Development of Machine Learning Model for Predicting Prolonged Operation Time in Lumbar Stenosis undergoing Posterior Lumbar Interbody Fusion: A Multi-center study.

BACKGROUND CONTEXT: Longer posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a method to accurately predict patients who are at risk for prolonged operation times. PURPOSE: This research aimed to develop a clinical model to predict prolonged operation time for patients undergoing PLIF procedures. STUDY DESIGN/SETTING: This study employs a machine-learning approach to analyze data retrospectively collected. PATIENT SAMPLE: 3233 patients diagnosed with lumbar spinal stenosis (LSS) had posterior lumbar interbody fusion (PLIF) at 22 hospitals in China from January 2015 to December 2022. OUTCOME MEASURES: The primary outcome was operation time. Prolonged operation time defined as exceeded 75% of the overall surgical duration, which mean exceeding 240 minutes. METHODS: A total of 3233 patients who underwent PLIF surgery with lumbar spinal stenosis (LSS) were divided into one training group and four test groups based on different district areas. The training group included 1569 patients, while Test1 had 541, Test2 had 403, Test3 had 351, and Test4 had 369 patients. Variables consisted of demographics, perioperative details, preoperative laboratory examinations and other Additional factors. Six algorithms were employed for variable screening, and variables identified by more than two screening methods were incorporated into the final model. In the training cohort, a 10-fold cross-validation (CV) and Bayesian hyperparameter optimization techniques were utilized to construct a model using eleven machine learning algorithms. Following this, the model was evaluated using four separate external test sets, and the mean Area Under the Curve (AUC) was computed to determine the best-performing model. Further performance metrics of the best model were evaluated, and SHapley Additive exPlanations(SHAP) were used for interpretability analysis to enhance decision-making transparency. Ultimately, an online calculator was created. RESULTS: Among the various machine learning models, the Random Forest achieved the highest performance in the validation set, with AUROC scores of 0.832 in Test1, 0.834 in Test2, 0.816 inTest3, 0.822 in Test4) compared with other machine learning models. The top contributing variables were number of levels fusion, pre-APTT, weight and age. The predictive model was further refined by developing a web-based calculator for clinical application. (https://wenle.shinyapps.io/PPOT_LSS/) CONCLUSIONS: This predictive model can facilitate identification of risk for prolonged operation time following PLIF surgery. Predictive calculators are expected to improve preoperative planning, identify patients with high risk factors, and help clinicians facilitating the improvement of treatment plans and the implementation of clinical intervention.

Pathomics models for CD40LG expression and prognosis prediction in glioblastoma.

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. In this study, we utilized pathomics analysis to explore the expression of CD40LG and its predictive value for the prognosis of GBM patients. We analyzed the expression differences of CD40LG in GBM tissue and normal brain tissue, along with performing survival prognosis analysis. Additionally, histopathological sections of GBM were used to screen for pathological features. Subsequently, SVM and LR pathomics models were constructed, and the models' performance was evaluated. The pathomics model was employed to predict CD40LG expression and patient prognosis. Furthermore, we investigated the potential molecular mechanisms through enrichment analysis, WGCNA analysis, immune correlation analysis, and immune checkpoint analysis. The expression level of CD40LG was significantly increased in GBM. Multivariate analysis demonstrated that high expression of CD40LG is a risk factor for overall survival (OS) in GBM patients. Five pathological features were identified, and SVM and LR pathomics models were constructed. Model evaluation showed promising predictive effects, with an AUC value of 0.779 for the SVM model, and the Hosmer-Lemeshow test confirmed the model's prediction probability consistency (P > 0.05). The LR model achieved an AUC value of 0.785, and the Hosmer-Lemeshow test indicated good agreement between the LR model's predicted probabilities and the true value (P > 0.05). Immune infiltration analysis revealed a significant correlation between the pathomics score (PS) and the degree of infiltration of activated DC cells, T cells CD4 naïve, Macrophages M2, Macrophages M1, and T cells CD4 memory resting. Our results demonstrate that the pathomics model exhibits predictability for CD40LG expression and GBM patient survival. These findings can be utilized to assist neurosurgeons in selecting optimal treatment strategies in clinical practice.

Regulator of calcineurin 3 as a novel predictor of diagnosis and prognosis in pan-cancer.

AIM: To assess diagnostic and prognostic value of regulator of calcineurin 3 (RCAN3) in various malignancies. METHODS: RCAN3 expression levels were assessed across pan-cancer data sets including various molecular and immune subtypes. Receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to determine the diagnostic and prognostic value of RCAN3 in pan-cancer, respectively. Enrichment analyses were used to identify RCAN3-associated terms and pathways. A special focus was placed on cervical squamous cell carcinoma and endocervical adenocarcinoma cervical cancer (CESC); we assessed the prognostic value of RCAN expression within distinct clinical subgroups and its effect on m6A modifications and immune infiltration. RESULTS: RCAN3 expression varied not only in different cancer types but also in different molecular and immune subtypes of cancers. RCAN3 displayed high accuracy in diagnosing and predicting cancers, and RCAN3 expression level was associated with the prognosis of certain cancers. CESC patients with a high RCAN3 level had a worse overall survival, disease-specific survival, and progression-free interval. RCAN3 expression was related to multiple m6A modifier genes and immune cells. CONCLUSION: In general, RCAN3 can serve as a novel biomarker for the diagnosis and prognosis in pan-cancer, especially in CESC. It may represent a promising molecular target for developing new treatments. However, our analysis is limited to bioinformatic predictions, and further biological experiments are necessary to verify our results.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

Infection and chronic disease activate a systemic brain-muscle signaling axis.

Infections and neurodegenerative diseases induce neuroinflammation, but affected individuals often show nonneural symptoms including muscle pain and muscle fatigue. The molecular pathways by which neuroinflammation causes pathologies outside the central nervous system (CNS) are poorly understood. We developed multiple models to investigate the impact of CNS stressors on motor function and found that Escherichia coli infections and SARS-CoV-2 protein expression caused reactive oxygen species (ROS) to accumulate in the brain. ROS induced expression of the cytokine Unpaired 3 (Upd3) in Drosophila and its ortholog, IL-6, in mice. CNS-derived Upd3/IL-6 activated the JAK-STAT pathway in skeletal muscle, which caused muscle mitochondrial dysfunction and impaired motor function. We observed similar phenotypes after expressing toxic amyloid-ß (Aß42) in the CNS. Infection and chronic disease therefore activate a systemic brain-muscle signaling axis in which CNS-derived cytokines bypass the connectome and directly regulate muscle physiology, highlighting IL-6 as a therapeutic target to treat disease-associated muscle dysfunction.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS: Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS: The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma.

OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. CONCLUSION: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Unveiling research trends in the prognosis of osteosarcoma: A bibliometric analysis from 2000 to 2022.

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone tumor in children and adolescents, producing osteoid and immature bone. Numerous high quality studies have been published in the OSA field, however, no bibliometric study related to this area has been reported thus far. Therefore, the present study retrieved the published data from 2000 to 2022 to reveal the dynamics, development trends, hotspots and future directions of the OSA. Methods: Publications regard to osteogenic sarcoma and prognosis were searched in the core collection on Web of Science database. The retrieved publications were analyzed by publication years, journals, categories, countries, citations, institutions, authors, keywords and clusters using the two widely available bibliometric visualization tools, VOS viewer (Version 1.6.16), Citespace (Version 6.2. R1). Results: A total of 6260 publications related to the current topic were retrieved and analyzed, revealing exponential increase in the number of publications with an improvement in the citations on the OSA over time, in which China and the USA are the most productive nations. Shanghai Jiao Tong University, University of Texas System and Harvard University are prolific institutions, having highest collaboration network. Oncology Letters and Journal of Clinical Oncology are the most productive and the most cited journals respectively. The Wang Y is a prominent author and articles published by Bacci G had the highest number of citations indicating their significant impact in the field. According to keywords analysis, osteosarcoma, expression and metastasis were the most apparent keywords whereas the current research hotspots are biomarker, tumor microenvironment, immunotherapy and DNA methylation. Conclusion: Our findings offer valuable information for researchers to understand the current research status and the necessity of future research to mitigate the mortality of the OS patients.

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 .

Development and validation of a clinical prediction model for the risk of distal metastasis in intrahepatic cholangiocarcinoma: a real-world study.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. METHODS: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. RESULTS: This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. CONCLUSIONS: This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA.

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.

Elucidation of the anti-colon cancer mechanism of Phellinus baumii polyphenol by an integrative approach of network pharmacology and experimental verification.

Colon cancer, a prevalent malignant tumor affecting the digestive system, presents a substantial risk to human health due to its high occurrence and mortality rates. Phellinus baumii polyphenol (PBP), a natural product derived from traditional Chinese medicine, has gained widespread popularity due to its low toxicity and minimal side effects, compared to radiation and chemotherapy. This study used an integrated approach of network pharmacology and experimental verification to elucidate the anti-colon cancer effects of PBP and its potential mechanisms. In network pharmacology, the identification of relevant targets involved a comprehensive search across multiple databases using keywords such as "active components of PBP" and "colon cancer". Venn diagram analysis was subsequently performed to ascertain the shared targets. To identify the key active components and core targets, we constructed a network of "Disease-Drug-Pathways-Targets" and a protein-protein interaction (PPI) network among the targets using Cytoscape 3.9.1. Furthermore, molecular docking was carried out to predict the binding affinity and conformation between the main active compounds (davallialactone and citrinin) of PBP and the core targets (TP53, STAT3, CASP3, CTNNB1, PARP1, MYC). To validate our findings, in vitro experiments were conducted. We verified that PBP exerted an anti-colon cancer effect on human colon cancer HCT116 cells by significantly inhibiting cell proliferation, promoting apoptosis and arresting the cell cycle in S phase by using Cell Counting Kit-8 (CCK-8) and flow cytometry. Finally, we determined the key regulatory proteins related to apoptosis and the cell cycle by western blot analysis, and proposed the potential mechanism by which PBP exerts an anti-colon cancer effect by inducing the caspase-dependent mitochondrial-mediated intrinsic apoptotic pathway and arresting the cell cycle in S phase in HCT116 cells. These results suggest that PBP possesses substantial potential for the treatment of colon cancer and may serve as a viable alternative therapeutic strategy in colon cancer treatment.

Cu2+ -Anchored Carbon Nano-Photocatalysts for Visible Water Splitting to Boost Hydrogen Cuproptosis.

Both hydrogen (H2 ) and copper ions (Cu+ ) can be used as anti-cancer treatments. However, the continuous generation of H2 molecules and Cu+ in specific sites of tumors is challenging. Here we anchored Cu2+ on carbon photocatalyst (Cu@CDCN) to allow the continuous generation of H2 and hydrogen peroxide (H2 O2 ) in tumors using the two-electron process of visible water splitting. The photocatalytic process also generated redox-active Cu-carbon centers. Meanwhile, the Cu2+ residues reacted with H2 O2 (the obstacle to the photocatalytic process) to accelerate the two-electron process of water splitting and cuprous ion (Cu+ ) generation, in which the Cu2+ residue promoted a pro-oxidant effect with glutathione through metal-reducing actions. Both H2 and Cu+ induced mitochondrial dysfunction and intracellular redox homeostasis destruction, which enabled hydrogen therapy and cuproptosis to inhibit cancer cell growth and suppress tumor growth. Our research is the first attempt to integrate hydrogen therapy and cuproptosis using metal-enhanced visible solar water splitting in nanomedicine, which may provide a safe and effective cancer treatment.

A pan-cancer analysis reveals the diagnostic and prognostic role of CDCA2 in low-grade glioma.

BACKGROUND: Cell division cycle associated 2 (CDCA2), a member of the cell division cycle associated proteins (CDCA) family, is crucial in the regulation of cell mitosis and DNA repair. CDCA2 was extensively examined in our work to determine its role in a wide range of cancers. METHODS: CDCA2 differential expression was studied in pan-cancer and in diverse molecular and immunological subgroups in this research. Additionally, the diagnostic and prognostic significance of CDCA2 in pan-cancer was also evaluated using receiver operating characteristic (ROC) and Kaplan-Meier (KM) curves. Prognostic value of CDCA2 in distinct clinical subgroups of lower grade glioma (LGG) was also investigated and a nomogram was constructed. Lastly, potential mechanisms of action of CDCA2 were interrogated including biological functions, ceRNA networks, m6A modification and immune infiltration. RESULTS: CDCA2 is shown to be differentially expressed in a wide variety of cancers. Tumors are diagnosed and forecasted with a high degree of accuracy by CDCA2, and the quantity of expression CDCA2 is linked to the prognosis of many cancers. Additionally, the expression level of CDCA2 in various subgroups of LGG is also closely related to prognosis. The results of enrichment analyses reveal that CDCA2 is predominantly enriched in the cell cycle, mitosis, and DNA replication. Subsequently, hsa-miR-105-5p is predicted to target CDCA2. In addition, 4 lncRNAs were identified that may inhibit the hsa-miR-105-5p/CDCA2 axis in LGG. Meanwhile, CDCA2 expression is shown to be associated to m6A-related genes and levels of immune cell infiltration in LGG. CONCLUSION: CDCA2 can serve as a novel biomarker for the diagnosis and prognosis in pan-cancer, especially in LGG. For the development of novel targeted therapies in LGG, it may be a potential molecular target. However, to be sure, we'll need to do additional biological experiments to back up our results from bioinformatic predictions.

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.

A cross-sectional study on the mental health of patients with COVID-19 1 year after discharge in Huanggang, China.

OBJECTIVE: This study is aimed to investigate the mental health status of COVID-19 survivors 1 year after discharge from hospital and reveal the related risk factors. METHODS: From April 11 to May 11, 2021, 566 COVID-19 survivors in Huanggang city were recruited through their primary doctors. A total of 535 participants (94.5%) admitted to participate in the survey and completed the questionnaires. Five scales were applied including 7-Items Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and Fatigue Scale-14. The chi-square and the Fisher's exact test were used to evaluate the classification data, multivariate logistic regression was used to explore the related factors of sleep quality, fatigue, anxiety, depression, and post-traumatic stress disorder (PTSD). RESULTS: One year after being discharged, of the 535 COVID-19 survivors, 252 (47.1%) had poor sleep quality; 157 (29.3%) had the symptoms of fatigue; 84 (15.7%),112 (20.9%), and 130 (24.3%) suffered from symptoms of anxiety, depression, and PTSD, respectively. The logistic regression analysis showed that history of chronic disease was risk factor for poor sleep quality (OR 2.501; 95% CI, 1.618-3.866), fatigue (OR 3.284; 95% CI 2.143-5.033), PTSD (OR 2.323; 95% CI 1.431-3.773) and depression (OR 1.950; 95% CI 1.106-3.436) in COVID-19 survivors. Smoking contributed to the poor sleep quality (OR 2.005; 95% CI 1.044-3.850), anxiety (OR 4.491; 95% CI 2.276-8.861) and depression (OR 5.459; 95% CI 2.651-11.239) in survivors. Drinking influenced fatigue (OR 2.783; 95% CI 1.331-5.819) and PTSD (OR 4.419; 95% CI 1.990-9.814) in survivors. Compared with college-educated survivors, survivors with high school education were at higher risk for poor sleep quality (OR 1.828; 95% CI 1.050-3.181) and PTSD (OR 2.521; 95% CI 1.316-4.830), and survivors with junior high school education were at higher risk for PTSD (OR 2.078; 95% CI 1.039-4.155). Compared with overweight survivors (BMI ≥ 23.0), survivors with normal BMI (18.5-22.9) (OR 0.600; 95% CI 0.405-0.889) were at lower risk for fatigue. While being housewife (OR 0.390; 95% CI 0.189-0.803) was protective factor for fatigue and having more family members was protective factor for PTSD (OR 0.404 95% CI 0.250-0.653) in survivors. CONCLUSIONS: One year after infection, poor sleep quality, fatigue, anxiety, depression, and PTSD, still existed in a relatively high proportion of COVID-19 survivors. Chronic disease history was an independent risk factor for poor sleep quality, fatigue, depression, and PTSD. Participants with low education levels were more likely to have mental problems than the others. We should focus on the long-term psychological impact of COVID-19 on survivors, and the government should apply appropriate mental health services to offer psychiatric support.

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules.

Objective: This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms. Methods: A retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC). Results: The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost. Conclusions: Combining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Phellinus baumii Polyphenol: A Potential Therapeutic Candidate against Lung Cancer Cells.

Phellinus baumii, a fungus that grows on mulberry trees and is used in traditional Chinese medicine, exerts therapeutic effects against various diseases, including cancer. Polyphenols, generally considered to be antioxidants, have antitumor and proapoptotic effects. In this study, we identified the composition of Phellinus baumii polyphenol (PBP) and characterized its 17 chemical components by UPLC-ESI-QTOF-MS. Furthermore, to clarify the potential mechanism of PBP against Lung Cancer Cells, network pharmacology and experimental verification were combined. Molecular docking elucidated the binding conformation and mechanism of the primary active components (Osmundacetone and hispidin) to the core targets CASP3, PARP1 and TP53. In addition, potential molecular mechanisms of PBP predicted by network pharmacology analysis were validated in vitro. PBP significantly inhibited the human lung cancer A549 cells and showed typical apoptotic characteristics, without significant cytotoxicity to normal human embryonic kidney (HEK293) cells. Analysis using flow cytometry and western blot indicated that PBP caused apoptosis, cell cycle arrest, reactive oxygen species (ROS) accumulation, and mitochondrial membrane potential (MMP) depression in A549 cells to exercise its antitumor effects. These results reveal that PBP has great potential for use as an active ingredient for antitumor therapy.

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma.

Simple summary: Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects. Background: Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer. Methods: Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds. Results: The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients. Conclusions: The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

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.