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Abstract We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions.

Background: Thyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. Methods: We collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. Results: A total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805–0.927), for the validation cohort it was 0.944 (95 % CI: 0.903–0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997–1.001). Calibration curves and AUC suggest that the model has good accuracy. Conclusions: We developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Abstract Objective Prostate cancer (PC) is a significant disease affecting men’s health worldwide. More than 60% of patients over 65 years old and more than 80% are diagnosed with localized PC. The current choice of treatment modalities for localized PC and whether overtreatment is controversial. Therefore, we wanted to construct a nomogram to predict the risk factors associated with cancer-specific survival (CSS) and overall survival (OS) in elderly patients with localized PC while assessing the survival differences in surgery and radiotherapy for elderly patients with localized PC. Methods Data of patients with localized PC over 65 years were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for CSS and OS. Nomograms predicting CSS and OS were built using multivariate Cox regression models. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the potential clinical value of this model. Results A total of 90,434 patients over 65 years and diagnosed with localized PC from 2010 to 2018 were included in the study. All patients were randomly assigned to the training set (n = 63,328) and the validation set (n = 27,106). Univariate and multivariate Cox regression model analysis showed that age, race, marriage, T stage, surgical, radiotherapy, prostate-specific antigen (PSA), and Gleason score (GS) were independent risk factors for predicting CSS in elderly patients with localized PC. Age, race, marriage, surgery, radiotherapy, PSA, and GS were independent risk factors for predicting OS in elderly patients with localized PC. The c-index of the training and validation sets for the predicted CSS is 0.802(95%CI:0.788–0.816) and 0.798(95%CI:0.776–0.820, respectively). The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704–0.720) and 0.724(95%:0.714–0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability. Conclusion We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with reasonable accuracy, reliability and potential clinical value, the model can be used for clinically assisted decision-making.

Abstract Background Neuroblastoma (NB) is the most common extracranial malignant solid tumor in children. Due to drug resistance to radiotherapy and chemotherapy, mainly due to the existence of cancer stem cells (CSCs), some children still have a poor prognosis. Therefore, researchers have focused their attention on CSCs. Our research group successfully constructed cancer stem cell-like cells named Piwil2-iCSCs by reprogramming human preputial fibroblasts (FBs) with the PIWIL2 gene in the early stage, and Piwil2-iCSCs were confirmed to induce the formation of embryonic tumors. PiRNAs, noncoding small RNAs that interact with PIWI proteins, play important roles in a variety of tumors. Therefore, our study aimed to explore the role of differentially expressed (DE) piRNAs derived from sequencing of Piwil2-iCSCs in NB. Methods The DE piRNAs in Piwil2-iCSCs were screened using high-throughput sequencing and further verified in NB tissues and cells. An unknown piRNA, named piRNA-MW557525, showed obvious downregulation in NB. Thus we studied the effect of piRNA-MW557525 on the biological behavior of NB through in vitro and in vivo experiments. On this basis, we successfully constructed a stably transfected NB cell line overexpressing piRNA-MW557525 and performed transcriptome sequencing to further explore the mechanism of piRNA-MW557525 in NB. Results In vitro, piRNA-MW557525 inhibited NB cell proliferation, migration and invasion and induced apoptosis; in vivo, piRNA-MW557525 significantly reduced the volume and weight of tumors and inhibited their proliferation, migration and invasion. piRNA-MW557525 overexpression induced G0/G1 phase arrest in NB cells via activation of the P53-P21-CDK2-Cyclin E signaling pathway thus inhibiting NB growth. Conclusions Our findings show that piRNA-MW557525 functions as a tumor suppressor gene in NB and may serve as an innovative biomarker and possible therapeutic target for NB.

Abstract Prostate Cancer (PC) is the most common male nonskin tumour in the world, and most diagnosed patients are over 65 years old. The main treatment for PC includes surgical treatment and nonsurgical treatment. Currently, for nonsurgically treated elderly patients, few studies have evaluated their prognostic factors. Our aim was to construct a nomogram that could predict cancer-specific survival (CSS) in nonsurgically treated elderly PC patients to assess their prognosis-related independent risk factors. Patient information was obtained from the Surveillance, Epidemiology and End Results (SEER) database, and our target population was nonsurgically treated PC patients who were over 65 years old. Independent risk factors were determined using both univariate and multivariate Cox regression models. A nomogram was built using a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested using the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to examine the potential clinical value of this model. A total of 87,831 elderly PC patients with nonsurgical treatment in 2010–2018 were included in the study and were randomly assigned to the training set (N = 61,595) and the validation set (N = 26,236). Univariate and multivariate Cox regression model analyses showed that age, race, marital status, TNM stage, chemotherapy, radiotherapy modality, PSA and GS were independent risk factors for predicting CSS in nonsurgically treated elderly PC patients. The C-index of the training set and the validation set was 0.894 (95% CI 0.888–0.900) and 0.897 (95% CI 0.887–0.907), respectively, indicating the good discrimination ability of the nomogram. The AUC and the calibration curves also show good accuracy and discriminability. We developed a new nomogram to predict CSS in elderly PC patients with nonsurgical treatment. The model is internally validated with good accuracy and reliability, as well as potential clinical value, and can be used for clinical aid in decision-making.

Abstract Background Osteosarcoma is the most prevalent primary malignant bone tumor. The primary treatment for osteosarcoma is a combination of chemotherapy and surgery. However, there has been no recent progress in the role of chemotherapy in improving the long-term survival of osteosarcoma patients. This study aims to analyze the factors that affect chemotherapy in patients with osteosarcoma and explore the challenges and survival benefits of chemotherapy. Methods Patient data were downloaded from The Surveillance, Epidemiology, and End Results database. Univariable and multivariable logistic regressions were used to analyze the factors affecting patients receiving chemotherapy. Kaplan–Meier (K–M) curve was used to analyze the survival benefit of chemotherapy in patients with osteosarcoma. Finally, we used annual percentage change (APC) to evaluate the annual changes in chemotherapy treatment rates and trends in 5-year survival rates in osteosarcoma patients. Results A total of 2157 osteosarcoma patients were included, of which 1887 patients received chemotherapy. Factors affecting patients receiving chemotherapy included age, primary tumor site, tumor size, N stage, M stage, and surgery. The K–M curve showed that older patients could benefit significantly from chemotherapy. The APC results showed no significant change in the chemotherapy treatment rate and 5-year overall survival rate of osteosarcoma patients. Conclusion Chemotherapy is an irreplaceable treatment for patients with osteosarcoma. However, in recent years, there has been no significant progress in chemotherapy for osteosarcoma, and the long-term survival of patients has not improved significantly. New chemotherapeutic drugs or drug delivery systems are urgently needed to improve the prognosis of patients with osteosarcoma.

Abstract Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004–2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725–0.769) and 0.765 (95% CI 0.736–0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710–0.773) and 0.786 (95% CI 0.755–0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making.

Objective: Partial bladder outlet obstruction(pBOO) is the most common cause of lower urinary tract symptoms (LUTS) and significantly affects the quality of life. Long-term pBOO can cause changes in bladder structure and function, referred to as bladder remodeling. The pathogenesis of pBOO-induced bladder remodeling has yet to be fully understood, so effective treatment options are lacking. Our study aimed to explore how pBOO-induced bladder remodeling brings new strategies for treating pBOO. Methods: A rat model of pBOO was established by partial ligation of the bladder neck, and the morphological changes and fibrosis changes in the bladder tissues were detected by H&E and Masson trichrome staining. Furthermore, EMT(epithelial-mesenchymal transition) related indicators and related pathway changes were further examined after TGF- β treatment of urothelial cells SV-HUC-1. Finally, the above indicators were tested again after using the PI3K inhibitor. Subsequently, RNA sequencing of bladder tissues to identify differential genes and related pathways enrichment and validated by immunofluorescence and western blotting analysis. Results: The pBOO animal model was successfully established by partially ligating the bladder neck. H&E staining showed significant changes in the bladder structure, and Masson trichrome staining showed significantly increased collagen fibers. RNA sequencing results significantly enriched in the cytoskeleton, epithelial-mesenchymal transformation, and the PI3K-AKT-mTOR signaling pathway. Immunofluorescence and western blotting revealed EMT and cytoskeletal remodeling in SV-HUC-1 cells after induction of TGF- β and in the pBOO bladder tissues. The western blotting showed significant activation of the PI3K-AKT-mTOR signaling pathway in SV-HUC-1 cells after induction of TGF-β and in pBOO bladder tissues. Furthermore, EMT and cytoskeletal damage were partially reversed after PI3K pathway inhibition using PI3K inhibitors. Conclusions: In the pBOO rat model, the activation of the PI3K-AKT-mTOR signaling pathway can mediate the cytoskeletal remodeling and the EMT to induce fibrosis in the bladder tissues. PI3K inhibitors partially reversed EMT and cytoskeletal damage.

Osteosarcoma is a bone tumor with a high incidence in children and adolescents. Chemotherapy for osteosarcoma is limited, and effective targeted drugs are urgently needed to treat osteosarcoma. Exosomes as a natural nano drug delivery platform have been widely studied and proven to have good drug delivery performance. However, the low production of exosomes hinders its development as a carrier. Exosome mimetics (EMs) as an alternative product of exosomes solve the problem of low production of exosomes and maintain the good performance of exosomes as carriers. In this study, bone marrow mesenchymal stem cells (BMSCs) were sequentially extruded to generate EMs to encapsulate doxorubicin (EM-Dox) to treat osteosarcoma. The results showed that we successfully prepared EMs of BMSC, and EM-Dox was prepared using an active-loading approach. Our engineered EM-Dox demonstrated significantly more potent tumor inhibition activity and fewer side effects than free doxorubicin. This novel biological nanomedicine system provides a promising opportunity to develop novel precision medicine for osteosarcoma.

Abstract Objective To discuss the clinical and prognostic indicators of pediatric malignant rhabdoid tumor of the kidney (MRTK), and to increase the understanding of the occurrence and development of MRTK. Methods From July 2014 to September 2021, all cases were confirmed by postoperative pathological examination. Among the 42 patients, there were 25 males and 17 females, with a median age of 10 (1–84) months. Abdominal mass or hematuria were the main clinical manifestations. Preoperative chemotherapy was performed in 9 cases (VC). The tumor stages were stage I-IV. Preoperative metastasis was found in 9 cases; the most common site was the lung. Postoperative patients received conventional chemotherapy, including VDACE regimen and UH-1 regimen. Among the 42 children in this group, survival at follow-up in this study was 26.2%(11/42). Results Preoperative anemia was found by univariate analysis, hypertension and hypercalcemia had shorter survival time. In addition, tumor-related factors had a significant impact on survival, with incomplete tumor resection, lymph node metastasis, stage III-IV had a lower survival rate. The impact of postoperative factors on survival included postoperative complications had a lower survival rate. The children were younger than 12 months, preoperative metastasis, no chemotherapy was performed after surgery was an independent risk factor for the prognosis of MRTK. Conclusion The main clinical manifestations about MRTK were abdominal mass and hematuria. Preoperative chemotherapy did not significantly improve the prognosis. Postoperative chemotherapy can significantly improve the survival rate. Diagnosis depends on clinical manifestations, imaging, histopathology, immunohistochemistry and other comprehensive judgment. Age less than 12 months, preoperative metastasis, and no postoperative chemotherapy were independent risk factors for prognosis.

Abstract Objective Thyroid carcinoma (TC) is the most common endocrine tumor in the human body. Papillary thyroid carcinoma (PTC) accounts for more than 80% of thyroid cancers. Accurate prediction of elderly PTC can help reduce the mortality of patients. We aimed to construct a nomogram predicting cancer-specific survival (CSS) in elderly patients with PTC. Methods Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. Univariate and multivariate Cox regression models were used to screen the independent risk factors for patients with PTC. The nomogram of elderly patients with PTC was constructed based on the multivariate Cox regression model. We used the concordance index (C-index), the area under the receiver operating characteristic curve (AUC) and the calibration curve to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the clinical value of the model. Results A total of 14,138 elderly patients with PTC were included in this study. Patients from 2004 to 2015 were randomly divided into a training set (N = 7379) and a validation set (N = 3141), and data from 2016 to 2018 were divided into an external validation set (N = 3618). Proportional sub-distribution hazard model showed that age, sex, tumor size, histological grade, TNM stage, surgery and chemotherapy were independent risk factors for prognosis. In the training set, validation set and external validation set, the C-index was 0.87(95%CI: 0.852–0.888), 0.891(95%CI: 0.866–0.916) and 0.931(95%CI:0.894–0.968), respectively, indicating that the nomogram had good discrimination. Calibration curves and AUC suggest that the prediction model has good discrimination and accuracy. Conclusions We constructed a new nomogram to predict CSS in elderly patients with PTC. Internal cross-validation and external validation indicate that the model has good discrimination and accuracy. The predictive model can help doctors and patients make clinical decisions.

Abstract Background Exosomes derived from mesenchymal stem cells (MSCs) have shown to have effective application prospects in the medical field, but exosome yield is very low. The production of exosome mimetic vesicles (EMVs) by continuous cell extrusion leads to more EMVs than exosomes, but whether the protein compositions of MSC-derived EMVs (MSC-EMVs) and exosomes (MSC-exosomes) are substantially different remains unknown. The purpose of this study was to conduct a comprehensive proteomic analysis of MSC-EMVs and MSC-exosomes and to simply explore the effects of exosomes and EMVs on wound healing ability. This study provides a theoretical basis for the application of EMVs and exosomes. Methods In this study, EMVs from human umbilical cord MSCs (hUC MSCs) were isolated by continuous extrusion, and exosomes were identified after hUC MSC ultracentrifugation. A proteomic analysis was performed, and 2315 proteins were identified. The effects of EMVs and exosomes on the proliferation, migration and angiogenesis of human umbilical vein endothelial cells (HUVECs) were evaluated by cell counting kit-8, scratch wound, transwell and tubule formation assays. A mouse mode was used to evaluate the effects of EMVs and exosomes on wound healing. Results Bioinformatics analyses revealed that 1669 proteins in both hUC MSC-EMVs and hUC MSC-exosomes play roles in retrograde vesicle-mediated transport and vesicle budding from the membrane. The 382 proteins unique to exosomes participate in extracellular matrix organization and extracellular structural organization, and the 264 proteins unique to EMVs target the cell membrane. EMVs and exosomes can promote wound healing and angiogenesis in mice and promote the proliferation, migration and angiogenesis of HUVECs. Conclusions This study presents a comprehensive proteomic analysis of hUC MSC-derived exosomes and EMVs generated by different methods. The tissue repair function of EMVs and exosomes was herein verified by wound healing experiments, and these results reveal their potential applications in different fields based on analyses of their shared and unique proteins.

Abstract Background At present, there are few studies on renal pelvic transitional cell carcinoma (RPTCC) in elderly patients in the literature. The study aims to establish a new nomogram of cancer-specific survival (CSS) in elderly patients with T1-T3N0M0 RPTCC and validate its reliability. Methods This study downloaded the data of 1375 elderly patients with T1-T3N0M0 RPTCC in the Surveillance, Epidemiology, and Final Results (SEER) database from 2004 to 2018. Patients were randomly divided into training cohort (n = 977) and validation cohort (n = 398). Proportional subdistribution hazard analyse was applied to determine independent prognostic factors. Based on these factors, we constructed a compting risk model nomogram. We use the calibration plots, the area under the receiver operating characteristics curve (AUC), concordance index (C-index), and decision curve analysis (DCA) to validate predictive performance and clinical applicability. Patients were divided into low-risk group and high-risk group based on nomogram risk score. Kaplan–Meier curve was applied to analyze the difference in survival curve between the two groups of patients. Results We found that the risk factors affecting CSS in elderly patients with T1-T3N0M0 RPTCC are surgery, AJCC stage, laterality, tumor size, histological grade, and tumour laterality. Based on these factors, we established a nomogram to predict the CSS of RPTCC patients at 1-, 3-, and 5-year. The calibration plots showed that the predicted value was highly consistent with the observed value. In the training cohort and validation cohort, the C-index of the nomogram were 0.671(95% CI 0.622–0.72) and 0.679(95% CI 0.608–0.750), respectively, the AUC showed similar results. The DCA suggests that namogram performs better than the AJCC stage system. The Kaplan–Meier curve showed that CSS of patients was significantly higher in the low-risk group. Conclusions In this study, the SEER database was used for the first time to create and validate a new nomogram prediction model for elderly patients with T1-T3N0M0 RPTCC. Compared with the traditional AJCC stage system, our new nomogram can more accurately predict the CSS of elderly patients with T1-T3N0M0 RPTCC, which is helpful for patient prognosis assessment and treatment strategies selection.

Abstract Background The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC. Methods The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA). Results The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage. Conclusion A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.

Background. Long-term extensive use of glucocorticoids will lead to hormonal necrosis of the femoral head, and osteoblasts play an important role in the prevention of osteonecrosis. However, there is no complete cure for necrosis of the femoral head. Mesenchymal stem cell- (MSCs-) derived exosomes are widely used for the repair of various tissue lesions. Therefore, the aim of this study was to investigate the mechanism of dexamethasone- (DEX-) induced osteoblast apoptosis and the therapeutic effect of human umbilical cord MSC- (hucMSC-) derived exosome mimetic vesicles (EMVs) on osteoblast-induced apoptosis by DEX. Methods. The viability and apoptosis of primary MC3T3-E1 cells were determined by the Cell Counting Kit-8 (CCK-8), FITC-Annexin V/PI staining and immunoblot. The intracellular levels of reactive oxygen species (ROS) after DEX treatment were measured by 2′, 7′ -dichlorodihydrofluorescein diacetate (DCFH-DA) staining. In this study, hucMSC-EMVs and N-acetyl-l-cysteine (NAC) were used as therapeutic measures. The expression of B-cell lymphoma 2-associated X, Bcl 2, HO-1, and nuclear factor erythroid-derived 2-like 2 and MAPK- signaling pathway in osteogenic cell MC3T3-E1 cells treated with Dex was analyzed by the immunoblotting. Results. DEX significantly induced osteoblasts MC3T3-E1 apoptosis and ROS accumulation. MAPK-signaling pathway was activated in MC3T3-E1 after DEX treatment. hucMSC-EMVs intervention significantly downregulated DEX-induced MAPK-signaling pathway activation and ROS accumulation. In addition, hucMSC-EMVs can reduce the apoptosis levels in osteoblast MC3T3-E1 cells induced by DEX. Conclusions. Our study confirmed that hucMSC-EMVs regulates MAPK-signaling pathway and ROS levels to inhibit DEX-induced osteoblast apoptosis.

BackgroundRenal cell carcinoma (RCC) is a common malignant tumor in the elderly, with an increasing trend in recent years. We aimed to construct a nomogram of cancer-specific survival (CSS) and overall survival (OS) in elderly patients with nonmetastatic renal cell carcinoma (nmRCC).MethodsClinicopathological information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program in elderly patients with nmRCC from 2010 to 2015. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression analyses were used to identify independent risk factors for patient outcomes in the training cohort. A nomogram was constructed based on these independent risk factors to predict the 1-, 3-, and 5-year CSS and OS in elderly patients with nmRCC. We used a range of methods to validate the accuracy and reliability of the model, including the calibration curve, consistency index (C-index), and the area under the receiver operating curve (AUC). Decision curve analysis (DCA) was used to test the clinical utility of the model.ResultsA total of 12,116 patients were enrolled in the study. Patients were randomly assigned to the training cohort (N = 8,514) and validation cohort (N = 3,602). In the training cohort, univariate and multivariate Cox regression analysis showed that age, marriage, tumor histological type, histological tumor grade, TN stage, tumor size, and surgery are independent risk factors for prognosis. A nomogram was constructed based on independent risk factors to predict CSS and OS at 1-, 3-, and 5- years in elderly patients with nmRCC. The C-index of the training and validation cohorts in CSS were 0.826 and 0.831; in OS, they were 0.733 and 0.734, respectively. The AUC results of the training and validation cohort were similar to the C-index. The calibration curve indicated that the observed value is highly consistent with the predicted value, meaning the model has good accuracy. DCA results suggest that the clinical significance of the nomogram is better than that of traditional TNM staging.ConclusionsWe built a nomogram prediction model to predict the 1-, 3- and 5-year CSS and OS of elderly nmRCC patients. This model has good accuracy and discrimination and can help doctors and patients make clinical decisions and active monitoring.

PurposePediatric testicular yolk sac tumor is a rare malignant germ cell tumor and there is a lack of large clinical studies. The purpose of this study is to summarize the clinical characteristics of pediatric testicular yolk tumor and evaluate the prognostic factors.Materials and methodsThe medical records of children with testicular yolk sac tumor in one pediatric medical centre in China from January 2005 to January 2021 were retrospectively investigated. Data regarding clinical characteristics, treatment and prognosis were collected.ResultsA total of 109 patients with a median diagnosed age of 18 months (range 2–69) were included in this study; of them 100 were diagnosed as stage I, 6 as stage II and 3 as stage IV. All patients underwent radical orchiectomy, and 61 of them underwent postoperative chemotherapy. The mean follow-up time was 61.3 months (range 3–259), during that time, 8 patients experienced relapse. The five-year overall survival was 90.6% (95% CI 84.6%–96.7%). Univariate Cox regression analysis showed that disease stage, relapse, maximum tumor diameter, and alpha-fetoprotein returning to normal within 2 months postoperatively were risk factors for survival (HRs of 25.43, 26.43, 1.48 and 0.08, respectively, p

PurposeTesticular yolk sac tumor (TYST) is a rare malignant germ cell tumor that mainly occurs in young men. Due to the low incidence of yolk sac tumors, there is a lack of prospective cohort studies with large samples. We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with TYST.Materials and methodsPatient information was downloaded from the Surveillance, Epidemiology and End Results (SEER) database. We enrolled all patients with TYST from 2000 to 2018, and all patients were randomly divided into a training set and a validation set. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors for patients. We constructed a nomogram based on the multivariate Cox regression model to predict 1-, 3-, and 5-year CSS in patients with TYST. We used a series of validation methods to test the accuracy and reliability of the model, including the concordance index (C-index), calibration curve and the area under the receiver operating characteristic curve (AUC).Results619 patients with TYST were enrolled in the study. Univariate and multivariate Cox regression analysis showed that age, T stage, M stage and chemotherapy were independent risk factors for CSS. A nomogram was constructed to predict the patient's CSS. The C-index of the training set and the validation set were 0.901 (95%CI: 0.859–0.847) and 0.855 (95%CI: 0.865–0.845), respectively, indicating that the model had excellent discrimination. The AUC showed the same results. The calibration curve also indicated that the model had good accuracy.ConclusionsIn this study, we constructed the nomogram for the first time to predict the CSS of patients with TYST, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

ObjectiveAccording to statistics, patients with high-risk prostate cancer (PC) account for about 15% of prostate cancer diagnoses, and high-risk patients usually have a poor prognosis due to metastasis and recurrence and have a high mortality rate. Therefore, the accurate prediction of prognostic-related risk factors in middle-aged high-risk PC patients between 50 and 65 can help reduce patient mortality. We aimed to construct new nomograms for predicting cancer-specific survival (CSS) and Overall survival (OS) in middle-aged high-risk PC patients.MethodsData for patients aged between 50 and 65 years old and diagnosed with high-risk PC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for CSS and OS in patients. Nomograms predicting CSS and OS were developed based on multivariate Cox regression models. The concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and the calibration curve are used to detect the accuracy and discrimination of the model. Decision curve analysis (DCA) is used to detect the potential clinical value of this model.ResultsBetween 2010 and 2018, 1,651 patients diagnosed with high-risk PC and aged 50–65 years were included. In this study, the training group (n = 1,146) and the validation group (n = 505) were randomly assigned in a ratio of 7:3. The results showed that M stage, Gleason (GS) and surgical mode were independent risk factors for CSS; marital status, T stage, M stage, surgical mode, and GS were independent risk factors for OS. The C-index for predicting CSS in the training and validation groups are 0.84 and 0.811, respectively; the C-index for predicting OS in the training and validation groups are 0.824 and 0.784, respectively. The AUC and the calibration curves also showed good accuracy and discrimination.ConclusionsWe constructed new nomograms to predict CSS and OS in middle-aged high-risk PC patients. The prediction tools showed good accuracy and reliability, which can help clinicians and patients to make better clinical decisions.

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.

Abstract Background To summarize and analyze the epidemiological characteristics, treatment and corresponding curative effect of triradiate cartilage injury(TCI) in children after trauma, to provide a theoretical basis for early diagnosis and improvement of treatment. Methods The TCI was classified according to Bucholz classification, and the final curative effect was evaluated with Harris Hip Score and imaging examination during follow-up. Finally, a comprehensive analysis was made by reviewing the cases in the literature combined with the patients in our hospital. Results A total of 15 cases (18 hips) of triradiate cartilage injuries were collected in our hospital. There was 1 hip with type I injury, nine hips with type II injury, two hips with type IV injury, one hip with type V injury and five hips with type VI injury. Among the 12 cases with complete follow-up, the bone bridge was found in or around the triradiate cartilage in 8 cases, early fusion of triradiate cartilage occurred in 5 patients, 3 cases had hip dysplasia, 4 cases had a subluxation of the femoral head, and HHS was excellent in 8 cases and good in 4 cases. Conclusion The early diagnosis of TCI is still a difficult problem. Conservative treatment is often the first choice. The overall prognosis of acetabular fractures involving triradiate cartilage is poor. The formation of the bone bridge in triradiate cartilage usually indicates the possibility of premature closure, which may lead to severe complications of post-traumatic acetabular dysplasia and subluxation of the femoral head.

BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH.

ObjectiveProstate cancer (PC) is the second leading cause of cancer death in men in the United States after lung cancer in global incidence. Elderly male patients over 65 years old account for more than 60% of PC patients, and the impact of surgical treatment on the prognosis of PC patients is controversial. Moreover, there are currently no predictive models that can predict the prognosis of elderly PC patients undergoing surgical treatment. Therefore, we aimed to construct a new nomogram to predict cancer-specific survival (CSS) in elderly PC patients undergoing surgical treatment.MethodsData for surgically treated PC patients aged 65 years and older were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for elderly PC patients undergoing surgical treatment. A nomogram of elderly PC patients undergoing surgical treatment was developed based on the multivariate Cox regression model. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the predictive model. Decision curve analysis (DCA) was used to examine the potential clinical value of this model.ResultsA total of 44,975 elderly PC patients undergoing surgery in 2010–2018 were randomly assigned to the training set (N = 31705) and validation set (N = 13270). the training set was used for nomogram development and the validation set was used for internal validation. Univariate and multivariate Cox regression model analysis showed that age, marriage, TNM stage, surgical style, chemotherapy, radiotherapy, Gleason score(GS), and prostate-specific antigen(PSA) were independent risk factors for CSS in elderly PC patients undergoing surgical treatment. The C index of the training set and validation indices are 0.911(95%CI: 0.899–0.923) and 0.913(95%CI: 0.893–0.933), respectively, indicating that the nomogram has a good discrimination ability. The AUC and the calibration curves also show good accuracy and discriminability.ConclusionsTo our knowledge, our nomogram is the first predictive model for elderly PC patients undergoing surgical treatment, filling the gap in current predictive models for this PC patient population. Our data comes from the SEER database, which is trustworthy and reliable. Moreover, our model has been internally validated in the validation set using the C-index,AUC and the and the calibration curve, showed that the model have good accuracy and reliability, which can help clinicians and patients make better clinical decision-making. Moreover, the DCA results show that our nomogram has a better potential clinical application value than the TNM staging system.

ObjectiveProstate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.MethodsInformation on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D’Amico risk stratification system.Results135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.ConclusionsWe developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

ObjectiveTo investigate the role of chemokines in Wilms tumours, especially their chemotaxis to immune cells and the role of DNA methylation in regulating the expression level of chemokines.MethodsRNAseqV2 gene expression and clinical data were downloaded from the TARGET database. DNA methylation data were downloaded from the GEO and cBioPortal database. The difference analysis and Kaplan-Meier(KM) analysis of chemokines were performed by edgeR package. Then predictive model based on chemokines was constructed by lasso regression and multivariate COX regression. ROC curve, DCA curve, Calibration curve, and Nomogram were used to evaluate the prognostic model. MCPcounter and Cibersort algorithm was used to calculate the infiltration of immune cells in Wilms tumour and para-tumour samples. Then the difference analysis of the immune cells was performed. The relationship between chemokines and immune cells were calculated by Pearson correlation. In addition, DNA methylation differences between Wilms tumour and para-tumour samples was performed. The correlation between DNA methylation and mRNA expression was calculated by Pearson correlation. Western blot(WB)and immunofluorescence were used to confirm the differential expression of CX3CL1 and T cells, and the correlation between them.ResultsA total of 16 chemokines were differentially expressed in tumour and para-tumour samples. A total of seven chemokines were associated with survival. CCL2 and CX3CL1 were positively correlated with prognosis, while high expression of CCL3, CCL8, CCL15, CCL18 and CXCL9 predicted poor prognosis. By lasso regression and multivariate COX regression, CCL3, CCL15, CXCL9 and CX3CL1 were finally included to construct a prediction model. The model shows good prediction ability. MCPcounter and Cibersort algorithm both showed that T cells were higher in para-tumour tissues than cancer tissues. Correlation analysis showed that CX3CL1 had a strong correlation with T cells. These were verified by Weston blot and immunofluorescence. DNA methylation analysis showed that various chemokines were different in para-tumours and tumours. CX3CL1 was hypermethylated in tumours, and the degree of methylation was negatively correlated with mRNA expression.Conclusion1. There is low T cell infiltration in nephroblastoma. 2. Chemokines such as CX3CL1 indicate a favourable prognosis and positively correlate with the number of T cells. 3. chemokines such as CX3CL1 are negatively regulated by DNA hypermethylation.

BackgroundWilms tumor (WT) is the most common tumor in children. We aim to construct a nomogram to predict the cancer-specific survival (CSS) of WT in children and externally validate in China.MethodsWe downloaded the clinicopathological data of children with WT from 2004 to 2018 in the SEER database. At the same time, we used the clinicopathological data collected previously for all children with WT between 2013 and 2018 at Children's Hospital of Chongqing Medical University (Chongqing, China). We analyzed the difference in survival between the patients in the SEER database and our hospital. Cox regression analysis was used to screen for significant risk factors. Based on these factors, a nomogram was constructed to predict the CSS of children with WT. Calibration curve, concordance index (C-index), the area under the receiver operating curve (AUC) and decision curve analysis (DCA) was used to evaluate the accuracy and reliability of the model.ResultsWe included 1,045 children with WT in the SEER database. At the same time, we collected 112 children with WT in our hospital. The Kaplan-Meier curve suggested that children in China with WT had a higher mortality rate than those in the United States. Cox regression analysis revealed that age, lymph node density (LND), and tumor stage were significant prognostic factors for the patients in the SEER database. However, the patients in our hospital only confirmed that the tumor stage and the number of positive regional lymph nodes were significant factors. The prediction model established by the SEER database had been validated internally and externally to prove that it had good accuracy and reliability.ConclusionWe have constructed a survival prognosis prediction model for children with WT, which has been validated internally and externally to prove accuracy and reliability.

ObjectivePapillary renal cell carcinoma (pRCC) is the second most common type of renal cell carcinoma and an important disease affecting older patients. We aimed to establish a nomogram to predict cancer-specific survival (CSS) in elderly patients with pRCC.MethodsPatient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) project, and we included all elderly patients with pRCC from 2004 to 2018. All patients were randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox proportional risk regression models were used to identify patient independent risk factors. We constructed a nomogram based on a multivariate Cox regression model to predict CSS for 1-, 3-, and 5- years in elderly patients with pRCC. A series of validation methods were used to validate the accuracy and reliability of the model, including consistency index (C-index), calibration curve, and area under the Subject operating curve (AUC).ResultsA total of 13,105 elderly patients with pRCC were enrolled. Univariate and multivariate Cox regression analysis suggested that age, tumor size, histological grade, TNM stage, surgery, radiotherapy and chemotherapy were independent risk factors for survival. We constructed a nomogram to predict patients' CSS. The training and validation cohort's C-index were 0.853 (95%CI: 0.859–0.847) and 0.855 (95%CI: 0.865–0.845), respectively, suggesting that the model had good discrimination ability. The AUC showed the same results. The calibration curve also indicates that the model has good accuracy.ConclusionsIn this study, we constructed a nomogram to predict the CSS of elderly pRCC patients, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

BackgroundClear cell renal cell carcinoma (ccRCC) is expected in the elderly and poor prognosis. We aim to explore prognostic factors of ccRCC in the elderly and construct a nomogram to predict cancer-specific survival (CSS) in elderly patients with ccRCC.MethodsClinicopathological information for all elderly patients with ccRCC from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression models were used to identify the independent risk factors for CSS. A new nomogram was constructed to predict CSS at 1-, 3-, and 5 years in elderly patients with ccRCC based on independent risk factors. Subsequently, we used the consistency index (C-index), calibration curves, and the area under the receiver operating curve (AUC) and decision curve analysis (DCA) to test the prediction accuracy of the model.ResultsA total of 33,509 elderly patients with ccRCC were enrolled. Univariate and multivariate Cox regression analyses results showed that age, sex, race, marriage, tumor size, histological grade, tumor, nodes, and metastases (TNM) stage, and surgery were independent risk factors for CSS in elderly patients with ccRCC. We constructed a nomogram to predict CSS in elderly patients with ccRCC. The C-index of the training cohort and validation cohort was 0.81 (95% CI: 0.802–0.818) and 0.818 (95% CI: 0.806–0.830), respectively. The AUC of the training cohort and validation cohort also suggested that the prediction model had good accuracy. The calibration curve showed that the observed value of the prediction model was highly consistent with the predicted value. DCA showed good clinical application value of the nomogram.ConclusionIn this study, we explored prognostic factors in elderly patients with ccRCC. We found that age, sex, marriage, TNM stage, surgery, and tumor size were independent risk factors for CSS. We constructed a new nomogram to predict CSS in elderly patients with ccRCC with good accuracy and reliability, providing clinical guidance for patients and physicians.

Abstract Background Acetabular roof lesions (ARLs) in children are uncommon and may involve a variety of diseases. The acetabular roof is the main weight-bearing area of the hip joint, and lesions affecting the acetabular roof lead to fluid accumulation in the hip joint, causing hip pain and claudication. Methods for diagnosing and treating ARLs and the prognosis after treatment are rarely reported. We present our experience in a group of children and teenagers with ARLs to retrospectively explore the clinical and imaging features and histopathological diagnosis and report the treatment methods and follow-up observations. Methods Patients with ARLs admitted to the Children’s Hospital of Chongqing Medical University from April 2011 to September 2018 were selected retrospectively. We collected the basic information of patients (name, sex, age), main symptoms and signs, results of various laboratory tests, treatment methods, and intraoperative observations through the hospital medical record system. We collected X-ray, computed tomography (CT), magnetic resonance imaging (MRI) and pathological examination data through the Picture Archiving and Communications System. Follow-up data were collected through an outpatient medical record system, telephone, and chat software (such as WeChat). We used descriptive methods to analyze the lesion structure and destruction mode based on the imaging findings and histopathological diagnosis. Results There were 14 ARL patients, including 6 with eosinophilic granuloma (EG), 5 with chronic osteomyelitis, 2 with bone cyst, and 1 with tuberculosis. One patient underwent percutaneous needle biopsy, 2 underwent open biopsy, and 11 underwent curettage; among them, 5 patients also underwent bone grafting. These lesions had no characteristic imaging findings, and the diagnosis was mainly based on histopathological examination. Most patients showed complete symptom resolution and good hip function at the 1-year follow-up. Conclusion ARLs are not common in children. The types of lesions are diverse and mostly benign, with EG being most common. Malignant tumors may also occur, such as Ewing’s sarcoma, non-Hodgkin’s lymphoma, metastases and neuroblastoma. CT and MRI can be helpful in diagnosing certain cases, but incisional biopsy is required in most cases.

BackgroundOsteosarcoma (OSC) and Ewing's sarcoma (EWS) are children's most common primary bone tumors. The purpose of the study is to develop and validate a new nomogram to predict the cancer-specific survival (CSS) of childhood OSC and EWS.MethodsThe clinicopathological information of all children with OSC and EWS from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen children's independent risk factors for CSS. These risk factors were used to construct a nomogram to predict the CSS of children with OSC and EWS. A series of validation methods, including calibration plots, consistency index (C-index), and area under the receiver operating characteristic curve (AUC), were used to validate the accuracy and reliability of the prediction model. Decision curve analysis (DCA) was used to validate the clinical application efficacy of predictive models. All patients were divided into low- and high-risk groups based on the nomogram score. Kaplan-Meier curve and log-rank test were used to compare survival differences between the two groups.ResultsA total of 2059 children with OSC and EWS were included. All patients were randomly divided into training cohort 60% (N = 1215) and validation cohort 40% (N = 844). Univariate and multivariate analysis suggested that age, surgery, stage, primary site, tumor size, and histological type were independent risk factors. Nomograms were established based on these factors to predict 3-, 5-, and 8-years CSS of children with OSC and EWS. The calibration plots showed that the predicted value was highly consistent with the actual value. In the training cohort and validation cohort, the C-index was 0.729 (0.702–0.756) and 0.735 (0.702–0.768), respectively. The AUC of the training cohort and the validation cohort also showed similar results. The DCA showed that the nomogram had good clinical value.ConclusionWe constructed a new nomogram to predict the CSS of OSC and EWS in children. This predictive model has good accuracy and reliability and can help doctors and patients develop clinical strategies.

ObjectiveWith the development of osteosarcoma treatment, limb salvage surgery is gradually replacing amputation as the primary surgical option. Most pediatric osteosarcomas of the limbs undergo limb-salvage surgery. We aimed to use propensity score matching (PSM) analysis test the difference in cancer-specific mortality (CSM) between amputation and limb-salvage surgery in pediatric patients with Osteosarcoma of the limbs. PSM is a statistical method used to deal with data from an Observational Study. The PSM method is designed to reduce the influence of biases and confounding variables to make a more reasonable comparison between experimental and control groups.MethodsPatient information was downloaded from the SEER (surveillance, epidemiology, and End Results) database from 2004 to 2018. We included all primary pediatric osteosarcoma patients who underwent limb salvage or amputation. Multivariate logistic regression models were used to explore the factors influencing patient choice of amputation. Differences in CSM and other causes of mortality (OSM) between limb salvage and amputation were analyzed using cumulative incidence plots and competitive risk regression tests after 1:1 proportional propensity score matching.ResultsA total of 1,058 pediatric patients with limbs Osteosarcoma were included. Patients who underwent amputations were more likely to be male (OR 1.4, P = 0.024) and more likely to have distant metastasis (OR 2.1, P < 0.001). Before propensity matching, CSM was 1.4 times higher in patients undergoing amputation than in patients undergoing limb salvage (P = 0.017) and 3.4 times higher in OSM (P = 0.007). After adjustment for propensity matching, CSM was 1.5 times higher in patients undergoing amputation than in patients undergoing limb salvage (P = 0.028), but there was no significant difference in OSM (HR 3.2, P = 0.078).ConclusionsOur results suggested that amputation is associated with a 1.5-fold increase in CSM in pediatric patients with limbs Osteosarcoma. Therefore, in the surgical selection of pediatric patients with Osteosarcoma, limb salvage surgery should be the first choice in the absence of other contraindications.

BackgroundRenal cell carcinoma (RCC) is the most common renal malignancy in adults, and chromophobe renal cell carcinoma (chRCC) is the third most common subtype of RCC. We aimed to construct a competitive risk model to predict cancer-specific survival (CSS) in elderly patients with chRCC.MethodsThe clinicopathological information of the patients was downloaded from the SEER database, and the patients were randomly divided into the training and validation cohorts. Patients' risk factors for cancer-specific death (CSM) were analyzed using proportional subdistribution hazard (SH). We constructed a competitive risk model to predict the CSS of elderly chRCC patients. Consistency index (C-index), the area under receiver operating curve (AUC), and a calibration curve were used to validate the model's accuracy. Decision curve analysis (DCA) was used to test the clinical value of the model.ResultsA total of 3,522 elderly patients with chRCC were included in the analysis. Patients were randomly assigned to either the training cohort (N = 2,474) or the validation cohort (N = 1,048). SH analysis found that age, race, T, N, and M stage, tumor size, and surgery were risk factors for CSM. We constructed a competitive risk model to predict patients' CSS. In the training set, the model predicted patients' 1-, 3-, and 5-year CSS with C-indices of 82.2, 80.8, and 78.2, respectively. The model predicted patient 1-, 3-, and 5-year CSS in the validation cohort with C-indices of 84.7, 83.4, and 76.9, respectively. The calibration curve showed that the model's predicted value is almost consistent with the observed value, which indicated that the model has good accuracy. The AUC of the training set and validation queue also suggested that the model has good discrimination. The clinical utility of the DCA model in predicting patients' CSS is higher than that of traditional TNM staging.ConclusionsWe constructed a competitive risk model to predict CSS in elderly patients with chRCC. The model has good accuracy and reliability, which can help doctors and patients to make clinical decisions and follow-up strategies.

BackgroundRenal cell carcinoma (RCC) is one of the most common cancers in middle-aged patients. We aimed to establish a new nomogram for predicting cancer-specific survival (CSS) in middle-aged patients with non-metastatic renal cell carcinoma (nmRCC).MethodsThe clinicopathological information of all patients from 2010 to 2018 was downloaded from the SEER database. These patients were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, we used the consistency index (C-index), calibration curve, and area under receiver operating curve (AUC) to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model.ResultsA total of 27,073 patients were included in the study. These patients were randomly divided into a training set (N = 18,990) and a validation set (N = 8,083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, histological tumor grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict patients' 1-, 3-, and 5-year CSS. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training and validation set ranged from 77.7 to 80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value.ConclusionWe found that independent risk factors for CSS in middle-aged patients with nmRCC were age, sex, histological tumor grade, T stage, tumor size, and surgery. We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision making.

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

BackgroundRenal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.MethodsThe clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.ResultsA total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.ConclusionThis study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

Thermoacoustic instabilities occur when heat release is coupled with pressure fluctuation, which may cause performance degradation of the combustor and serious structural damage. This study focued on an active control method using discharge plasma and showed experimentally that discharge plasma can make a difference in controlling the thermoacoustic instabilities in a Rijke tube. A vertically placed Rijke tube thermoacoustic system using induction heating tungsten mesh as a heat source was built. The results show that the high repetition rate discharge can effectively suppress the thermoacoustic oscillations in the Rijke tube and that they will not re-occur for some time. Additionally, their effectiveness depended more on average power than energy per pulse. Combining the collected pressure, schlieren data, and theoretical analysis, it can be suggested that the plasma discharge could heat the inlet airflow, which could influence the heat exchange and then could break thermo-acoustic coupling, and its high-frequency pressure perturbation might increase the dissipation of the energy of sound.

This paper proposes an improved wavelength modulation spectroscopy with the 2nd harmonics normalized by the 1st harmonics (WMS-2f/1f) spectral fitting method using the orthogonal test in selection of the initial parameters. The method is implemented and validated experimentally in measurement of the temperature of diluted H2O in air (1 atm, 291K, 0.7%) by the WMS-2f/1f technique. The transition center wavelength targets near 1344 nm. Results demonstrate that the sum-square-error (SSE) between the calculated and measured WMS-2f/1f spectral profiles decreases significantly within given updating times when the optimized initial parameters are used. Compared to the conventional method, the optimized initial parameters can make the fitting routine converge more efficiently. The temperature of the vapor inferred from the proposed spectral fitting method are in good agreement with the true values.

The experimental investigations into the thermoacoustic instability in a Rijke tube are presented. In order to capture the dynamics of the temperature, a single-ended tunable diode laser absorption spectroscopy (TDLAS) technique was developed, with a measurement rate of 5 kHz. The temperature was found to fluctuate periodically at a dominant frequency of 230 Hz, corresponding to the fundamental frequency of the Rijke tube used in the experiment. The flame chemiluminescence was detected by a high-speed camera to demonstrate flame response to thermoacoustic instability. It was evident that the flame front stretched regularly and had jagged edges. To quantitate the fluctuations of chemiluminescence intensity, the relative area was defined. According to the result, the intensity also oscillated at 230 Hz. Furthermore, the same feature was found in regard to pressure at the exit of the Rijke tube. Compared with temperature and chemiluminescent intensity, the pressure oscillations presented the most approximate standard waveform, as they suffered the least disruptions. The results indicated that the dominant frequencies of temperature, chemiluminescent intensity and pressure were consistent, in accordance with the fundamental frequency of the Rijke tube in the experiment. In addition, etalon effects on the TDLAS signals were mitigated efficiently by a lowpass filter.