Harnessing Renewable Lignocellulosic Potential for Sustainable Wastewater Purification.

Utilizing renewable lignocellulosic resources for wastewater remediation is crucial to achieving sustainable social development. However, the resulting by-products and the synthetic process characterized by complexity, high cost, and environmental pollution limit the further development of lignocellulose-based materials. Here, we developed a sustainable strategy that involved a new functional deep eutectic solvent (DES) to deconstruct industrial xylose residue into cellulose-rich residue with carboxyl groups, lignin with carboxyl and quaternary ammonium salt groups, and DES effluent rich in lignin fragments. Subsequently, these fractions equipped with customized functionality were used to produce efficient wastewater remediation materials in cost-effective and environmentally sound manners, namely, photocatalyst prepared by carboxyl-modified cellulose residue, biochar-based adsorbent originated from modified lignin, and flocculant synthesized by self-catalytic in situ copolymerization of residual DES effluent at room temperature. Under the no-waste principle, this strategy upgraded the whole components of waste lignocellulose into high-value-added wastewater remediation materials with excellent universality. These materials in coordination with each other can stepwise purify high-hazardous mineral processing wastewater into drinkable water, including the removal of 99.81% of suspended solids, almost all various heavy metal ions, and 97.09% chemical oxygen demand, respectively. This work provided promising solutions and blueprints for lignocellulosic resources to alleviate water shortages while also advancing the global goal of carbon neutrality.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study.

Background: The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making. Methods: Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator. Results: A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911-0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk. Conclusions: In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.

A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study.

Background: Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to predict the risk of LM using the nomogram in patients with osteosarcoma. Methods: A total of 1100 patients who were diagnosed as osteosarcoma between 2010 and 2019 in the Surveillance, Epidemiology and End Results (SEER) database were selected as the training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice. Results: A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model. Conclusions: In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.

The protective effects of procyanidin supplementation on PM2.5-induced acute cardiac injury in rats.

OBJECTIVE: Numerous epidemiological and experimental studies have indicated that ambient fine particulate matter (PM2.5) exposure can lead to myocardial injury by inhibiting oxidative stress and apoptosis. The effects of procyanidin (PC) on PM2.5-induced cardiovascular diseases (CVDs) are still unknown. The purpose of this study was to explore the protective effect of PC supplementation on PM2.5-induced oxidative stress and cardiomyocyte apoptosis in rats. METHOD: Rats were treated by gavage with three different PC concentrations (50, 100 and 200 mg/kg) for 21 days prior to exposure to 10 mg/kg PM2.5 suspension liquid by intratracheal instillation every other day for three times. We determined myocardial reactive oxygen species (ROS) and malondialdehyde (MDA) levels. Superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities in the myocardium were measured. The expression levels of apoptosis-related proteins, including p-Akt/Akt, Bcl-2, caspase-3 and Bax, were determined. In addition, histopathological examination was used to evaluate cardiac injury. RESULTS: PM2.5 exposure noticeably elevated the contents of MDA and ROS and decreased the activities of GSH-Px and SOD. PM2.5 exposure inhibited Bcl-2 expression and up-regulated caspase-3 and Bax expression in the myocardium of rats. The anti-apoptosis-related index p-Akt/Akt was reduced. Moreover, pretreatment with PC could attenuate these PM2.5-induced changes. However, remarkable differences in the protective effect of different PC doses did not exist. CONCLUSIONS: The results indicated that PC supplementation could effectively attenuate the oxidative stress and apoptosis induced by PM2.5 in rat myocardial tissue.

A deep belief network-based clinical decision system for patients with osteosarcoma.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study.

OBJECTIVE: The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES). METHODS: Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model. RESULTS: The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility. CONCLUSIONS: The tool built in this paper to predict 1- and 3-year survival in ES patients ( https://drwenleli0910.shinyapps.io/EwingApp/ ) has a good identification and predictive power.

Dynamic Predictive Models With Visualized Machine Learning for Assessing Chondrosarcoma Overall Survival.

Chondrosarcoma is a malignant bone tumor with a low incidence rate. Accurate risk evaluation is crucial for chondrosarcoma treatment. Due to the limited reliability of existing predictive models, we intended to develop a credible predictor for clinical chondrosarcoma based on the Surveillance, Epidemiology, and End Results data and four Chinese medical institutes. Three algorithms (Best Subset Regression, Univariate and Cox regression, and Least Absolute Shrinkage and Selector Operator) were used for the joint training. A nomogram predictor including eight variables-age, sex, grade, T, N, M, surgery, and chemotherapy-is constructed. The predictor provides good performance in discrimination and calibration, with area under the curve ≥0.8 in the receiver operating characteristic curves of both internal and external validations. The predictor especially had very good clinical utility in terms of net benefit to patients at the 3- and 5-year points in both North America and China. A convenient web calculator based on the prediction model is available at https://drwenle029.shinyapps.io/CHSSapp, which is free and open to all clinicians.

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study.

Background: Currently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma. Methods: Clinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers. Results: Eight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients. Conclusions: The clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

Cerebral infarction following bee stings: Case report and literature review.

Background: To date, only 25 cases of cerebral infarction following a bee or wasp sting have been reported. Due to its rarity, undefined pathogenesis, and unique clinical features, we report a case of a 62-year-old man with progressive cerebral infarction following bee stings, possibly related to vasospasm. Furthermore, we review relevant literature on stroke following bee or wasp stings. Case presentation: A 62-year-old retired male presented with progressive ischemic stroke after bee stings to the ear and face. Initial magnetic resonance imaging of the brain showed small punctate infarcts in the left medulla oblongata. Head and neck computed tomography angiography showed significant stenosis in the basilar artery and occlusion in the left V4 vertebral artery. The patient received intravenous alteplase (0.9 mg/kg) without symptomatic improvement. Digital subtraction angiography later demonstrated additional near occlusion in the left posterior cerebral artery (PCA). Thrombectomy was considered initially but was aborted due to hemodynamic instability. Repeated CT brain after 24 h showed acute infarcts in the left parieto-occipital region and left thalamus. The near occluded PCA was found to be patent again on magnetic resonance angiography (MRA) 25 days later. This reversibility suggests that vasospasm may have been the underlying mechanism. Unfortunately, the patient had persistent significant neurological deficits after rehabilitation one year later. Conclusion: Cerebral infarction following bee stings is rare. There are several proposed pathophysiological mechanisms. While the natural course of this phenomenon is not well characterized, early diagnosis and treatment are essential. Furthermore, it is important to establish standardized care procedures for this unique entity.

Shortening door-to-puncture time and improving patient outcome with workflow optimization in patients with acute ischemic stroke associated with large vessel occlusion.

OBJECTIVE: We aimed to evaluate door-to-puncture time (DPT) and door-to-recanalization time (DRT) without directing healthcare by neuro-interventionalist support in the emergency department (ED) by workflow optimization and improving patients' outcomes. METHODS: Records of 98 consecutive ischemic stroke patients who had undergone endovascular therapy (EVT) between 2018 to 2021 were retrospectively reviewed in a single-center study. Patients were divided into three groups: pre-intervention (2018-2019), interim-intervention (2020), and post-intervention (January 1st 2021 to August 16th, 2021). We compared door-to-puncture time, door-to-recanalization time (DRT), puncture-to-recanalization time (PRT), last known normal time to-puncture time (LKNPT), and patient outcomes (measured by 3 months modified Rankin Scale) between three groups using descriptive statistics. RESULTS: Our findings indicate that process optimization measures could shorten DPT, DRT, PRT, and LKNPT. Median LKNPT was shortened by 70 min from 325 to 255 min(P < 0.05), and DPT was shortened by 119 min from 237 to 118 min. DRT shortened by 132 min from 338 to 206 min, and PRT shortened by 33 min from 92 to 59 min from the pre-intervention to post-intervention groups (all P < 0.05). Only 21.4% of patients had a favorable outcome in the pre-intervention group as compared to 55.6% in the interventional group (P= 0.026). CONCLUSION: This study demonstrated that multidisciplinary cooperation was associated with shortened DPT, DRT, PRT, and LKNPT despite challenges posed to the healthcare system such as the COVID-19 pandemic. These practice paradigms may be transported to other stroke centers and healthcare providers to improve endovascular time metrics and patient outcomes.

Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients.

Background: Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms. Methods: A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals. Results: Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals. Conclusions: T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.

An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning.

Background: Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods: We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results: Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions: The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies.

A Visualized Dynamic Prediction Model for Lymphatic Metastasis in Ewing's Sarcoma for Smart Medical Services.

Background: This study aims to predict the lymphatic metastasis in Ewing's sarcoma (ES) patients by nomogram. The risk of lymphatic metastasis in patients with ES was predicted by the built model, which provided guidance for the clinical diagnosis and treatment planning. Methods: A total of 929 patients diagnosed with ES were enrolled from the year of 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The nomogram was established to determine predictive factors of lymphatic metastasis according to univariate and multivariate logistic regression analysis. The validation of the model performed using multicenter data (n = 51). Receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the prediction accuracy of the nomogram. Decision curve analysis (DCA) was implemented to illustrate the practicability of the nomogram clinical application. Based on the nomogram, we established a web calculator to visualize the risk of lymphatic metastases. We further plotted Kaplan-Meier overall survival (OS) curves to compare the survival time of patients with and without lymphatic metastasis. Results: In this study, the nomogram was established based on six significant factors (survival time, race, T stage, M stage, surgery, and lung metastasis), which were identified for lymphatic metastasis in ES patients. The model showed significant diagnostic accuracy with the value of the area under the curve (AUC) was 0.743 (95%CI: 0.714-0.771) for SEER internal validation and 0.763 (95%CI: 0.623-0.871) for multicenter data external validation. The calibration plot and DCA indicated that the model had vital clinical application value. Conclusion: In this study, we constructed and developed a nomogram with risk factors to predict lymphatic metastasis in ES patients and validated accuracy of itself. We found T stage (Tx OR = 2.540, 95%CI = 1.433-4.503, P < 0.01), M stage (M1, OR = 2.061, 95%CI = 1.189-3.573, P < 0.05) and survival time (OR = 0.982, 95%CI = 0.972-0.992, P < 0.001) were important independent factors for lymphatic metastasis in ES patients. Furthermore, survival time in patients with lymphatic metastasis or unclear situation (P < 0.0001) was significantly lower. It can help clinicians make better decisions to provide more accurate prognosis and treatment for ES patients.

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma.

Objective: In order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing's sarcoma (ES) based on machine learning (ML) algorithms. Methods: Clinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application. Results: LNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py). Conclusion: With the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.

Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.

Background: This study aimed to develop and validate machine learning (ML)-based prediction models for lung metastasis (LM) in patients with Ewing sarcoma (ES), and to deploy the best model as an open access web tool. Methods: We retrospectively analyzed data from the Surveillance Epidemiology and End Results (SEER) Database from 2010 to 2016 and from four medical institutions to develop and validate predictive models for LM in patients with ES. Patient data from the SEER database was used as the training group (n = 929). Using demographic and clinicopathologic variables six ML-based models for predicting LM were developed, and internally validated using 10-fold cross validation. All ML-based models were subsequently externally validated using multiple data from four medical institutions (the validation group, n = 51). The predictive power of the models was evaluated by the area under receiver operating characteristic curve (AUC). The best-performing model was used to produce an online tool for use by clinicians to identify ES patients at risk from lung metastasis, to improve decision making and optimize individual treatment. Results: The study cohort consisted of 929 patients from the SEER database and 51 patients from multiple medical centers, a total of 980 ES patients. Of these, 175 (18.8%) had lung metastasis. Multivariate logistic regression analysis was performed with survival time, T-stage, N-stage, surgery, and bone metastasis providing the independent predictive factors of LM. The AUC value of six predictive models ranged from 0.585 to 0.705. The Random Forest (RF) model (AUC = 0.705) using 4 variables was identified as the best predictive model of LM in ES patients and was employed to construct an online tool to assist clinicians in optimizing patient treatment. (https://share.streamlit.io/liuwencai123/es_lm/main/es_lm.py). Conclusions: Machine learning were found to have utility for predicting LM in patients with Ewing sarcoma, and the RF model gave the best performance. The accessibility of the predictive model as a web-based tool offers clear opportunities for improving the personalized treatment of patients with ES.

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface.

Background: This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods: Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results: Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions: In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface-a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S.

Prediction of post-hepatectomy liver failure and long-term prognosis after curative resection of hepatocellular carcinoma using liver stiffness measurement.

BACKGROUND AND STUDY AIMS: Post-hepatectomy liver failure (PHLF) is the main cause of perioperative death after hepatocellular carcinoma (HCC) resection. PHLF occurrence is related to both the hepatectomy volume and the degree of cirrhosis. Accurate preoperative assessment of the degree of cirrhosis may aid in reducing the incidence of PHLF. Several studies have shown that the liver stiffness measurement (LSM) is well correlated with cirrhosis. This study explored the relationship between LSM and PHLF occurrence after radical HCC resection and the effect on long-term prognosis. PATIENTS AND METHODS: We retrospectively analyzed the clinical data of 164 patients who underwent radical HCC resection at our center from January 2017 to January 2020. The related postoperative PHLF factors were analyzed. The LSM threshold in postoperative PHLF was calculated through receiver operating characteristic (ROC) curve analysis. Patients were grouped according to different LSM thresholds and survival analysis was performed. RESULTS: Forty-six patients experienced PHLF, of whom 19, 21, and 6 were classified as grades A, B, and C, respectively. Multivariate analysis indicated that LSM was an independent risk factor for PHLF after HCC surgery (OR = 1.174, P < 0.000). LSM (OR = 1.219, P < 0.000) and intraoperative bleeding (OR = 1.001, P = 0.047) were risk factors for grade B-C PHLF. The LSM threshold that predicted PHLF occurrence was 17.9 kPa (AUC = 0.831, P < 0.000) and 24.5 kPa (AUC = 0.867, P < 0.000) for grade B-C PHLF. LSM was correlated with PHLF severity (r = 0.439, P < 0.001). The median survival times were 32 vs 26 months (P = 0.016) for patients with LSM ≤ 17.9 kPa vs those with LSM > 17.9 kPa and 28 vs 24 months (P = 0.004) for patients with LSM ≤ 24.5 kPa vs those with LSM > 24.5 kPa. CONCLUSION: LSM is related to PHLF occurrence in patients undergoing HCC resection; a higher LSM is associated with the occurrence of more severe PHLF after surgery. In addition, LSM may aid in predicting long-term survival after liver resection in patients with HCC.

Potential biomarkers of acute myocardial infarction based on co-expression network analysis.

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in 'RAGE receptor binding' and 'negative regulation of T cell cytokine production'. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in 'cardiovascular diseases', of AKR1C3 in 'cardenolide biosynthesis', of MGAM in 'glycogenolysis', of B3GNT5 in 'glycosphingolipid biosynthesis' and of ACSL1 in 'icosapentaenoate biosynthesis II'. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

FOXO4 May Be a Biomarker of Postmenopausal Osteoporosis.

PURPOSE: Postmenopausal osteoporosis (PMOP) is a common and debilitating chronic disease, but it has just no cure options. The objective of this study was to identify genes associated with osteoporosis and reveal potential therapeutic targets. METHODS: Expression profiles from GSE13850 and GSE56815 datasets were combined for differential expression analysis. Extraction of intersecting genes from the combined datasets and the differentially expressed genes in GSE56814 were performed to construct a multi-scale embedded gene co-expression network analysis (MEGENA) to obtain module genes. Module genes with an area under the receiver operating characteristic curve (AUC) >0.60 were chosen to construct the least absolute shrinkage and selection operator (LASSO) model to obtain feature genes. A regulated network was constructed using differentially expressed micro-RNAs (miRNAs) in GSE74209 and feature genes. Finally, key genetic pathways and pathways of the Kyoto Encyclopedia of Genes and Genomes were identified and explored. RESULTS: The commonly identified differentially expressed genes involve oxidative phosphorylation and caffeine metabolism. We identified 66 modules with 2354 module genes based on MEGENA. CARD8, FOXO4, IL1R2, MPHOSPH6, MPRIP, MYOM1, PRR5L and YIPF4 were identified as feature genes by the LASSO model. Furthermore, predicted miRNA target genes included 8 genes associated with PMOP. The largest AUC was observed for FOXO4, which was found at the nexus of feature genes and miRNA-regulated genes and which correlated with the upregulation of dendritic cells. Moreover, FOXO4 was found to be involved in ABC transporters, as well as cocaine and nicotine addiction. CONCLUSION: FOXO4 may serve as potential biomarker and therapeutic target for PMOP.