Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Real-Life Efficacy and Tolerability of Lacosamide in Pediatric Patients Aged 4 Years or Older with Drug-Resistant Epilepsy

Purpose The aim of this study was to evaluate the efficacy and safety of adjunctive lacosamide therapy in pediatric patients aged ≥4 years with drug-resistant epilepsy (DRE). Methods Medical records of children aged 4 to 19 years treated with lacosamide as adjunctive therapy for DRE were retrospectively reviewed. The patients were divided into two groups according to their age at the start of lacosamide treatment: group A (aged 4–15 years) and group B (aged 16–19 years). Changes in seizure frequency from baseline, adverse events, and the retention rate were evaluated at each follow-up visit. Results Sixty-two patients (33 males and 29 females) with a mean age of 11.4 years (range, 4 to 19) were included. The mean duration of follow-up was 20.1±12.9 months. The mean maintenance dose of lacosamide was 6.7±4.8 mg/kg/day. Forty-two patients (67.7%) were responders (≥50% reduction in seizures) with 19.4% (12/62) achieving freedom from seizures. The response rate did not differ significantly between groups A and B (67.6% vs. 68.0%, P=0.795) and was not affected by the concomitant use of sodium channel blockers. Significant independent factors associated with a good response to lacosamide treatment were a shorter duration of epilepsy (P=0.035) and fewer concomitant anti-seizure medications (P=0.002). Mild transient adverse events were observed in 20 patients (32.3%). Conclusion Lacosamide adjunctive therapy was efficacious and tolerated in children aged ≥4 years with DRE. Early use of lacosamide may be helpful for a good response to drug-resistant seizures

Ursolic acid exerts anti-cancer activity by suppressing vaccinia-related kinase 1-mediated damage repair in lung cancer cells

Many mitotic kinases have been targeted for the development of anti-cancer drugs, and inhibitors of these kinases have been expected to perform well for cancer therapy. Efforts focused on selecting good targets and finding specific drugs to target are especially needed, largely due to the increased frequency of anti-cancer drugs used in the treatment of lung cancer. Vaccinia-related kinase 1 (VRK1) is a master regulator in lung adenocarcinoma and is considered a key molecule in the adaptive pathway, which mainly controls cell survival. We found that ursolic acid (UA) inhibits the catalytic activity of VRK1 via direct binding to the catalytic domain of VRK1. UA weakens surveillance mechanisms by blocking 53BP1 foci formation induced by VRK1 in lung cancer cells, and possesses synergistic anti-cancer effects with DNA damaging drugs. Taken together, UA can be a good anti-cancer agent for targeted therapy or combination therapy with DNA damaging drugs for lung cancer patients.Published versio