Construction of risk prediction model for hypothermia during pancreaticoduodenectomy.

Purpose: To investigate the factors influencing hypothermia during pancreaticoduodenectomy and establish and verify a prediction model. Method: The clinical data of patients undergoing pancreaticoduodenectomy in Hunan People's Hospital between January 1, 2022 and October 15, 2022 were analysed. The patients were divided into a hypothermia group (n = 302) and a non-hypothermia group (n = 164) according to whether hypothermia occurred during surgery. A binary logistic regression model was used to analyse the independent risk factors for hypothermia in patients undergoing pancreaticoduodenectomy. A risk prediction model was established, and R software was used to plot a column graph. The predictive value of the model was evaluated using the receiver operating characteristic (ROC) curve. Results: Among the 466 patients undergoing pancreaticoduodenectomy, 302 (64.81 %) had hypothermia, including 154 men and 148 women, with a median age of 58.6 (38-86) years. The binary logistic regression analysis showed that low body mass index (BMI), room temperature at the time of entry, intraoperative flushing fluid volume and peritoneal flushing fluid temperature were independent risk factors for intraoperative hypothermia in patients undergoing pancreaticoduodenal surgery (P < 0.05). A multivariate logistic regression analysis (backward logistic regression) was used to establish the prediction model. The area under the ROC curve was 0.927, P ≤ 0.001, the sensitivity was 0.921 and the specificity was 0.848, indicating good differentiation by the prediction model. Conclusion: The nomogram constructed using four independent risk factors: BMI, room temperature at the time of entry, intraoperative peritoneal flushing fluid volume and intraoperative peritoneal flushing fluid temperature, has good predictive efficacy and good clinical application value for predicting intraoperative hypothermia in patients undergoing pancreaticoduodenectomy.

Factors Associated with Extended Hospital Stay and its Impact on Subsequent Short-term Readmission with Tuberculosis Patients.

BACKGROUND: This study aimed to explore the factors associated with extended length of stay (LOSE) for patients with tuberculosis (TB) in China, and construct a nomogram to predict it. In addition, the impact of extended hospital stay on short-term readmission after discharge was assessed. METHODS: A retrospective observational study was conducted at Changsha Central Hospital, from January 2018 to December 2020. Patients (≥18 years who were first admitted to hospital for TB treatment) with non-multidrug-resistant TB were selected using the World Health Organization's International Classification of Diseases, 10th Revision (ICD-10-CM), and the hospital's electronic medical record system. RESULTS: A multivariate logistic regression analysis was used to evaluate the associations between TB and LOSE. The relationship between length of hospital stay and readmission within 31 days after discharge was assessed using a univariate Cox proportional risk model. A total of 14259 patients were included in this study (13629 patients in the development group and 630 in the validation group). The factors associated with extended hospital stays were age, smear positivity, extrapulmonary involvement, surgery, transfer from other medical structures, smoking, chronic liver disease, and drug-induced hepatitis. There was no statistical significance in the 31-day readmission rate of TB between the LOSE and length of stay≤14 days groups (hazards ratio: 0.92, 95% CI: 0.80-1.06, P=0.229). CONCLUSION: LOSE with TB was influenced by several patient-level factors, which were combined to construct a nomograph. The established nomograph can help hospital administrator and clinicians to identify patients with TB requiring extended hospital stays, and more efficiently plan for treatment programs and resource needs.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors. AIM: To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients. METHODS: This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA). RESULTS: The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit. CONCLUSION: Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

Novel model for cancer-specific survival of patients with pulmonary sarcomatoid carcinoma: A population-based analysis and external validation.

BACKGROUND/OBJECTIVE: We aimed to develop a comprehensive and effective nomogram for predicting cancer-specific survival (CSS) in patients with pulmonary sarcomatoid carcinoma (PSC). METHODS: Data for patients diagnosed with PSC between 2004 and 2018 from the Surveillance, Epidemiology, and End Results database were retrospectively collected and randomly divided into training and internal validation sets. We then retrospectively recruited patients diagnosed with PSC to construct an external validation cohort from the Southwest Hospital. A prognostic nomogram for CSS was established using independent prognostic factors that were screened from the multivariate Cox regression analysis. The performance of the nomogram was evaluated using area under the receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration diagrams, and decision curve analysis (DCA). The clinical value of the nomogram and tumor, nodes, and metastases (TNM) staging system was compared using the C-index and net reclassification index (NRI). RESULTS: Overall, 1356 patients with PSC were enrolled, including 876, 377, and 103 in the training, internal validation, and external validation sets, respectively. The C-index and ROC curves, calibration, and DCA demonstrated satisfactory nomogram performance for CSS in patients with PSC. In addition, the C-index and NRI of the nomogram suggested a significantly higher nomogram value than that of the TNM staging system. Subsequently, a web-based predictor was developed to help clinicians obtain this model easily. CONCLUSIONS: The prognostic nomogram developed in this study can conveniently and precisely estimate the prognosis of patients with PSC and individualize treatment, thereby assisting clinicians in their shared decision-making with patients.

Establishment and application of severity assessment system for patients with delayed encephalopathy caused by carbon monoxide poisoning.

OBJECTIVE: To identify the factors related to the severity of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) and establishment of a clinical nomogram assessment model. METHODS: Clinical data of 200 patients with DEACMP admitted to the First Hospital of Yulin from January 2019 to December 2022 were retrospectively analyzed. The patients were classified into severe and non-severe groups according to the severity of the disease. Clinical data was collected from both groups. Logistic regression was applied to analyze the risk factors for disease severity of DEACMP patients. The risk prediction model of the nomogram was constructed by incorporating risk factors, and its effectiveness was verified. Model differentiation performance was evaluated using the Respondent Operating Characteristic (ROC) Curve. Model calibration curve was adopted for fitting the situation of evaluation. The consistency of the model was evaluated by Hosmer-Lemeshow (H-L) analysis. RESULT: Age, coma time out of exposure, creatine kinase (CK), caspase, and red blood cell distribution width (RDW) were the risk factors for the severe DEACMP. A nomogram prediction model was built based on the above indicators. The area under the curve (AUC) of the model in predicting severe DEACMP was 0.961 (95% CI: 0.934-0.988) and 0.929 (95% CI: 0.841-1) in the training and test sets, respectively. The H-L test showed good goodness of fit (χ2 = 4.468, P = 0.813). The calibration curve showed a good agreement between the predicted values of the nomogram and the actual observed values. CONCLUSION: Age, coma time out of exposure, CK, caspase, and RDW were significantly correlated with the severity of DEACMP patients. The nomogram prediction model incorporating the five indicators has certain clinical reference value for predicting the severe DEACMP and could be used as an accurate and rapid clinical assessment tool.

Prediction model using risk factors associated with anastomotic leakage after minimally invasive esophagectomy.

Objective: To explore the risk factors of anastomotic leakage after minimally invasive esophagectomy (MIE) and to build a prediction model of the probability of postoperative anastomotic leakage. Methods: Clinical data of patients undergoing MIE, admitted in the Fourth Hospital of Hebei Medical University from March 2018 to March 2022, were retrospectively selected, and risk factors of anastomotic leakage after MIE were analyzed by univariate and multivariate logistic regression. A prediction nomogram model was established based on the independent risk factors, and its prediction effect was evaluated. Results: A total of 308 patients were included. Thirty patients had postoperative anastomotic leakage, with an incidence of 9.74%. Logistic regression analysis showed that age, postoperative delirium, pleural adhesion, postoperative pulmonary complications, high postoperative white blood cell count and low lymphocyte count were risk factors for postoperative anastomotic leakage. A nomograph prediction model was constructed based on these risk factors. The predicted probability of occurrence of the nomograph model was consistent with the actual probability of occurrence. The calculated C-index value (Bootstrap method) was 0.9609, indicating that the nomograph prediction model had a good discrimination ability. By drawing the receiver operating characteristic (ROC) curve, we showed that the area under the curve (AUC) of the nomograph prediction model was 0.9609 (95%CI: 0.937-0.985), which indicated a good prediction efficiency of the model. Conclusions: The nomograph prediction model based on the independent risk factors of anastomotic leakage after MIE can accurately predict the probability of postoperative anastomotic leakage.

Prediction of ablation zone disappearance after microwave ablation for the patients with papillary thyroid microcarcinoma using nomograph.

Objectives: To construct a prognostic nomogram to predict the ablation zone disappearance for patients with papillary thyroid microcarcinoma (PTMC) after microwave ablation (MWA). Materials and methods: From April 2020 to April 2022, patients with PTMC who underwent MWA treatment were collected retrospectively. Ultrasound (US) or contrast-enhanced ultrasound (CEUS) was performed at 1 day, 1, 3, 6, 12, 18 and 24 months after MWA to observe the curative effect after ablation. The volume, volume reduction rate (VRR) and complete disappearance rate of the ablation zone at each time point were calculated. Univariate and multivariate logistic regression analysis were used to determine the prognostic factors associated with the disappearance of the ablation zone after MWA, and the nomogram was established and validated. Results: 72 patients with PTMCs underwent MWA were enrolled into this study. After MWA, no tumor progression (residual, recurrence or lymph node metastasis) and major postoperative complications occurred. The ablation zone in 28 (38.89%) patients did not completely disappear after MWA in the follow-up period. Three variables, including age (odds ratio [OR]: 1.216), calcification type (OR: 12.283), initial maximum diameter (OR: 2.051) were found to be independent prognostic factors predicting ablation zone status after MWA by multivariate analysis. The above variables and outcomes were visualized by nomogram (C-index=0.847). Conclusions: MWA was a safe and effective treatment for PTMC. Older patients with macrocalcification and larger size PTMCs were more unlikely to obtain complete disappearance of ablation zones. Incomplete disappearance of ablation zone was not related to recurrence.

DNA methylation analysis reveals the effect of arsenic on gestational diabetes mellitus.

BACKGROUND: Arsenic (As) exposure is one of the risk factors for gestational diabetes mellitus (GDM). This study aimed to explore the effect of As-exposure on DNA methylation in GDM and to establish a risk assessment model of GDM in As exposed pregnant women. METHOD: We collected elbow vein blood of pregnant women before delivery to measure As concentration and DNA methylation data. Then compared the DNA methylation data and established a nomogram. RESULT: We identified a total of 10 key differentially methylated CpGs (DMCs) and found 6 corresponding genes. Functions were enriched in Hippo signaling pathway, cell tight junction, prophetic acid metabolism, ketone body metabolic process, and antigen processing and presentation. A nomogram was established that can predict GDM risks (c-index = 0.595, s:p = 0.973). CONCLUSION: We found 6 genes associated with GDM with high As exposure. The prediction of the nomograms has been proven to be effective.

Development and Validation of a Nomograph Model for Post-Operative Central Nervous System Infection after Craniocerebral Surgery.

PURPOSE: A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated. METHODS: The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set. RESULTS: A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773-0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715-0.805). The calibration curves of the training set and the validation set showed p-values of 0.439 and 0.561, respectively, with the Hosmer-Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value. CONCLUSION: The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability.

Influencing factors of prognosis in children with pulmonary atresia with intact ventricle septum after transthoracic balloon dilation of pulmonary valve and construction of a nomograph prediction model.

This study aimed to identify factors that affect the prognosis of children with pulmonary valve atresia and intact ventricular septum treated with transthoracic balloon dilation of the pulmonary valve. The study included 148 participants who were followed up for 5 years. Of these, 10 died, while 138 survived. Independent sample t-test and χ2 test were used to analyze clinical data of children in the death and survival groups. It was found that height, weight, body surface area, arterial oxygen saturation, degree of tricuspid regurgitation, pulmonary valve cross valve pressure difference, ICU length of stay, length of stay, reoperation intervention, and complications were statistically significant (P<0.05). ROC curve analysis of the measurement indicators with statistically significant differences showed that height, weight, body surface area, arterial oxygen saturation, ICU length of stay, and length of stay had AUCs ranging from 0.723 to 0.870. Logistic regression analysis revealed that the degree of tricuspid regurgitation, pulmonary valve cross valvular pressure difference, ICU length of stay, reoperation intervention, and complications were independent risk factors that affect the prognosis of patients with PA/IVS undergoing transthoracic balloon dilation of pulmonary valve. The study proposed a nomogram prediction model using R language software 4.0 "rms" package, which was validated using calibration curve and decision curve. The model had a C-index of 0.667 (95% CI: 0.643-0.786) and high degree of fit. This study provides clinicians with a prediction model to identify children with poor prognosis after treatment with transpulmonary valve balloon dilatation. .

Established the first clinical prediction model regarding the risk of hyperuricemia in adult IgA nephropathy.

OBJECTIVE: To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN). METHODS: Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver-operating characteristic curve (AUC), calibration plots, and a decision curve analysis. RESULTS: Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24 h urinary protein (24 h TP), gross hematuria and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 (95% CI 0.804-0.864). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable. CONCLUSION: Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

Pancancer analysis of SKA1 mutation and its association with the diagnosis and prognosis of human cancers.

This study aims to explore the role of SKA1 in cancer diagnosis and prognosis and to investigate the mechanism by which SKA1 affects the malignant behaviors of ovarian cancer. Herein, we analyzed the oncogenic role of SKA1 at pan-cancer level by multiple informatics databases and verified the analysis by in vitro experiments. As a result, SKA1 was upregulated across cancers and was related to poor clinical outcome and immune infiltration. Specifically, the constructed nomogram showed superior performance in predicting the prognosis of epithelial ovarian cancer patients. Furthermore, the in vitro experiments revealed that silencing SKA1 significantly inhibited the proliferation, migratory ability and enhanced the cisplatin sensitivity of ovarian cancer cells. Therefore, we explored the oncogenic and potential therapeutic role of SKA1 across cancers through multiple bioinformatic analysis and revealed that SKA1 may promote ovarian cancer progression and chemoresistance to cisplatin by activating the AKT-FOXO3a signaling pathway.

Risk analysis of bone cement leakage after percutaneous puncture vertebroplasty for osteoporotic spinal compression fractures and construction of a predictive model with column line drawings / 解剖学报

Objective To analysis risk factor and to construct a line graph prediction model for bone cement leakage after percutaneous transluminal vertebroplasty treatment in patients with osteoporotic spinal compression fractures. Methods A total of 236 patients with osteoporotic spinal compression fractures who came to our hospital from December 2019 to December 2021 were selected for the stud)', and they were divided into a leakage group (n = 58) and a non-leakage group (n = 178) according to whether bone cement leakage occurred after percutaneous transluminal vertebroplasty treatment. The clinical data were collected to analyze the factors associated with bone cement leakage; The work receiver operating characteristic

A nomograph model for prediction of central lymph node metastasis of papillary thyroid carcinoma / 预防医学

Objective@#To establish a nomograph model for prediction of cervical central lymph node metastasis (CLNM) among patients with thyroid papillary carcinoma (PTC), so as to provide the evidence for designing personalized treatment plans for PTC.@* Methods @#The data of patients that underwent thyroidectomy and were pathologically diagnosed with PTC post-surgery in the Affiliated Traditional Chinese Medicine Hospital of Xinjiang Medical University from 2018 to 2021 were collected. Patients' data captured from 2018 to 2020 and from 2021 were used as the training set and the validation set, respectively. Predictive factors were screened using a multivariable logistic regression model, and the nomograph model for prediction of CLNM risk was established. The predictive value of the model was evaluated using the receiver operating characteristic (ROC) curve and the adjusted curve.@* Results@#Totally 1 820 PTC cases were included in the training set, including 458 cases with CLNM (25.16%), and 797 cases in the validation set, including 207 cases with CLNM (25.98%). The prediction model is p=ey/(1+ey), y=0.761 + 0.525 × sex + (-0.039) ×age + 0.351 × extrathyroid invasion + 0.368 × neck lymph node enlargement + 1.021×maximum tumor diameter + (-0.009) × TT4 + (-0.001) × anti-TPOAb. The area under the ROC curve was 0.732 for the training set and 0.731 for the validation set, and Hosmer-Lemeshow test showed a good fitting effect (P=0.936, 0.722).@*Conclusion@# The nomograph model constructed in this study has a high predictive value for CLNM among patients with PTC.

A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer.

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52-66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets. Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets. Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.

Ferroptosis-Related Gene Signature Accurately Predicts Survival Outcomes in Patients With Clear-Cell Renal Cell Carcinoma.

As a type of regulated cell death induced by Ras selective lethal (RSL) compounds such as erasti, ferroptosis is characterized by iron-dependent lipid peroxide accumulation to lethal levels. At present, little is known about the role of ferroptosis-related genes in clear-cell renal cell carcinoma (ccRCC). In the present study, the expression data of ferroptosis-related genes in ccRCC were obtained from the Cancer Genome Atlas (TCGA), and COX regression analysis was performed to construct a risk model of ferroptosis prognostic signature. The GEO database was used to verify the accuracy of the model. The following findings were made: the results reveal that the prognostic signature constructed by 11 ferroptosis genes (CARS, CD44, DPP4, GCLC, HMGCR, HSPB1, NCOA4, SAT1, PHKG2, GOT1, HMOX1) was significantly related to the overall survival (OS) of ccRCC patients based on the lowest Akaike information criterion (AIC); multivariate analysis indicates that ferroptosis-related gene prognostic signature was an independent prognostic factor in ccRCC patients; the calibration curve and c-index value (0.77) demonstrate that the nomogram with the signature could predict the survival of ccRCC patients; and enrichment analysis shows that the high-risk group were enriched in humoral immunity and receptor interaction pathways. The aforementioned findings indicate that the ferroptosis-related gene signature can accurately predict the prognosis of ccRCC patients and provide valuable insights for individualized treatment.

A New RBPs-Related Signature Predicts the Prognosis of Colon Adenocarcinoma Patients.

The dysregulation of RNA binding proteins (RBPs) is closely related to tumorigenesis and development. However, the role of RBPs in Colon adenocarcinoma (COAD) is still poorly understood. We downloaded COAD's RNASeq data from the Cancer Genome Atlas (TCGA) database, screened the differently expressed RBPs in normal tissues and tumor, and constructed a protein interaction network. COAD patients were randomly divided into a training set (N = 315) and a testing set (N = 132). In the training set, univariate Cox analysis identified 12 RBPs significantly related to the prognosis of COAD. By multivariate COX analysis, we constructed a prognostic model composed of five RBPs (CELF4, LRRFIP2, NOP14, PPARGC1A, ZNF385A) based on the lowest Akaike information criterion. Each COAD patient was scored according to the model formula. Further analysis showed that compared with the low-risk group, the overall survival rate (OS) of patients in the high-risk group was significantly lower. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve was 0.722 in the training group and 0.738 in the test group, which confirmed a good prediction feature. In addition, a nomogram was constructed based on clinicopathological characteristics and risk scores. C-index and calibration curve proved the accuracy in predicting the 1-, 3-, and 5-year survival rates of COAD patients. In short, we constructed a superior prognostic and diagnostic signature composed of five RBPs, which indicates new possibilities for individualized treatment of COAD patients.

Low Expression of Keratin17 is Related to Poor Prognosis in Bladder Cancer.

OBJECTIVE: To investigate the association between KRT17 and the prognosis in bladder cancer patients. METHODS: The clinical data of 101 patients with bladder cancer from May 2013 to May 2015 were retrospectively analyzed. At the same time, the expression of KRT17 and its correlation with clinicopathological factors were examined by immunohistochemistry. We search the prognostic value of KRT17 in bladder cancer from the cancer genome map (TCGA) online database. To explore the possible cellular mechanism, gene set enrichment analysis (GSEA) was used. The patients were divided into two groups: high expression of KRT17 and low expression of KRT17. The patients were followed up for 5 years to observe the survival. Kaplan-Meier method and Log rank test were used for univariate survival analysis, and Cox regression analysis was used for multivariate analysis. Finally, a nomogram was constructed on this basis for internal verification. RESULTS: Among the 101 patients, 46 (45.5%) were in the KRT17 low expression group and 55 (54.5%) in the high KRT17 expression group. After 5 years of follow-up, 79 patients survived with a survival rate of 78.2% and 22 patients died with a mortality rate of 21.8%. Kaplan-Meier survival analysis showed that OS and PFS of patients with high expression of KRT17 were significantly higher than those of patients with low expression of KRT17 (p<0.001, p=0.005). Cox multivariate analysis showed that KRT17 expression was an independent risk factor for tumor progression (p=0.019). And tumor size, vascular tumor thrombus, and T stage also affected tumor progression (p<0.05). In the internal validation, the c-index of nomogram was 0.898 (95% CI: 0.854-0.941). CONCLUSION: The decreased expression of KRT17 is associated with poor prognosis in patients with bladder cancer. KRT17 can be used as a novel predictive biomarker to provide a new therapeutic target for bladder cancer patients.

Effect of Rho GTPase activating protein 9 combined with preoperative ratio of platelet distribution width to platelet count on prognosis of patients with serous ovarian cancer.

BACKGROUND: This study aimed to investigate the relationship between Rho GTPase activating protein 9 (ARHGAP9) combined with preoperative ratio of platelet distribution width to platelet count (PDW/PLT) and patients prognosis with serous ovarian cancer. METHODS: The clinical data of 80 patients with serous ovarian cancer treated in Jiangsu Cancer Hospital from May 2011 to May 2016 were analyzed retrospectively. We verified ARHGAP9 expression in The Cancer Genome Atlas (TCGA) database, then detected messenger RNA (mRNA) expression encoding ARHGAP9 in ovarian cancer tissue samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR). These patients were divided into an ARHGAP9 low-expression group and an ARHGAP9 high-expression group. The optimal critical value of PDW/PLT was determined by receiver operating characteristic (ROC) curve. The patients were divided into low PDW/PLT group and high PDW/PLT group. Kaplan-Meier method and log-rank test were used for univariate survival analysis, Cox regression method was used for multivariate analysis, and then a nomogram was constructed for internal verification. RESULTS: The ARHGAP9 protein was highly expressed both in TCGA serous ovarian cancer database and the serous ovarian cancer tumor tissues. There were significant differences in menstrual status, the International Federation of Gynecology and Obstetrics (FIGO) stage and grade between the ARHGAP9 low expression group and ARHGAP9 high expression group (all P<0.05). There were significant differences in FIGO stage, lymph node metastasis, and ascites between the low PDW/PLT group and high PDW/PLT group (all P<0.05). Finally, 80 patients were included, with a mortality rate of 45.0% and a survival rate of 55.0%; the median progression-free survival (PFS) was 19 months, and the median overall survival (OS) was 62.5 months. Cox multivariate analysis showed that PDW/PLT and ARHGAP9 were independent risk factors for tumor progression (P=0.026 and P=0.028, respectively). In the internal validation, the C-index of the nomogram was 0.6518 [95% confidence interval (CI): 0.5685 to 0.7352], and the prediction model had certain accuracy. CONCLUSIONS: ARHGAP9 and PDW/PLT Decrease can significantly prolong OS and PFS in serous ovarian cancer patients. Therefore, ARHGAP9 can be used as a new predictive biomarker and may be related to the immune infiltration of serous ovarian cancer.

Time since death nomographs implementing the nomogram, body weight adjusted correction factors, metric and imperial measurements.

The concept of nomography was developed around 1880 as a means to compute formulas graphically. Regular use has decreased over time in most fields, mainly owing to progress in electronic computation devices. In forensic pathology, nomography is still used in the so-called "nomogram method" for the estimation of time since death. It is the graphical representation of the formula by Marshall and Hoare with the parameters of Henssge. Here, two nomograms exist (for ambient temperatures below and above 23 °C, no imperial measurements). Rounding for body weight input and result reading introduces errors. In addition, correction factors, applied to body weight, allow to adapt for certain conditions on the crime scene and are essential to the method. They are not directly integrated into the nomograms but must be applied in advance. A formula, scaling correction factors for different body weights, was later added by Henssge, along with a simplified table for case work. In this publication, we present newly designed time since death nomographs as representations of Henssge's parameters with the addition of both metric and imperial measurements, integration of weight adjusted scaling of correction factors, and a geometrically consistent framework for body weight and result reading, which eliminates some rounding steps and reduces the overall rounding-related estimation errors.