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Abstract Background Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. Methods We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. Results The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. Conclusions This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Abstract Background High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. Results We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. Conclusions The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.

Abstract Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Calcium influx into neurons triggers neuronal death during cerebral ischemia/reperfusion injury. Various calcium channels are involved in cerebral ischemia/reperfusion injury. Cav3.2 channel is a main subtype of T-type calcium channels. T-type calcium channel blockers, such as pimozide and mibefradil, have been shown to prevent cerebral ischemia/reperfusion injury-induced brain injury. However, the role of Cav3.2 channels in cerebral ischemia/reperfusion injury remains unclear. Here, in vitro and in vivo models of cerebral ischemia/reperfusion injury were established using middle cerebral artery occlusion in mice and high glucose hypoxia/reoxygenation exposure in primary hippocampal neurons. The results showed that Cav3.2 expression was significantly upregulated in injured hippocampal tissue and primary hippocampal neurons. We further established a Cav3.2 gene-knockout mouse model of cerebral ischemia/reperfusion injury. Cav3.2 knockout markedly reduced infarct volume and brain water content, and alleviated neurological dysfunction after cerebral ischemia/reperfusion injury. Additionally, Cav3.2 knockout attenuated cerebral ischemia/reperfusion injury-induced oxidative stress, inflammatory response, and neuronal apoptosis. In the hippocampus of Cav3.2-knockout mice, calcineurin overexpression offset the beneficial effect of Cav3.2 knockout after cerebral ischemia/reperfusion injury. These findings suggest that the neuroprotective function of Cav3.2 knockout is mediated by calcineurin/nuclear factor of activated T cells 3 signaling. Findings from this study suggest that Cav3.2 could be a promising target for treatment of cerebral ischemia/reperfusion injury.

Abstract To investigate the gender-specific relationship between total bilirubin (TBIL) and fundus arteriosclerosis in the general population, and to explore whether there is a dose–response relationship between them. In a retrospective cohort study, 27,477 participants were enrolled from 2006 to 2019. The TBIL was divided into four groups according to the quartile. The Cox proportional hazards model was used to estimate the HRs with 95% CIs of different TBIL level and fundus arteriosclerosis in men and women. The dose–response relationship between TBIL and fundus arteriosclerosis was estimated using restricted cubic splines method. In males, after adjusting for potential confounders, the Q2 to Q4 level of TBIL were significantly associated with the risk of fundus arteriosclerosis. The HRs with 95% CIs were 1.217 (1.095–1.354), 1.255 (1.128–1.396) and 1.396 (1.254–1.555), respectively. For females, TBIL level was not associated with the incidence of fundus arteriosclerosis. In addition, a linear relationship between TBIL and fundus arteriosclerosis in both genders (P

ObjectiveThe population is aging exponentially and the resulting frailty is becoming increasingly evident. We aimed to explore the association between altitude and frailty, and to identify associated factors for frailty.MethodsThis is a community-based cross-sectional survey. 1,298 participants aged ≥60 years from three different altitudes were included in the study. To quantify frailty, we constructed a frailty index (FI) and a frailty score (FS). The FI was divided into non-frailty, prefrailty, and frailty. The Odds Ratios and confidence intervals (ORs, 95%CIs) were used to evaluate the association between altitude and FI and FS in multivariate ordinal logistic regression and linear regression.ResultsThere were 560 (53.1%) participants in the prefrailty and 488 (37.6%) in the frailty group. The FS increased with higher altitude (P for trend

Abstract Introduction As a disorder of the brain in adults and children, traumatic brain injury (TBI) is considered the major cause of mortality and morbidity. As a serious complication of TBI, post-traumatic hydrocephalus (PTH) is commonly identified and significantly associated with neurocognitive impairment, motor dysfunction, and growth impairment. The long-term functional outcomes after shunt dependence are totally not clear. Methods This study included 6279 patients between 2012 and 2022. To identify the unfavorable functional outcomes and the PTH-related factors, we carried out univariable logistic regression analyses. To identify the occurrence time of PTH, we conducted the log-rank test and Kaplan–Meier analysis. Results Mean patient age was 51.03 ± 22.09 years. Of the 6279 patients with TBI, 327 developed PTH (5.2%). Several PTH development-associated factors, such as intracerebral hematoma, diabetes, longer initial hospital stay, craniotomy, low GCS (Glasgow Coma Scale), EVD (external ventricular drain), and DC (decompressive craniectomy) (p 80 years, repeated operations, hypertension, EVD, tracheotomy, and epilepsy (p

Abstract Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary $${R}^{2}$$ R 2 , coefficient of determination $${R}^{2}$$ R 2 , mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA $${(0, 1, 1) (0, 1, 0)}_{12}$$ ( 0 , 1 , 1 ) ( 0 , 1 , 0 ) 12 model with the covariates of $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

BackgroundThe impact of serum uric acid (SUA) trajectories on the development of retinal arteriosclerosis is uncertain. The purpose of this study was to identify adult SUA trajectories by sex and determine their association with risk of retinal arteriosclerosis.MethodsIn this longitudinal study, 4,324 participants who were aged between 18 and 60 years without retinal arteriosclerosis at or before baseline (from January 1, 2010, through December 31, 2010) were included. Group-based trajectory modeling was used to identify SUA trajectories during the exposure period (from January 1, 2006, through December 31, 2010). Cox proportional-hazards models were applied to evaluate the associations between SUA trajectories and the risk of incident retinal arteriosclerosis during the outcome period (from January 1, 2011, through December 31, 2019).Results4 distinct SUA trajectories were identified in both women and men: low, moderate, moderate-high, and high. During a median follow-up of 9.54 years (IQR 9.53–9.56), 97 women and 295 men had developed retinal arteriosclerosis. In the fully adjusted model, a significant association between the moderate-high SUA trajectory group and incidence of retinal arteriosclerosis was observed only in men (HR: 1.76, 95% CI: 1.17–2.65) compared with the low trajectory group, but not in women (HR: 0.77, 95% CI: 0.39–1.52). Also, the high SUA trajectory group had the highest risk with an adjusted HR of 1.81 (95% CI, 1.04–3.17) in men. However, they did not exhibit a substantially increased risk in women.ConclusionHigher SUA trajectory groups were significantly associated with an increased risk of incident retinal arteriosclerosis in men but not in women.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model’s original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.

This study described the knowledge, attitude, practice regarding antimicrobial resistance (AMR) among medical students between 2017 and 2022 in East China. A questionnaire-based survey was conducted with a total of 1,066 respondents. We highlighted that the undergraduates had a significant increase in the knowledge of antimicrobial resistance during the 5 years from 2017 to 2022 (p < 0.001). The majority of the assertions about the AMR were correctly identified by respondents. However, gaps were still observed in the issues of antimicrobial targets and bacterial transmission. In addition, overconfident attitudes and inappropriate behaviors of antimicrobial overuse and misuse were observed in the respondents. A number of 30.2% to 45.2% of the respondents asserted that there is no risk of AMR as long as the antimicrobials are taken correctly, and a proportion of the students (25.3% in 2022; 69.3% in 2017, p < 0.001) declared to buy antimicrobials from friends or family members to treat the same illness. Finally, spearman correlation coefficient was enrolled to compare the correlation of the student's KAP. Results showed that the students' knowledge of antimicrobials had a correlation with attitude (p = 0.0126) and practice (p < 0.001), suggesting that public education on knowledge could influence the behaviors among the medical students. Taken all together, our findings show a need to strengthen the medical students' cogitation on antimicrobial attitude and practice of appropriate usage as an essential strategy to reduce intractable public health problems. Additional curriculum reforms will be needed to add more specific AMR-related lectures to raise awareness amongst medical students in China.

PurposeWe developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.Methods and MaterialsAnalyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.ResultsIn the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.ConclusionsThe 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

The interleukins (ILs) are a pluripotent cytokine family that have been reported to regulate ischemic stroke and cerebral ischemia/reperfusion (I/R) injury. IL-22 is a member of the IL-10 superfamily and plays important roles in tissue injury and repair. However, the effects of IL-22 on ischemic stroke and cerebral I/R injury remain unclear. In the current study, we provided direct evidence that IL-22 treatment decreased infarct size, neurological deficits, and brain water content in mice subjected to cerebral I/R injury. IL-22 treatment remarkably reduced the expression of inflammatory cytokines, including IL-1β, monocyte chemotactic protein- (MCP-) 1, and tumor necrosis factor- (TNF-) α, both in serum and the ischemic cerebral cortex. In addition, IL-22 treatment also decreased oxidative stress and neuronal apoptosis in mice after cerebral I/R injury. Moreover, IL-22 treatment significantly increased Janus tyrosine kinase (JAK) 2 and signal transducer and activator of transcription (STAT) 3 phosphorylation levels in mice and PC12 cells, and STAT3 knockdown abolished the IL-22-mediated neuroprotective function. These findings suggest that IL-22 might be exploited as a potential therapeutic agent for ischemic stroke and cerebral I/R injury.

Background and Purpose. Abdominal aortic aneurysm (AAA) is a chronic inflammatory disorder and the important causes of death among men over the age of 65 years. Interleukin-12p35 (IL12p35) is an inflammatory cytokine that participates in a variety of inflammatory diseases. However, the role of IL12p35 in the formation and development of AAA is still unknown. Experimental Approach. Male apolipoprotein E-deficient (Apoe-/-) mice were generated and infused with 1.44 mg/kg angiotensin II (Ang II) per day. We found that IL12p35 expression was noticeably increased in the murine AAA aorta and isolated aortic smooth muscle cells (SMCs) after Ang II stimulation. IL12p35 silencing promoted Ang II-induced AAA formation and rupture in Apoe-/- mice. IL12p35 silencing markedly increased the expression of inflammatory cytokines, including IL-1β, IL-6, and tumor necrosis factor-α (TNF-α), in both the serum and AAA aorta. Additionally, IL12p35 silencing exacerbated SMC apoptosis in Apoe-/- mice after Ang II infusion. IL12p35 silencing significantly increased signal transducer and activator of transcription (STAT) 4 phosphorylation levels in AAA mice, and STAT4 knockdown abolished the IL12p35-mediated proinflammatory response and SMC apoptosis. Interpretation. Silencing IL12p35 promotes AAA formation by activating the STAT4 pathway, and IL12p35 may serve as a novel and promising therapeutic target for AAA treatment.

In cancer cells, intracellular Ca2+ homeostasis is altered, and this is involved in tumor initiation, progression, and metastasis. However, little is known about the underlying mechanisms. Here, we report that transient receptor potential channel 5 (TrpC5), a receptor-activated non-selective Ca2+ channel, is correlated with tumor metastasis in colon cancer patients. Moreover, in colon cancer cells, overexpression of TrpC5 caused a robust rise in the concentration of ([Ca2+]i), decreased E-cadherin, and increased mesenchymal biomarker expression, then promoted cell migration, invasion, and proliferation. Interestingly, we found that TrpC5 mediated hypoxia-inducible factor 1α (HIF-1α) expression, activating Twist to promote the epithelial–mesenchymal transition (EMT). Notably, patients with high expression of TrpC5 displayed poorer overall and metastasis-free survival. Taken together, our findings demonstrate that TrpC5 induces the EMT through the HIF-1α–Twist signaling pathway to promote tumor metastasis in colon cancer. [ABSTRACT FROM AUTHOR]

An abstract of the article "Vascular Endothelial Growth Factor/Platelet-Derived Growth Factor Receptor Pathway Is Involved in Bone Marrow Mesenchymal Stem Cell Differentiation and Directional Migration Toward Gliomas," by Chaoshi Niu Yongfei Dong, and Ge Gao is presented.