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Autonomous driving is a complex problem that requires intelligent decision making, and it has recently garnered significant interest due to its potential advantages in convenience and safety. In autonomous driving, conventional path planning to reach a destination is a time-consuming challenge. Therefore, learning-based approaches have been successfully applied to the controller or decision-making aspects of autonomous driving. However, these methods often lack explainability, as passengers cannot discern where the vehicle is headed. Additionally, most experiments primarily focus on highway scenarios, which do not effectively represent road curvature. To address these issues, we propose a reinforcement-learning-based trajectory learning in the Frenet frame (RLTF), which involves learning trajectories in the Frenet frame. Learning trajectories enable the consideration of future states and enhance explainability. We demonstrate that RLTF achieves a 100% success rate in the simulation environment, considering future states on curvy roads with continuous obstacles while overcoming issues associated with the Frenet frame. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is one of the most common causes of intoxication. Delayed neurologic sequelae (DNS) have a major impact on prognosis of CO poisoning patients. Hyperbaric oxygen therapy (HBOT) is widely used to treat DNS. However, there is no consensus regarding the optimal timing of HBOT. This prospective study enrolled patients who visited the hospital from November 2019 to October 2022. The cutoff value for the latency to HBOT after CO exposure was determined, and the area under the receiver operating characteristic curve (AUC) was estimated. In total, 167 patients were divided into non-DNS and DNS groups. The initial Glasgow Coma Scale (GCS) score, CO exposure time, latency to HBOT after CO exposure, median length of hospital stay (p < 0.001) and creatine kinase (p = 0.016) showed significant differences. A GCS score ≤ 9 had an odds ratio (OR) of 5.059 (95% confidence interval [CI]: 1.602–15.976, p = 0.006), and latency to HBOT after CO exposure ≥ 200 min had an OR of 18.971 (95% CI: 4.310–83.508, p < 0.001). The AUC was 0.8235 (95% CI: 0.7504–0.8966). A GCS score ≤ 9 and latency to HBOT ≥ 200 min may be significant risk factors for DNS. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is one of the most common poisoning substances worldwide. Since acute brain injury (ABI) is an important determinant of the neurological outcome in CO poisoning, screening for patients at a high risk of developing ABI is essential for the proper treatment. This study identified predictors of ABI in patients with CO poisoning. This prospective registry-based study was conducted in patients who visited a tertiary care hospital for CO poisoning from August 2016 to June 2020. ABI was defined as the presence of acute hypoxic lesions on diffusion-weighted magnetic resonance imaging. Multiple logistic regression analysis was performed to identify the predictors of ABI. Of 231 patients, 64 (27.7%) showed ABI. Multiple logistic regression analysis showed that a Glasgow Coma Scale (GCS) score <9 at presentation (odds ratio [OR] 3.28, 95% confidence interval (CI) 1.08–10.01), creatinine level >1.2 mg/dL (OR 3.04, 95% CI 1.16–8.01), and C-reactive protein (CRP) level >9.2 mg/L (OR 4.38, 95% CI 1.41–13.65) predicted ABI in cases of acute CO poisoning. In CO poisoning, the GCS score at presentation, and serum creatinine and CRP levels, were useful predictors of ABI, and may help clinicians identify high-risk patients for whom treatment should be prioritized. [ABSTRACT FROM AUTHOR]

Introduction: Carbon monoxide (CO) poisoning can result in delayed neurological sequelae (DNS). Factors predicting DNS are still controversial. This study aims to determine whether acute brain lesions observed using diffusion-weighted magnetic resonance imaging (MRI) following acute CO poisoning are related to the subsequent development of DNS. Methods: This prospective study was conducted on patients with CO poisoning treated at a university hospital in Bucheon, Korea. From August 2016 to July 2019, a total of 283 patients visited the hospital because of CO poisoning. Exclusion criteria included age under 18 years, refusing hyperbaric oxygen therapy, refusing MRI, being discharged against medical advice, being lost to follow-up, having persistent neurological symptoms at discharge, and being transferred from another hospital 24 h after exposure. Results: Of the 154 patients included in the final study, acute brain lesions on MRI (ABLM) were observed in 49 patients (31.8%) and DNS occurred in 30 patients (19.5%). In a logistic regression analysis, lower Glasgow coma scale score and higher exposure time were associated with DNS, and the presence of ABLM in white matter was significantly associated with DNS (OR 6.741; 95% CI, 1.843–24.660; p = 0.004). Conclusion: The presence of ABLM in white matter was significantly related to the occurrence of DNS. Early prediction of the risk of developing DNS through MRI may be helpful in treating patients with CO poisoning. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins exist as highly dynamic conformational ensembles of diverse forms. However, the majority of virtual screening only focuses on proteins with defined structures. This means that computer-aided drug discovery is restricted. As a breakthrough, understanding the structural characteristics of intrinsically disordered proteins and its application can open the gate for unrestricted drug discovery. First, we segmented the target disorder-to-order transition region into a series of overlapping 20-amino-acid-long peptides. Folding prediction generated diverse conformations of these peptides. Next, we applied molecular docking, new evaluation score function, and statistical analysis. This approach successfully distinguished known compounds and their corresponding binding regions. Especially, Myc proto-oncogene protein (MYC) inhibitor 10058F4 was well distinguished from others of the chemical compound library. We also studied differences between the two Methyl-CpG-binding domain protein 2 (MBD2) inhibitors (ABA (2-amino-N-[[(3S)-2,3-dihydro-1,4-benzodioxin-3-yl]methyl]-acetamide) and APC ((R)-(3-(2-Amino-acetylamino)-pyrrolidine-1-carboxylic acid tert-butyl ester))). Both compounds bind MBD2 through electrostatic interaction behind its p66α-binding site. ABA is also able to bind p66α through electrostatic interaction behind its MBD2-binding site while APC-p66α binding was nonspecific. Therefore, structural heterogeneity mimicking of the disorder-to-order transition region at the peptide level and utilization of the new docking score function represent a useful approach that can efficiently discriminate compounds for expanded virtual screening toward intrinsically disordered proteins. [ABSTRACT FROM AUTHOR]

Murine erythroleukemia (MEL) cells are often employed as a model to dissect mechanisms of erythropoiesis and erythroleukemia in vitro. Here, an allograft model using MEL cells resulting in splenomegaly was established to develop a diagnostic model for isolation/quantification of metastatic cells, anti-cancer drug screening, and evaluation of the tumorigenic or metastatic potentials of molecules in vivo. In this animal model, circulating MEL cells from the blood stream were successfully isolated and quantified with an additional in vitro cultivation step. In terms of the molecular-pathological analysis, we were able to successfully evaluate the functional discrimination between methyl-CpG-binding domain 2 (Mbd2) and p66α in erythroid differentiation, and tumorigenic potential in spleen and blood stream of allograft model mice. In addition, we found that the number of circulating MEL cells in anti-cancer drug-treated mice was dose-dependently decreased. Our data demonstrate that the newly established allograft model is useful to dissect erythroleukemia pathologies and non-invasively provides valuable means for isolation of metastatic cells, screening of anti-cancer drugs, and evaluation of the tumorigenic potentials. [ABSTRACT FROM AUTHOR]