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Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis.

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OBJECTIVE: The HeartMate 3 survival risk score was recently validated in the Multicenter study Of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3 to predict patient-specific survival in HeartMate 3 left ventricular assist device candidates. The HeartMate 3 survival risk score stratifies individuals into tertiles according to survival probability. METHODS: We performed a single-center retrospective review of all HeartMate 3 left ventricular assist device recipients between September 2017 and August 2022. Baseline characteristics were collected from the electronic medical records. HeartMate 3 survival risk scores were calculated for all eligible patients. One- and 2-year Kaplan-Meier survival analyses were conducted. A univariate and multivariable Cox regression model was used to identify predictors. RESULTS: A total of 181 patients were included in this final analysis. The median age was 62 years, 83% were male, and 26% were Interagency Registry for Mechanically Assisted Circulatory Support Profile 1. The mean HeartMate 3 survival risk score for the entire cohort was 2.66 ± 0.66. Two-year survivals in the high, average, and low survival groups were 93.5% ± 3.2%, 81.6% ± 7.4%, and 82.0% ± 6.6%, respectively. As a continuous variable, the unadjusted HeartMate 3 survival risk score was a significant predictor of mortality (hazard ratio, 2.20; 95% CI, 1.08-4.45; P = .029). The areas under the curve were 0.70 and 0.66 at 1 and 2 years, respectively. We were unable to demonstrate the discriminatory ability of the HeartMate 3 survival risk score using the original stratification, but we found significantly increased survival in the high survival group using a binary cutoff (hazard ratio, 4.8; 95% CI, 1.01-20.9; P = .038). CONCLUSIONS: The unadjusted HeartMate 3 survival risk score was associated with postimplant survival in patients outside of the Multicenter study Of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3 but did not remain an independent predictor after adjusting for ischemic etiology and severe diabetes. The HeartMate 3 survival risk score was able to identify patients at high survival using a binary cutoff, but we were unable to demonstrate its discriminatory ability among the previously published risk tertiles.

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In a context where pessimistic survival perceptions have been widespread as a result of the HIV/AIDS epidemic (Fig. 1 A), we study vaccine uptake and other health behaviors during the recent COVID-19 pandemic. Leveraging a longitudinal cohort study in rural Malawi that has been followed for up to 25 y, we document that a 2017 mortality risk information intervention designed to reduce pessimistic mortality perceptions (Fig. 1 B) resulted in improved health behavior, including COVID-19 vaccine uptake (Fig. 1 C). We also report indirect effects for siblings and household members. This was likely the result of a reinforcing process where the intervention triggered engagement with the healthcare system and stronger beliefs in the efficacy of modern biomedical treatments, which led to the adoption of health risk reduction behavior, including vaccine uptake. Our findings suggest that health information interventions focused on survival perceptions can be useful in promoting health behavior and participation in the formal healthcare system, even during health crises-such as the COVID-19 pandemic-that are unanticipated at the time of the intervention. We also note the importance of the intervention design, where establishing rapport, tailoring the content to the local context, and spending time with respondents to convey the information contributed to the salience of the message.

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Background: Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting 'less-than-median-survival risk' in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. Methods: To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. Results: The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. Conclusion: Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

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Lung cancer is rare in young people, but the incidence and mortality are on the rise. We retrospectively analyzed the data of young patients aged ≤45 years diagnosed as lung cancer in our hospital from 2014 to 2021. The purpose was to explore the clinicopathological characteristics of young patients, and the risk factors affecting overall survival (OS) time. The results showed that the young patients were mainly female, had no smoking history, asymptomatic at initial diagnosis, with a high proportion of adenocarcinoma and stage I-II. We divided all patients into two groups according to age and found that the proportion of stage I-II in 18-35 years group was significantly higher than that in 36-45 years group (P = 0.021). The main manifestation of tumor was ground glass opacity (GGO) in 18-35 years group, while most showed non-GGO in 36-45 years group (P = 0.003). The proportion of minimally invasive adenocarcinoma was higher in 18-35 years group, while the invasive adenocarcinoma was higher in 36-45 years group (P = 0.004). Univariate analysis showed that asymptomatic, stage I-II, surgery, women, with few or no metastatic organs had longer OS. Multivariate analysis showed that the independent factors affecting the OS of young patients were tumor stage and more metastatic organs.

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Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

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PURPOSE: To investigate the predictive power of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) in prognosis and survival risk of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy. MATERIALS AND METHODS: Forty-five patients with laryngeal or hypopharyngeal squamous cell carcinoma were retrospectively enrolled. All patients had undergone pretreatment IVIM examination, subsequently, mean apparent diffusion coefficient (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin) and ADCrange (ADCmax - ADCmean) by mono-exponential model, true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f) by bi-exponential model, distributed diffusion coefficient (DDC), and diffusion heterogeneity index (α) by stretched exponential model were measured. Survival data were collected for 5 years. RESULTS: Thirty-one cases were in the treatment failure group and fourteen cases were in the local control group. Significantly lower ADCmean, ADCmax, ADCmin, D, f, and higher D* values were observed in the treatment failure group than in the local control group (p < 0.05). D* had the greatest AUC of 0.802, with sensitivity and specificity of 77.4 and 85.7% when D* was 38.85 × 10-3 mm2/s. Kaplan-Meier survival analysis showed that the curves of N stage, ADCmean, ADCmax, ADCmin, D, D*, f, DDC, and α values were significant. Multivariate Cox regression analysis showed ADCmean and D* were independently correlated with progression-free survival (PFS) (hazard ratio [HR] = 0.125, p = 0.001; HR = 1.008, p = 0.002, respectively). CONCLUSION: The pretreatment parameters of mono-exponential and bi-exponential models were significantly correlated with prognosis of LHSCC, ADCmean and D* values were independent factors for survival risk prediction.

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INTRODUCTION: As life expectancy is currently growing, more elderly and fragile patients need colorectal resection for cancer. We sought to assess the link between enhanced rehabilitation after surgery (ERAS), risk factors and overall survival at 3 years, in patients aged 65 and over. METHODS: Between 2005 and 2017, all patients undergoing colorectal resection for cancer were included. Overall survival at 3 years was compared for patients treated in following ERAS guidelines compared to conventional treatment (pre-ERAS). RESULTS: 661 patients were included (ERAS, n = 325; pre-ERAS, n = 336). The 3-year overall survival rate was significantly better regardless of age for ERAS vs pre-ERAS patients (73.1% vs 64.4%; p = 0.016). With overall survival rates of 83.2% vs 73.8%, 65.4% vs 62.8% and 59.6% vs 40% for the age bands 65-74, 75-84 and ≥ 85 years. The analysis of survival at 3 years by a multivariate Cox model identified ERAS as a protective factor with a reduction in the risk of death of 30% (HR = 0.70 [0.50-0.94], p = 0017) independently of other identified risk factors: age bands, ASA score > 2, smoking, atrial fibrillation and abdominal surgery. This result is confirmed by an analysis of the propensity score (HR = 0.67 [0.47-0.97], p = 0.032). CONCLUSIONS: Our study shows that ERAS is associated with better 3-year survival in patients undergoing colorectal resection for cancer, independent of risk factors. The practice of ERAS is effective and should be offered to patients aged 65 and over.

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Background: Prediction of prognosis for patients with esophageal cancer(EC) is beneficial for their postoperative clinical decision-making. This study's goal was to create a dependable machine learning (ML) model for predicting the prognosis of patients with EC after surgery. Methods: The files of patients with esophageal squamous cell carcinoma (ESCC) of the thoracic segment from China who received radical surgery for EC were analyzed. The data were separated into training and test sets, and prognostic risk variables were identified in the training set using univariate and multifactor COX regression. Based on the screened features, training and validation of five ML models were carried out through nested cross-validation (nCV). The performance of each model was evaluated using Area under the curve (AUC), accuracy(ACC), and F1-Score, and the optimum model was chosen as the final model for risk stratification and survival analysis in order to build a valid model for predicting the prognosis of patients with EC after surgery. Results: This study enrolled 810 patients with thoracic ESCC. 6 variables were ultimately included for modeling. Five ML models were trained and validated. The XGBoost model was selected as the optimum for final modeling. The XGBoost model was trained, optimized, and tested (AUC = 0.855; 95% CI, 0.808-0.902). Patients were separated into three risk groups. Statistically significant differences (p < 0.001) were found among all three groups for both the training and test sets. Conclusions: A ML model that was highly practical and reliable for predicting the prognosis of patients with EC after surgery was established, and an application to facilitate clinical utility was developed.

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Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive system with poor prognosis and high mortality. It is of great significance to predict the prognosis risk of patients with cancer by using medical pathology information. To take full advantage of the clinic pathological information of ESCC patients and improve the accuracy of postoperative survival risk prediction, this paper proposes an ESCC survival risk prediction model based on Relief feature selection and convolutional neural network (CNN). Firstly, statistical analysis methods and relief feature selection algorithm are used to extract the important risk factors related to the survival risk of patients. Then, One-dimensional convolutional neural network (1D-CNN) is used to establish the survival risk prediction model of patients with esophageal cancer. Finally, the data of patients with esophageal cancer provided by the First Affiliated Hospital of Zhengzhou University is used to assess the performance of the model. The results show that the model proposed in this paper has a high accuracy rate, which can effectively predict the postoperative survival risk of the patient through the clinical phenotypic index of the patient.

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BACKGROUND: Surveillance of severe injury incidence and prevalence using ICD-based injury severity scores (ICISS) requires valid, locally applicable diagnosis-specific survival probabilities (DSPs). This study aims to derive and validate ICISS in Victoria, Australia, and compare various ICISS methodologies in terms of accuracy and calculated severe injury prevalence. METHODS: This study used injury admissions (ICD-10-AM coded) from the Victorian Admitted Episodes Database (VAED) linked with death data (Cause of Death - Unit Record Files: CODURF). Using design data (July 2008 - June 2014; n = 720,759), various ICISS scales were derived, based on (i) in-hospital and (ii) three-month mortality. These scales were applied to testing data (July 2014 - December 2016; n = 334,363). Logistic regression modelling was used to determine model discrimination and calibration. RESULTS: In the design data, there were 6,337(0.9%) hospital deaths and 17,514(2.4%) three-months deaths; in the testing data, there were 2,700(0.8%) hospital deaths and 8,425(2.5%) three-month deaths. Newly developed ICISS scales had acceptable to outstanding discrimination, with Area Under the Curve ranging from 0.758 to 0.910. Age-specific ICISS scales were superior to general ICISS scales in model discrimination but inferior in model calibration. Calculated severe injury (ICISS ≤0.941) prevalence in the testing data ranged from 2% to 24%, depending on which mortality outcomes were used to calculate DRGs. CONCLUSIONS: This study provides local, validated ICISS scores that can be used in Victoria. It is recommended that age group stratified ICISS based on the worst-injury method is used. From the comparison of various ICISS scores, reflecting the range of ICISS permutations that are currently in use, care should be taken to compare ICISS methodology before comparing severe injury prevalence per population, injury cause, and time trends.

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@#Objective    To explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. Methods    The clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The  performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. Results    The 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). Conclusion    Machine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.

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Objective:To investigate the prognosis value of baseline contrast-enhanced CT in predicting progression-free survival (PFS) and overall survival (OS) for clinically diagnosed as metastatic far-advanced gastric cancer patients.Methods:Between January 2019 and May 2020, 85 pathologically confirmed gastric adenocarcinoma patients with peritoneal or hepatic metastasis at Shanghai Ruijin Hospital with complete preoperative clinical, image and follow-up data were enrolled in this retrospective study. Clinical factors included performance status (PS) score, tumor location, and tumor serological indicators. Imaging factors included the longest diameter and maximum cross-sectional area of the tumor, CT value, enhancement uniformity, CT extramural venous invasion (ctEMVI), the largest short diameter of the metastatic lymph nodes, confluent lymph nodes, lymph nodes necrosis, fused bulk lymph nodes, the maximum cross-sectional area and CT value of the liver metastases, peritoneal metastasis score, longest diameter of nodules with peritoneal metastasis. Kaplan-Meier survival curve and log-rank test were used to analyze the prognostic differences between groups. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors for PFS and OS.Results:There were significant differences in the maximum cross-sectional area of the tumor, non-contrast CT value, delayed-phase CT value, and delayed-phase CT ratio value between the high- and low-risk groups in PFS ( P<0.05). There were significant differences between the high- and low-risk groups with the maximum cross-sectional area of the tumor in PFS and OS ( P<0.05). In the univariate analysis, the maximum cross-sectional area of tumor, plain-scan CT value, delayed-phase CT value, delayed-phase CT ratio value and the largest short diameter of metastatic lymph nodes were risk factors for PFS ( P<0.05). PS score, CA724, maximum cross-sectional area of the tumor, maximum cross-sectional area of liver metastases, and peritoneal metastasis score were shown as risk factors for OS ( P<0.05). In the multivariate analysis, the maximum cross-sectional area of the tumor and non-contrast CT value were independent risk factors for PFS (HR=0.41, 2.50, P<0.05, 0.006). PS score, CA724 and peritoneal metastasis score were independent risk factors for OS (HR=46.78, 6.26, 92.92, P=0.026, 0.009, 0.007). Conclusions:Tumor size, CT attenuations, and peritoneal metastasis score on baseline CT can be used as independent risk factors for survival in patients with far-advanced gastric cancer with peritoneal or hepatic metastasis. Baseline CT is potentially useful in prediction of the survival status for patients with metastatic far-advanced gastric cancer.

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BACKGROUND: Trauma-induced coagulopathy (TIC) is a disorder that occurs in one-third of severely injured trauma patients, manifesting as increased bleeding and a 4X risk of mortality. Understanding the mechanisms driving TIC, clinical risk factors are essential to mitigating this coagulopathic bleeding and is therefore essential for saving lives. In this retrospective, single hospital study of 891 trauma patients, we investigate and quantify how two prominently described phenotypes of TIC, consumptive coagulopathy and hyperfibrinolysis, affect survival odds in the first 25 h, when deaths from TIC are most prevalent. METHODS: We employ a joint survival model to estimate the longitudinal trajectories of the protein Factor II (% activity) and the log of the protein fragment D-Dimer ([Formula: see text]g/ml), representative biomarkers of consumptive coagulopathy and hyperfibrinolysis respectively, and tie them together with patient outcomes. Joint models have recently gained popularity in medical studies due to the necessity to simultaneously track continuously measured biomarkers as a disease evolves, as well as to associate them with patient outcomes. In this work, we estimate and analyze our joint model using Bayesian methods to obtain uncertainties and distributions over associations and trajectories. RESULTS: We find that a unit increase in log D-Dimer increases the risk of mortality by 2.22 [1.57, 3.28] fold while a unit increase in Factor II only marginally decreases the risk of mortality by 0.94 [0.91,0.96] fold. This suggests that, while managing consumptive coagulopathy and hyperfibrinolysis both seem to affect survival odds, the effect of hyperfibrinolysis is much greater and more sensitive. Furthermore, we find that the longitudinal trajectories, controlling for many fixed covariates, trend differently for different patients. Thus, a more personalized approach is necessary when considering treatment and risk prediction under these phenotypes. CONCLUSION: This study reinforces the finding that hyperfibrinolysis is linked with poor patient outcomes regardless of factor consumption levels. Furthermore, it quantifies the degree to which measured D-Dimer levels correlate with increased risk. The single hospital, retrospective nature can be understood to specify the results to this particular hospital's patients and protocol in treating trauma patients. Expanding to a multi-hospital setting would result in better estimates about the underlying nature of consumptive coagulopathy and hyperfibrinolysis with survival, regardless of protocol. Individual trajectories obtained with these estimates can be used to provide personalized dynamic risk prediction when making decisions regarding management of blood factors.

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The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941-0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16-18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

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OBJECTIVES: The aim of this study was to study the impact of a decision-making protocol for shunt type in the Norwood procedure for hypoplastic left heart syndrome. Our cohort extends from 2004 to 2016. In era 1 (pre-2008), there was no policy for the choice of Norwood shunt. In era 2 (post-2008), a standard protocol was implemented. The right ventricle (RV)-to-pulmonary artery conduit was utilized for low-birth weight patients (<2.5 kg). The right modified Blalock-Taussig Shunt (RBTS) was constructed for normal birth weight patients. METHODS: The records of 133 consecutive operative patients with hypoplastic left heart syndrome anatomy between 2004 and 2016 were retrospectively reviewed. Survival risk factors were analysed using the Cox proportional hazards risk model. RESULTS: The Norwood procedure was performed at a mean age of 2.9 ± 1.9 days. Bidirectional cavopulmonary shunt was performed at a median age of 99 days (interquartile range 91-107). In era 1, 38.6% (22/57) of patients received the RBTS and 61.4% (35/57) of patients received the RV-to-pulmonary artery conduit. In era 2, 86.8% (66/76) of patients received the RBTS and 13.2% (10/76) of patients received the RV-to-pulmonary artery conduit. The actuarial survival to Fontan was 72.2% (96/133). Era 1 patients were more likely to die within the 1st year (hazard ratio = 2.310, P = 0.025). CONCLUSIONS: The shunt protocol may improve outcomes in high-risk patients, and we have demonstrated the reliability of the RBTS in low-risk patients. The short- and mid-term outcomes of our Norwood population justify the continued efforts to improve surgical and perioperative management.

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OBJECTIVE: To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility. METHODS: Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome. RESULTS: In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups. CONCLUSIONS: We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.

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Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and the third of cancer mortality worldwide. Although the study of HCC has made great progress, the molecular mechanism and signal pathways of HCC are not yet clear. Therefore, it is necessary to investigate the early diagnosis and prognosis biomarkers for HCC. The aim of this study is to screen the relevant genes and study the association of gene expression with the survival status of HCC patients using bioinformatics approaches, in the hope of establishing marker genes for diagnosis and prognosis of HCC. The gene expression data and corresponding clinical information of HCC samples were downloaded from the The Cancer Genome Atlas database. We performed to study the relationship between gene expression and prognosis of HCC and screen significantly relevant genes associated with prognosis of HCC by analyzing survival and function enrichment of genes. In this study, we collected 421 samples with gene expression data, including 371 tumor samples and 50 normal samples. By using single factor Cox regression analysis, we screened 1,197 genes significantly associated with survival time in the modeling data containing 117 samples and also searched six genes as the best markers to predict living status of HCC patients. Besides, we established score system of survival risk of HCC. Our study recognized six genes (PGBD3, PGM5P3-AS1, RNF5, UTP11, BAG6, and KCND2) to be significantly associated with diagnosis and prognosis of HCC, providing novel targets for studying potential mechanism about the progression of HCC.

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We examined associations between adverse childhood experiences (ACEs) and shorter telomere length (TL) in 83 older women, including 42 women with less than secondary education and 41 with secondary or more education in a city of Northeast Brazil, a region with substantial socioeconomic inequalities. The low education sample was selected from a representative survey at local neighborhood health centers, while the high education group consisted of a convenience sample recruited by advertising in community centers and centers affiliated with the local university. Relative leukocyte TL was measured by quantitative polymerase chain reaction from blood samples. ACEs were self-reported. Spline linear regression was fitted to assess the strength of the associations between ACEs and TL. Among women with low education, median TL was 1.02 compared with 0.64 in the high education group (p = 0.0001). Natural log-transformed T/S ratio as the dependent variable was used in analysis. Women with low education had been exposed to more ACEs, and among them those experiencing two or more ACEs had longer TL than women exposed to ≤1 ACEs (p = 0.03); among women with high education, this difference was not significant (p = 0.49). In analyses adjusted by age, education, and parental abuse of alcohol, the linear trend of higher TL with increasing ACEs was confirmed (p = 0.02), and the mean difference in TL between groups remained significant (p = 0.002). The unexpected positive relationship between low education and ACEs with TL suggests that older adults who have survived harsh conditions prevailing in Northeast Brazil have the longest TL of their birth cohort.

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We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub.