Burden of ischemic stroke attributable to a high red meat diet in China, 1990–2019: analysis based on the 2019 Global Burden of Disease Study

BackgroundThe burden of ischemic stroke (IS) linked to high consumption of red meat is on the rise. This study aimed to analyze the mortality and disability-adjusted life years (DALYs) trends for IS attributed to high red meat intake in China between 1990 and 2019 and to compare these trends with global trends.MethodsThis study extracted data on IS attributed to diets high in red meat in China from 1990 to 2019 from the Global Burden of Disease Study (GBD) database. Key measures, including mortality, DALYs, age-standardized mortality rates (ASMR), and age-standardized DALYs rates (ASDR), were used to estimate the disease burden. The estimated annual percentage change and joinpoint regression models were employed to assess the trends over time. An age-period-cohort analysis was used to assess the contribution of a diet high in red meat to the age, period, and cohort effects of IS ASMR and ASDR.ResultsBetween 1990 and 2019, deaths and DALYs from IS attributed to a diet high in red meat in China, along with corresponding age-standardized rates, significantly increased. The overall estimated annual percentage change for the total population and across sex categories ranged from 1.01 to 2.08. The average annual percentage changes for overall ASDR and ASMR were 1.4 and 1.33, respectively, with male ASDR and ASMR average annual percentage changes at 1.69 and 1.69, respectively. Contrastingly, female ASDR and ASMR average annual percentage changes were 1.07 and 0.87, respectively. Except for a few periods of significant decrease in females, all other periods indicated a significant increase or nonsignificant changes. Incidence of IS linked to a diet high in red meat rose sharply with age, displaying increasing period and cohort effects in ASDR. Female ASMR period and cohort effect ratios initially increased and then decreased, whereas the male ratio showed an upward trend.ConclusionThis study comprehensively analyzed epidemiological characteristics that indicated a marked increase in mortality and DALYs from IS attributable to high red meat consumption, contrasting with a global downtrend. This increase was more pronounced in males than females. This research provides valuable insights for enhancing IS prevention in China

A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.

ObjectiveSuccessful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC.MethodsData on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME).ResultsA total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients.ConclusionsAfter validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Abstract Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004–2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell’s concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC

Clinical and pathological characteristics of the study sample of patients with osteosarcoma.

Clinical and pathological characteristics of the study sample of patients with osteosarcoma.</p

Patient selection.

ObjectiveSuccessful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC.MethodsData on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME).ResultsA total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients.ConclusionsAfter validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.</div

Interface display of the web-based calculator.

ObjectiveSuccessful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC.MethodsData on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME).ResultsA total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients.ConclusionsAfter validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.</div

Fig 3 -

The time-dependent ROC curves for (A) the training cohorts and (B) the validation cohorts.</p

Performance of the DeepSurv and cox proportional hazard (CPH) models in the validation cohort.

(A, B) Prediction errors in the DeepSurv model by root-mean-square error (RMSE) and mean absolute error (MAE), respectively. (C, D) Prediction errors in the CPH model by RMSE and MAE, respectively.</p

LRP2 and DOCK8 Are Potential Antigens for mRNA Vaccine Development in Immunologically ‘Cold’ KIRC Tumours

The administration of mRNA-based tumour vaccines is considered a promising strategy in tumour immunotherapy, although its application against kidney renal clear cell carcinoma (KIRC) is still at its infancy stage. The purpose of this study was to identify potential antigens and to further select suitable patients for vaccination. Gene expression data and clinical information were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. GEPIA2 was used to evaluate the prognostic value of selected antigens. The relationship of antigens presenting cell infiltration with antigen expression was evaluated by TIMER, and immune subtypes were determined using unsupervised cluster analysis. Tumour antigens LRP2 and DOCK8, which are associated with prognosis and tumour-infiltrating antigen-presenting cells, were identified in KIRC. A total of six immune subtypes were identified, and patients with immune subtype 1–4 (IS1–4) tumours had an immune ‘cold’ phenotype, a higher tumour mutation burden, and poor survival. Moreover, these immune subtypes showed significant differences in the expression of immune checkpoint and immunogenic cell death modulators. Finally, the immune landscape of KIRC revealed the immune-related cell components in individual patients. This study suggests that LRP2 and DOCK8 are potential KIRC antigens in the development of mRNA vaccines, and patients with immune subtypes IS1–4 are suitable for vaccination