Quantifying the underlying landscape, entropy production and biological path of the cell fate decision between apoptosis and pyroptosis

We aimed to evaluate the association between inflammation-based prognostic markers and mortality after hip replacement. From March 2010 to June 2020, we identified 5,369 consecutive adult patients undergoing hip replacement with C-reactive protein (CRP), albumin, and complete blood count measured within six months before surgery. Receiver operating characteristic (ROC) curves were generated to evaluate predictabilities and estimate thresholds of CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Patients were divided according to threshold, and mortality risk was compared. The primary outcome was one-year mortality, and overall mortality was also analyzed. One-year mortality was 2.9%. Receiver operating characteristics analysis revealed areas under the curve of 0.838, 0.832, 0.701, and 0.732 for CAR, NLR, PLR, and modified Glasgow Prognostic Score, respectively. The estimated thresholds were 2.10, 3.16, and 11.77 for CAR, NLR, and PLR, respectively. According to the estimated threshold, high CAR and NLR were associated with higher one-year mortality after adjustment (1.0% vs. 11.7%; HR = 2.16; 95% CI 1.32–3.52; p = 0.002 for CAR and 0.8% vs. 9.6%; HR = 2.05; 95% CI 1.24–3.39; p = 0.01 for NLR), but PLR did not show a significant mortality increase (1.4% vs. 7.4%; HR = 1.12; 95% CI 0.77–1.63; p = 0.57). Our study demonstrated associations of preoperative levels of CAR and NLR with postoperative mortality in patients undergoing hip replacement. Our findings may be helpful in predicting mortality in patients undergoing hip replacement.

Section: Discussionmentioning

confidence: 88%

Association between inflammation-based prognostic markers and mortality after hip replacement

Oh,

Kwon,

Jin

et al. 2024

“…In such application areas, uncertainty quantification supports the understanding of applied models based on human reasoning, helps to judge the anticipated reliability of predictions for practical applications, and thereby increases the acceptance of models in interdisciplinary research settings. The benefits of uncertainty quantification also extend to other areas in pharmaceutical and biological research, for example, for time series-based predictions of cell death in different biological systems using ML models 45 – 47 . Here, evaluating the uncertainty of different models can aid in prioritizing alternative ML approaches and in advancing the understanding of suitable application domains and domain-dependent model limitations.…”

Section: Discussionmentioning

confidence: 99%

Relationship between prediction accuracy and uncertainty in compound potency prediction using deep neural networks and control models

Roth,

Bajorath

2024

The assessment of prediction variance or uncertainty contributes to the evaluation of machine learning models. In molecular machine learning, uncertainty quantification is an evolving area of research where currently no standard approaches or general guidelines are available. We have carried out a detailed analysis of deep neural network variants and simple control models for compound potency prediction to study relationships between prediction accuracy and uncertainty. For comparably accurate predictions obtained with models of different complexity, highly variable prediction uncertainties were detected using different metrics. Furthermore, a strong dependence of prediction characteristics and uncertainties on potency levels of test compounds was observed, often leading to over- or under-confident model decisions with respect to the expected variance of predictions. Moreover, neural network models responded very differently to training set modifications. Taken together, our findings indicate that there is only little, if any correlation between compound potency prediction accuracy and uncertainty, especially for deep neural network models, when predictions are assessed on the basis of currently used metrics for uncertainty quantification.

“…Additionally, concentrating on the interactions between miRNAs and lncRNAs emerges as a promising avenue for advancing VVLEs management and patient care 38 – 40 . The employment of theoretical models based on ordinary differential equations (ODE) is particularly crucial in shedding light on the regulatory mechanisms within gene/protein signaling networks associated with VVLEs recurrence 41 – 43 . Such models offer profound insights into the complex biological systems governing variceal recurrence, highlighting the importance of these mechanisms for a deeper understanding of the disease’s causes 35 , 44 , 45 .…”

Section: Discussionmentioning

confidence: 99%

A prognostic nomogram for recurrence survival in post-surgical patients with varicose veins of the lower extremities

Hu,

Deng

et al. 2024

Varicose veins of the lower extremities (VVLEs) are prevalent globally. This study aims to identify prognostic factors and develop a prediction model for recurrence survival (RS) in VVLEs patients after surgery. A retrospective analysis of VVLEs patients from the Third Hospital of Nanchang was conducted between April 2017 and March 2022. A LASSO (Least Absolute Shrinkage and Selection Operator) regression model pinpointed significant recurrence predictors, culminating in a prognostic nomogram. The model’s performance was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression identified seven predictors for the nomogram predicting 1-, 2-, and 5-year RS. These predictors were age, body mass index (BMI), hypertension, diabetes, the Clinical Etiological Anatomical Pathophysiological (CEAP) grade, iliac vein compression syndrome (IVCS), and postoperative compression stocking duration (PCSD). The nomogram’s C-index was 0.716, with AUCs (Area Under the Curve scores) of 0.705, 0.725, and 0.758 for 1-, 2-, and 5-year RS, respectively. Calibration and decision curve analyses validated the model’s predictive accuracy and clinical utility. Kaplan–Meier analysis distinguished between low and high-risk groups with significant prognostic differences (P < 0.05). This study has successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery, enhancing personalized care and informing clinical decision-making.