A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.

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.

Impact of cladribine, cytarabine, and G-CSF (CLAG) as a bridging therapy prior to allogeneic hematopoietic stem cell transplantation in relapsed or refractory acute myeloid leukemia.

The combination of cladribine, cytarabine, and G-CSF (CLAG) has exhibited robust synergistic anti-leukemia activity as an induction therapy (IT) in acute myeloid leukemia (AML). However, the impact of CLAG as a bridging therapy (BT) administered between IT and allogeneic hematopoietic stem cell transplantation (allo-HSCT) for patients with relapsed or refractory (R/R) AML remains uncertain. In this retrospective study, we examined the efficacy of CLAG as a transitional strategy prior to allo-HSCT in R/R AML. We included 234 patients with R/R AML who received the modified busulfan plus cyclophosphamide conditioning regimen for allo-HSCT in our center during the past 6 years, performed a propensity-score matching analysis, partitioned them into four distinct cohorts, and further integrated them into the CLAG group and non-CLAG group based on response to IT and utilization of CLAG. Our cohorts encompassed 12 patients in Cohort A (modified composite complete remission (mCRc) after IT, CLAG), 31 in Cohort B (mCRc after IT, non-CLAG), 35 in Cohort C (non-complete remission (non-CR) after IT, CLAG), and 80 in Cohort D (non-CR after IT, non-CLAG). Intriguingly, among patients with non-CR status, the administration of CLAG correlated with a notably statistically diminished risk of relapse and improved survival at 2-year follow-up (Cohort C vs. Cohort D). Employing CLAG as a BT prior to allo-HSCT demonstrates substantial effectiveness, a relative degree of safety, and manageable toxicity in selected R/R AML cases.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

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.

High FAAP24 expression reveals poor prognosis and an immunosuppressive microenvironment shaping in AML.

BACKGROUND: As a core member of the FA complex, in the Fanconi anemia pathway, FAAP24 plays an important role in DNA damage repair. However, the association between FAAP24 and patient prognosis in AML and immune infiltration remains unclear. The purpose of this study was to explore its expression characteristics, immune infiltration pattern, prognostic value and biological function using TCGA-AML and to verify it in the Beat AML cohort. METHODS: In this study, we examined the expression and prognostic value of FAAP24 across cancers using data from TCGA, TARGET, GTEx, and GEPIA2. To further investigate the prognosis in AML, development and validation of a nomogram containing FAAP24 were performed. GO/KEGG, ssGSEA, GSVA and xCell were utilized to explore the functional enrichment and immunological features of FAAP24 in AML. Drug sensitivity analysis used data from the CellMiner website, and the results were confirmed in vitro. RESULTS: Integrated analysis of the TCGA, TARGET and GTEx databases showed that FAAP24 is upregulated in AML; meanwhile, high FAAP24 expression was associated with poor prognosis according to GEPIA2. Gene set enrichment analysis revealed that FAAP24 is implicated in pathways involved in DNA damage repair, the cell cycle and cancer. Components of the immune microenvironment using xCell indicate that FAAP24 shapes an immunosuppressive tumor microenvironment (TME) in AML, which helps to promote AML progression. Drug sensitivity analysis showed a significant correlation between high FAAP24 expression and chelerythrine resistance. In conclusion, FAAP24 could serve as a novel prognostic biomarker and play an immunomodulatory role in AML. CONCLUSIONS: In summary, FAAP24 is a promising prognostic biomarker in AML that requires further exploration and confirmation.

Robust radiosensitization of hemoglobin-curcumin nanoparticles suppresses hypoxic hepatocellular carcinoma.

BACKGROUND: Radioresistance inducing by hypoxic microenvironment of hepatocellular carcinoma is a major obstacle to clinical radiotherapy. Advanced nanomedicine provides an alternative to alleviate the hypoxia extent of solid tumor, even to achieve effective synergistic treatment when combined with chemotherapy or radiotherapy. RESULTS: Herein, we developed a self-assembled nanoparticle based on hemoglobin and curcumin for photoacoustic imaging and radiotherapy of hypoxic hepatocellular carcinoma. The fabricated nanoparticles inhibited hepatoma migration and vascular mimics, and enhanced the radiosensitivity of hypoxic hepatoma cells in vitro via repressing cell proliferation and DNA damage repair, as well as inducing apoptosis. Benefit from oxygen-carrying hemoglobin combined with polyphenolic curcumin, the nanoparticles also effectively enhanced the photoacoustic contrast and the efficacy of radiotherapy for hepatocellular carcinoma in vivo. CONCLUSIONS: Together, the current study offered a radiosensitization platform for optimizing the efficacy of nanomedicines on hypoxic radioresistant tumor.

Phytophthora cinnamomi causing root rot on Rhododendron lapponicum and control it using potential biocontrol agents.

Rhododendron lapponicum (R. lapponicum) is a dwarf Rhododendron species, which is severely infected with root rot and wilt in Yunnan province, China. However, the causal agent causing these symptoms was unknown. An isolate, Pci-1 was identified as Phytophthora cinnamomi, based on its morphology and the sequences of ß-tubulin, internal transcribed spacer, and Ypt1 genes and verified according to the Koch's postulate. We found that this pathogen could infect 14 species of plants, including Althaea rosea, Viburnum cylindricum, and Brassica napus. Strain Pci-1 could cause R. lapponicum to wither and die; and it grows best in an oat medium with pH 7.0 - 8.0 and an optimum temperature of 27°C. We suggest that the rhizosphere of R. lapponicum treated with biocontrol strains Paenibacillus polymyxoides P2-5 and Trichoderma asperellum Tv-1 showed a significant inhibitory effect on pathogen Pci-1. The inhibitory effect of 70% dimethomorph + cymoxanil was significantly higher with EC50 and EC90 values of 0.1894 and 0.3618 a.i. µg/ml, respectively. Greenhouse experiments revealed that the pathogen load is decreased in the presence of potential antagonists. This study provides fundamentals on risk assessment and theoretical support for the management of P. cinnamomi pathogen and contributes significantly to the planting of forest and horticultural crops in a disease-free environment.

Bullous Pemphigoid and Diabetes medications: A disproportionality analysis based on the FDA Adverse Event Reporting System.

Background: The world's first Diabetes Medications (Insulin) was marketed in October 1923. Some studies suggested the association of diabetes medications with Bullous Pemphigoid (BP), especially the Dipeptidyl Peptidase 4 (DPP-4) inhibitors. The study aims to detect an association between diabetes medications (focusing on DPP-4 inhibitors) and bullous pemphigoid based on FDA Adverse Event Reporting System (FAERS). Methods: All spontaneous reports of diabetes medications inhibitors-related BP recorded in the FAERS between March 2004 and August 2020 were included in the present study. Disproportionality analysis was performed to find the signal between diabetes medications and BP. The Chi-Squared with Yates' correction (χ2 Yates), proportional reporting ratio (PRR) and the lower limit of the 95% confidence interval of the Reporting Odds Ratio (ROR025) were calculated as a measure. A signal was detected when ROR025 > 1, PRR > 2, χ2 Yates > 4 and at least 3 cases. Results: There were 3770 reports for BP in FAERS. The strongest signal for diabetes medications-BP association were DDP-4 inhibitors (ROR025: 13.700, PRR: 15.408), followed by Meglitinides (ROR025: 12.708, PRR: 16.777), Non-sulfonylureas (ROR025: 6.434, PRR: 7.016), Alpha-glucosidase inhibitors (ROR025: 6.105, PRR: 10.738), Sulfonylureas (ROR025:2.655, PRR: 3.200). Conclusions: This study detected a strong signal between BP and DDP-4 inhibitors, alpha-glucosidase inhibitors, meglitinides, non-sulfonylureas, and sulfonylureas in FAERS. The signal was significantly higher with alogliptin than with the other DPP-4 inhibitors. The study doesn't suggest the association between the incretin mimetics, insulin, SGLT-2 inhibitors, thiazolidinediones and BP in FAERS.

Reductions in mortality resulting from COVID-19 quarantine measures in China.

BACKGROUND: To explore the impact of quarantine measures on the cause of death. METHODS: We use time series analysis with the data from death cause surveillance database of Suzhou from January 2017 to December 2019 to estimate the expected deaths from January to June 2020 and compare these expected deaths with the reported numbers of deaths. RESULTS: After the implementation of epidemic prevention measures in Suzhou in the first 3 months, overall number of all-cause deaths declined for 5.36, 7.54 and 7.02% compared with predicted numbers. The number of deaths from respiratory causes and traffic accidents declined shapely by 30.1 and 26.9%, totally. When quarantine measures were released (April-June), however, the observed numbers of total deaths exceeded the predicted deaths. People aged over 70 accounted for 91.6% of declined death number in respiratory causes and people aged over 60 accounted for 68.0% of declined death number in traffic accidents. Women over the age of 80 benefited the most from respiratory prevention (accounts for 41% of all reductions), whereas women aged over 60 benefited the most from traffic control (44%). CONCLUSIONS: Overall, the whole population benefited from the epidemic prevention measures especially elderly females. This study is a useful supplement to encourage the government to develop regular preventive measures under the era of normalized epidemic.

BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology.

SUMMARY: BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM. AVAILABILITY AND IMPLEMENTATION: The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.

Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information.

BACKGROUND: Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes. RESULTS: We here propose a Bayesian hierarchical Cox survival model, called the group spike-and-slab lasso Cox (gsslasso Cox), for predicting disease survival outcomes and detecting associated genes by incorporating group structures of biological pathways. Our hierarchical model employs a novel prior on the coefficients of genes, i.e., the group spike-and-slab double-exponential distribution, to integrate group structures and to adaptively shrink the effects of genes. We have developed a fast and stable deterministic algorithm to fit the proposed models. We performed extensive simulation studies to assess the model fitting properties and the prognostic performance of the proposed method, and also applied our method to analyze three cancer data sets. CONCLUSIONS: Both the theoretical and empirical studies show that the proposed method can induce weaker shrinkage on predictors in an active pathway, thereby incorporating the biological similarity of genes within a same pathway into the hierarchical modeling. Compared with several existing methods, the proposed method can more accurately estimate gene effects and can better predict survival outcomes. For the three cancer data sets, the results show that the proposed method generates more powerful models for survival prediction and detecting associated genes. The method has been implemented in a freely available R package BhGLM at https://github.com/nyiuab/BhGLM .

Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.

Motivation: Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. Results: We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. Availability and implementation: The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Contact: nyi@uab.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

The spike-and-slab lasso Cox model for survival prediction and associated genes detection.

MOTIVATION: Large-scale molecular profiling data have offered extraordinary opportunities to improve survival prediction of cancers and other diseases and to detect disease associated genes. However, there are considerable challenges in analyzing large-scale molecular data. RESULTS: We propose new Bayesian hierarchical Cox proportional hazards models, called the spike-and-slab lasso Cox, for predicting survival outcomes and detecting associated genes. We also develop an efficient algorithm to fit the proposed models by incorporating Expectation-Maximization steps into the extremely fast cyclic coordinate descent algorithm. The performance of the proposed method is assessed via extensive simulations and compared with the lasso Cox regression. We demonstrate the proposed procedure on two cancer datasets with censored survival outcomes and thousands of molecular features. Our analyses suggest that the proposed procedure can generate powerful prognostic models for predicting cancer survival and can detect associated genes. AVAILABILITY AND IMPLEMENTATION: The methods have been implemented in a freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ ). CONTACT: nyi@uab.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The nuclear transcription factor RelB functions as an oncogene in human lung adenocarcinoma SPC-A1 cells.

BACKGROUND: Lung cancer is a leading public health issue worldwide. Although therapeutic approaches have improved drastically in the last decades, the prognosis of lung cancer patients remains suboptimal. The canonical nuclear transcription factor kappa B (NF-κB) signalling pathway is critical in the carcinogenesis of lung cancer. The non-canonical NF-κB signalling pathway (represented by RelB) has attracted increasing attention in the pathogenesis of haematological and epithelial malignancies. However, the function of RelB in non-small cell lung cancer (NSCLC) is still unclear. Recently, high expression of RelB has been detected in NSCLC tissues. We have also demonstrated that RelB expression is an independent prognostic factor in NSCLC patients. METHODS: The mRNA and protein expression of RelB in NSCLC tissues were detected by qRT-PCR and IHC assay. The cell growth of SPC-A1 cells was detected in real-time using the x-Celligence system and xenograft tumour assays. The proliferation capability of cells was detected using a CFSE assay. Cell apoptosis was measured using Annexin V/PI staining, cell cycle was analyzed by the cytometry. Cell migration abilities were detected using the x-Celligence system and wound healing assays. The relative amounts of the active and inactive gelatinases MMP-2 and MMP-9 were examined using gelatin zymography experiments. Apoptosis of RelB depletion SPC-A1 cells after ionizing radiation at 8 Gy. The expression of cellular proliferation signal pathway related-proteins were examined by Western blot analysis. RESULTS: The expression of RelB increases in NSCLC tissues. High RelB expression was significantly correlated with advanced-metastatic stage in patients with NSCLC. RelB-silencing inhibits cell growth in vitro and in vivo. We found that RelB affected cell proliferation by regulating AKT phosphorylation. RelB silencing attenuates the migration and invasion abilities of SPC-A1 cells and is likely related to the down regulation of MMP-9 activity and Integrin ß-1 expression. In addition, RelB modulated radiation-induced survival of NSCLC cells predominantly by regulating Bcl-xL expression. CONCLUSIONS: Given the involvement of RelB in cell proliferation, migration, invasion, and radio-resistance, RelB functions as an oncogene in NSCLC cells. Our data here shed light on unexplored aspects of RelB in NSCLC.

Negative binomial mixed models for analyzing microbiome count data.

BACKGROUND: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. RESULTS: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. CONCLUSIONS: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.

Clinical factors and incidence of prolonged fever in neurosurgical patients.

AIMS AND OBJECTIVES: To describe the incidence of prolonged fever in patients admitted to the neurosurgery department, and the corresponding risk indicators. BACKGROUND: Prolonged fever was defined as a temperature higher than 38·3°C lasting more than five days. Prolonged fever is a common phenomenon and could lead to worsened outcomes in specific patient groups, especially for those with brain injury. However, the studies on prolonged fever in neurosurgical patients are limited and insufficient. DESIGN: A retrospective observational study. METHODS: Retrospective data were collected from 1 January 2014 to 31 December 2014, at the neurosurgical department of a large teaching hospital. We performed univariate and multivariate analyses to identify independent indicators for prolonged fever vs. short-term fever. RESULTS: Among 2845 patients, prolonged fever occurred in 466 (16%). The older patients were associated with longer duration of mechanical ventilation and hospital stay. It predominantly occurred in patients with subarachnoid haemorrhage (SAH) and traumatic brain injury. Patients receiving antibiotic treatment tended to manifest prolonged fever more frequently. Multivariate analysis revealed that the use of antibiotics, central venous catheter and prolonged mechanical ventilation were independent risk predictors for prolonged fever. Patients diagnosed with brain tumour seemed to be not associated with prolonged fever. CONCLUSIONS: Prolonged fever is the common complication in neurosurgical patients. The risks of prolonged fever in patients are attributed to antibiotic therapy, use of central venous catheter and prolonged mechanical ventilation. Indicators of prolonged fever are helpful for better identification of high-risk patients and fever control. RELEVANCE TO CLINICAL PRACTICE: A better reveal on the epidemiology and predictable factors of prolonged fever in neurosurgical patients will provide a better understanding on those patients who are most at risk, and therefore contribute to fever control and better outcome.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank.

BACKGROUND: Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them. METHODS: We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD. RESULTS: The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve. LIMITATIONS: The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent. CONCLUSIONS: Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

Adverse event profile of CGRP monoclonal antibodies: findings from the FDA adverse event reporting database.

BACKGROUND: Four CGRP Monoclonal Antibodies (mAbs) have been approved for migraine prophylaxis by the Food and Drug Administration (FDA) since 2018. However, there are concerns about the safety of these four drugs for real-world use. OBJECTIVE: To compare the adverse event profiles of four CGRP-mAbs with FAERS data. METHODS: The study was based on records from the FAERS database. Only reports containing one of the active ingredients with CGRP-mAbs were included in this study. Disproportionality analyses including but not limited to reporting odds ratio (ROR) and information components (IC) were conducted to identify drug-AE associations. RESULTS: In total, 58110 reports were identified for CGRP-mAbs. 80 overlapping signals were disproportionately reported. They affected a range of organs and systems, including the gastrointestinal and cardiovascular systems, skin, and hair. Additionally, the rare cardiovascular adverse events were significantly different among the four CGRP-mAbs. CONCLUSION: We identified numerous shared underlying signals (overlapping signals) for CGRP-mAbs as suspected drugs in multiple systems and organs. The unlabeled common signals may indicate potential safety issues. In addition, the underlying safety signals varied among the four CGRP-mAbs, particularly in the cardiovascular system, and further studies are needed to confirm these associations and the potential clinical implications.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking.

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.

Loneliness, Living Alone, and Risk of Cardiovascular Disease Among Older Adults in China.

BACKGROUND: Older adults are prone to live alone and feel lonely. The main objective of this study was to assess the associations of loneliness and living alone with cardiovascular disease (CVD) among community-dwelling older individuals in China. METHODS: We conducted a longitudinal analysis on 3 661 participants aged older than 65 years from the latest 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cox proportional hazards models were used to assess the associations of loneliness and living alone with CVD risk, with adjustment for confounding factors. RESULTS: A total of 616 incident CVD cases were identified during follow-up. Participants who reported feeling lonely experienced a 28% increased risk of developing CVD after adjustment for sociodemographic characteristics, lifestyle factors, and baseline health status (adjusted hazard ratio [HR]: 1.28, 95% confidence interval [CI]: 1.01-1.62; ptrend = .046). In contrast, no significant association was observed between living alone and CVD risk. Subgroup analyses showed that among those individuals who lived alone, often feeling lonely doubled the risk of CVD compared to never being lonely (HR: 2.17, 95% CI: 1.20-3.93; ptrend = .007). CONCLUSIONS: Loneliness was an independent risk factor for CVD among Chinese older adults. Our findings underscore the importance of addressing loneliness in the prevention of CVD among older individuals, especially those who live alone.

Association of altitude and frailty in Chinese older adults: using a cumulative frailty index model.

Objective: The 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. Methods: This 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. Results: There 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 <0.001). Multivariate ordinal logistic regression analysis revealed an association between altitude and frailty, OR = 1.91 (95% CI: 1.38-2.64) in mid-high altitude and 2.49 (95% CI:1.40-4.45) in high altitude. The same trend of association was found in the univariate analysis. The FS increased by 1.69 (95% CI: 0.78-2.60) at mid-high altitude and 3.24 (95%CI:1.66-4.81) at high altitude compared to medium altitude. Conclusion: The study indicates that high altitude exposure is an associated factor for frailty in older adults. This association become stronger with higher altitudes. As a result, it is essential to conduct early frailty screening for residents living at high altitudes.