The recognition of mislocalized DNA and RNA by cGAS–STING, RIG-I/MDA5, the OAS–RNase L axis, and endosomal TLR3/7/8 has emerged as a unifying paradigm linking cancer, autoinflammation, and antiviral immunity. Counterbalancing these sensors is a structurally heterogeneous nuclease repertoire whose distinct substrate specificities, subcellular compartments and pH optima constrain ligand availability in space and time. Disruption of this equilibrium drives disease through two mirror-image mechanisms. In cancer, DNASE1 is inactivated by tumor-derived G-actin, DNASE1L3 is transcriptionally silenced in hepatocellular, colorectal and lung adenocarcinomas, and DNASE2 is upregulated in immunologically “cold” tumors, together permitting neutrophil-extracellular-trap–mediated exclusion of cytotoxic T cells and suppression of cytosolic DNA sensing. In autoimmunity, biallelic loss of DNASE1L3, TREX1, RNase H2, ADAR1 or RNase T2 produces the interferonopathies of systemic lupus and Aicardi–Goutières syndrome. Diagnostically, nuclease-specific cleavage signatures have matured into cell-free DNA fragmentomics validated across 13 cancer types in a 3,021-patient cohort; therapeutically, the field now spans engineered actin-resistant DNASE1/DNASE1L3 biologics, selective TREX1 and ADAR1 inhibitors entering first-in-human evaluation, STING-activating nanomedicines, RNase Fc-fusions such as RSLV-132 in phase 2a lupus, and JAK1/2 inhibition as standard of care in Aicardi–Goutières syndrome. We synthesize this evidence as a two-fate problem: whether an endogenous nucleic acid accumulates at these sensors to drive autoinflammation or is cleared by nucleases before detection is set by the balance between sensor engagement and clearance capacity. The therapeutic corollary acts on the ligand rather than the enzyme, restoring ligand availability where disease is malignant and restoring clearance where disease is self-directed.
Lintao Xia, Yixi Wang, Xiu-Li Yan et al.· Molecular Biomedicine· 0 citations
Patients with lumbar spinal stenosis are typically elderly with multiple comorbidities, necessitating accurate preoperative anesthetic risk assessment. The American Society of Anesthesiologists (ASA) classification quantifies functional reserve and disease burden, serving as a widely used tool for risk stratification. However, ASA classification is often influenced by subjective factors including physician experience and varies among clinicians with different seniority, while the assessment process remains time-consuming. This study aimed to develop an automated model for anesthetic risk stratification and evaluate its performance, with the goal of providing decision support for surgical and anesthetic management in this patient population.
Clinical data of 600 patients with lumbar spinal stenosis were collected and randomly divided into training (
n
= 480) and internal validation (
n
= 120) sets. An additional 100 patients from another tertiary hospital formed an external validation set. The model was validated and hyperparameter-tuned using k-fold cross-validation. Model performance, including overall classification and high-risk identification, was evaluated using accuracy, macro-average precision, macro-average recall, macro-average F1 score, weighted Kappa, linear weighted accuracy, positive predictive value, and negative predictive value.
In the internal validation set, the accuracy, macro-average precision, macro-average recall, macro-average F1 score, weighted Kappa coefficient and linear weighted accuracy of the model are 0.97, 0.96, 0.95, 0.96, 0.93 and 0.98 respectively, while in the external validation set, they are 0.97, 0.97, 0.94, 0.96, 0.93 and 0.99 respectively. The confusion matrix heatmap shows that the error is mainly concentrated between adjacent classes, and there is no cross-class misjudgment. In the internal validation set, the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Kappa coefficient for identifying high-risk patients were 0.98, 0.95, 0.98, 0.91, 0.99, and 0.92, respectively. In the external validation set, these values were 0.98, 0.93, 0.99, 0.93, 0.99, and 0.92, respectively.
The machine learning model developed in this study demonstrates strong capability in stratifying anesthetic risk for patients with lumbar spinal stenosis, providing valuable reference for selecting surgical and anesthetic approaches.
Jitao Yang, Yixi Wang, Qihao Chen et al.· Frontiers in Medicine· 0 citations