised by respiratory manifestations like coughing, breathing difficulties and pneumonia that could lead to death. Real-time whole genome sequencing of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was adopted in many countries to track the infection dynamics and evolution of the virus. In parallel with the global efforts, genome sequencing trials were established in Iraq during the COVID-19 pandemic, however, this new approach has not been assessed yet. Therefore, for better readiness and improvement for future pandemics, here we obtained all genomes of SARS-CoV-2 virus from Iraq (182) that were deposited in National Center for Biotechnology Information (NCBI) during the period (2020–2023). Statistical analyses of sample size, distribution and other epidemiological parameters from associated metadata, as well as the quality of genome sequences were assessed. Our data analyses highlighted some drawbacks that could be improved, namely, that most genomic sequences (62%) were collected from only two cities, a low sample size was noticed and sequencing quality was inconsistent. There was a shortage and impairment of sequencing facilities especially those of the Ministry of Health. Consequently, genome sequencing should be achieved in centres that produce the best quality. The results revealed the importance of well-documented and high-quality sequences that represent many important cities in the country, which is crucial to draw a clear projection for health officials on infection dynamics and tracking viral evolution to help in taking successful steps towards infection control.

Author Correction: 1-Phenyl-β-carboline-3-carboxamide-1,2,3-triazole-N-phenylacetamide hybrids as new α-glucosidase inhibitors

Biosynthesis and characterization of silver nanoparticles from Asplenium dalhousiae and their potential biological properties

This study investigated the green synthesis of silver nanoparticles (AgNPs) using the medicinal plant Asplenium dalhousiae focusing on its bioactive chemical constituents as natural reducing agents. Aqueous, chloroform, and n-hexane extracts of the plant leaves were utilized in the nanoparticle synthesis process. The synthesized AgNPs were confirmed through UV-visible spectroscopy, showing absorption peaks at approximately 420 nm, 443 nm, and 439 nm. Fourier-transform infrared (FTIR) spectroscopy was used to recognize the functional groups in the plant extracts responsible for facilitating the reduction process. The morphological and structural characteristics of the synthesized nanoparticles were analyzed using Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD). These analyses revealed that the nanoparticles synthesized using the Aqueous, chloroform, and n-hexane extracts were predominantly spherical silver nanoparticles (AgNPs) with a crystalline structure and an average diameter of 46.98 ± 12.45 nm, as determined by SEM. The antibacterial efficacy of the synthesized AgNPs was evaluated against Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa at a concentration of 30 μg/ml. Among the tested nanoparticles, the AgNPs synthesized from the n-hexane extract exhibited the highest antibacterial activity, with zones of inhibition measuring 20.0 ± 1.8 mm for E. coli, 19.0 ± 1.2 mm for B. subtilis, and 19.5 ± 1.4 mm for P. aeruginosa. Additionally, the silver nanoparticles (AgNPs) from Asplenium dalhousiae demonstrated significant α-amylase inhibition, with 85.04% inhibition at 500 µg/ml, compared to Acarbose (90.84%) and the leaf extract (78.65%). The antioxidant activity of the synthesized AgNPs was assessed using the DPPH method, which confirmed their significant antioxidant properties alongside their antibacterial activity. The aqueous and n-hexane silver nanoparticles (AgNPs) showed strong cytotoxic activity with low IC50 values, particularly in A2780 cells (15.76 µg/ml and 9.11 µg/ml, respectively), while the plant methanolic extract and CHCl3 AgNPs exhibited much higher IC50 values, indicating moderate to low activity. This study highlights the potential of AgNPs in handling anticancer, α-amylase, and antibacterial infections. By assimilating natural products with nanotechnology, it deals an inventive approach to developing targeted, eco-friendly therapies, paving the way for cutting-edge biomedical applications and improved treatment outcomes.

Research on advanced grouting curtain technology for water interception and control at the source of aquifer in coal seam roof

Interpretable machine learning for predicting isolated basal septal hypertrophy

Background The basal septal hypertrophy(BSH) is an often under-recognized morphological change in the left ventricle. This is a common echocardiographic finding with a prevalence of approximately 7–20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. It also poses a risk of severe left ventricular outflow tract obstruction and is a significant cause of postoperative complications in patients undergoing transcatheter aortic valve implantation (TAVI). Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models. However, no predictive models for BSH have been developed using machine learning algorithms. Objective To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH. Methods Echocardiographic and clinical data from 902 patients were collected from the First Central Hospital of Baoding City, including 91 BSH patients and 811 non-BSH patients. The data were divided into training and test sets in a 7:3 ratio. Five machine learning algorithms -XGBoost, Random Forest(RF), Dicision tree(DT), K-Nearest Neighbor classification(KNN), and Naive Bayes(NB) were applied to construct the models, combined with logistic regression (LR) based on Lasso regression. The performance of each model was evaluated using Receiver Operating Characteristic curve (ROC),calibration curves and Decision Curve Analysis (DCA)curve, with the model demonstrating the best performance being selected. The shapley additive explanation (SHAP) method was employed to interpret the XBoost and RF models. Results The logistic regression (LR) of the Lasso regression model showed that IVS-AO Angle, Left Ventricular Mass Index (LVMI), Diastolic Left Ventricular Internal Diameter Index (LVIDdI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Distance from mitral valve closure point to basal segment of interventricular septum (MVCP-Sd), GLU, and Mitral Valve peak A (MV-A) were associated with BSH, with odds ratios (OR) of 0.86 (0.831–0.888), 1.034 (1.018–1.052), 0.104 (0.023–0.403), 1.041 (1.021–1.064), 0.964 (0.93–0.998), 0.852 (0.764–0.949), 1.146 (1.023–1.281), and 0.967 (0.947–0.987), respectively. The area under the ROC curve (AUC) for Model-relevant variable IVS-AO Angle, MVCP_Sd,LVMI, GLU, LVIDdI, SBP,DBP,LVIDdI,MV_A were 0.87,0.68,0.66,0.55,0.56,0.67,0.75,0.75. The AUC for the algorithms (XGBoost, RF, DT, KNN, NB) in the test set were 0.92, 0.91, 0.85, 0.84, and 0.88, respectively. The SHAP method identified eight predictor variables for BSH based on importance rankings, with the top four being IVS-AO Angle, LVMI, LVIDdI, and SBP, with IVS-AO Angle emerging as the most important predictor. The external validation of the RF model yielded an AUC of 0.86. Conclusion Machine learning can effectively predict BSH, with IVS-AO Angle identified as an independent predictor. The RF model, being simple to operate, can be applied to the risk management of BSH patients.

Author Correction: Oenothera biennis improves pregnancy outcomes by suppressing inflammation and fibrosis in an intra-uterine adhesion rat model

Delayed surgery among patients diagnosed with spinal disorders: Retrospective analysis

To determine the association between race and access to healthcare services with respect to the treatment of spinal cord disorders, a retrospective cohort study of patients receiving an initial diagnosis, two Boston hospitals, September 1, 2017, to June 1, 2018, follow-up through December 31, 2019. Data from patients (18–89 years) diagnosed with spinal cord disorders were extracted retrospectively from a centralized database. Kaplan-Meier curves and multivariable Cox proportional hazards models analyzed the time to spine surgery following initial diagnosis. Patient race was the primary explanatory variable, with five racial groups (Asian, Black, Hispanic, Other, and White) based on a combination of their self-reported race and ethnicity. Hispanic ethnicity (regardless of race), non-Hispanic ethnicity (designated Asian, Black, or White), and “Other” (non-Hispanic patients who designated their race as other than Asian, Black, or White; this included American Indian, Alaska Native, Native Hawaiian or other Pacific Islander, or two or more races). Among 56,186 patients (4% Asian, 7% Black, 5% Hispanic, 6% Other, 77% White) meeting inclusion criteria, Asian (hazard ratio (HR) 0.67 (0.55, 0.82)), Black (HR 0.55 (0.47, 0.63)), Hispanic (HR 0.43 (0.35, 0.52)), and Other (HR 0.59 (0.51, 0.69)) patients had significantly longer times to surgery compared with White patients.