Dynamic prediction of disease processes based on recurrent history and functional principal component analysis of longitudinal biomarkers: Application for ovarian epithelial cancer.

Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.

Continuously available ratio of SpO2/FiO2 serves as a noninvasive prognostic marker for intensive care patients with COVID-19.

RATIONALE: Oxygen saturation to fraction of inspired oxygen ratio (SpO2/FiO2) has been described as potential predictor of poor outcome for COVID-19, without considering its time-varying behavior though. METHODS: Prognostic value of SpO2/FiO2 was evaluated by jointly modeling the longitudinal responses of SpO2/FiO2 and time-to-event data retrieved from 280 severe and critically ill (intensive care) patients with COVID-19. RESULTS: A sharply decrease of SpO2/FiO2 from the first to second measurement for non-survivors was observed, and a strong association between square root SpO2/FiO2 and mortality risk was demonstrated, with a unit decrease in the marker corresponding to 1.82-fold increase in mortality risk (95% CI: 1.56-2.13). CONCLUSIONS: The current study suggested that SpO2/FiO2 could serve as a non-invasive prognostic marker to facilitate early adjustment for treatment, thus improving overall survival.

Integrative analysis identifies region- and sex-specific gene networks and Mef2c as a mediator of anxiety-like behavior.

The molecular mechanisms underlying multi-brain region origins and sexual dimorphism of anxiety remain unclear. Here, we leverage large-scale transcriptomics from seven brain regions in mouse models of anxiety and extensive experiments to dissect brain-region- and sex-specific gene networks. We identify 4,840 genes with sex-specific expression alterations across seven brain regions, organized into ten network modules with sex-biased expression patterns. Modular analysis prioritizes 86 sex-specific mediators of anxiety susceptibility, including myocyte-specific enhancer factor 2c (Mef2c) in the CA3 region of male mice. Mef2c expression is decreased in the pyramidal neurons (PyNs) of susceptible male mice. Up-regulating Mef2c in CA3 PyNs significantly alleviates anxiety-like behavior, whereas down-regulating Mef2c induces anxiety-like behavior in male mice. The anxiolytic effect of Mef2c up-regulation is associated with enhanced neuronal excitability and synaptic transmission. In summary, this study uncovers brain-region- and sex-specific networks and identifies Mef2c in CA3 PyNs as a critical mediator of anxiety in male mice.

TGR5-mediated lateral hypothalamus-dCA3-dorsolateral septum circuit regulates depressive-like behavior in male mice.

Although bile acids play a notable role in depression, the pathological significance of the bile acid TGR5 membrane-type receptor in this disorder remains elusive. Using depression models of chronic social defeat stress and chronic restraint stress in male mice, we found that TGR5 in the lateral hypothalamic area (LHA) predominantly decreased in GABAergic neurons, the excitability of which increased in depressive-like mice. Upregulation of TGR5 or inhibition of GABAergic excitability in LHA markedly alleviated depressive-like behavior, whereas down-regulation of TGR5 or enhancement of GABAergic excitability facilitated stress-induced depressive-like behavior. TGR5 also bidirectionally regulated excitability of LHA GABAergic neurons via extracellular regulated protein kinases-dependent Kv4.2 channels. Notably, LHA GABAergic neurons specifically innervated dorsal CA3 (dCA3) CaMKIIα neurons for mediation of depressive-like behavior. LHA GABAergic TGR5 exerted antidepressant-like effects by disinhibiting dCA3 CaMKIIα neurons projecting to the dorsolateral septum (DLS). These findings advance our understanding of TGR5 and the LHAGABA→dCA3CaMKIIα→DLSGABA circuit for the development of potential therapeutic strategies in depression.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.