Purpose: Low levels of low-density lipoprotein cholesterol (LDL-C) have been suggested to increase the risk of hemorrhagic transformation (HT) following acute ischemic stroke. However, the literature on the relationship between LDL-C levels and post-thrombectomy HT is sparse. The aim of our study is to investigate the association between LDL-C and delayed parenchymal hematoma (PH) that was not seen on immediate post-thrombectomy dual-energy computed tomography (DECT). Materials and Methods: A retrospective analysis was conducted on all patients with anterior circulation large vessel occlusion who underwent thrombectomy at a comprehensive stroke center from 2018–2021. Per institutional protocol, all patients received DECT immediately post-thrombectomy and magnetic resonance imaging or CT at 24 hours. The presence of immediate hemorrhage was assessed by DECT, while delayed PH was assessed by 24-hour imaging. Multivariable analysis was performed to identify predictors of delayed PH. Patients with hemorrhage on immediate post-thrombectomy DECT were excluded to select only those with delayed PH. Results: Of 159 patients without hemorrhage on immediate post-thrombectomy DECT, 18 (11%) developed delayed PH on 24-hour imaging. In multivariable analysis, LDL-C (odds ratio [OR], 0.76; P=0.038; 95% confidence interval [CI], 0.59–0.99; per 10 mg/dL increase) independently predicted delayed PH. High-density lipoprotein cholesterol, triglyceride, and statin use were not associated. After adjusting for potential confounders, LDL-C ≤50 mg/dL was associated with an increased risk of delayed PH (OR, 5.38; P=0.004; 95% CI, 1.70–17.04), while LDL-C >100 mg/dL was protective (OR, 0.26; P=0.041; 95% CI, 0.07–0.96). Conclusion LDL-C ≤50 mg/dL independently predicted delayed PH following thrombectomy and LDL-C >100 mg/dL was protective, irrespective of statin. Thus, patients with low LDL-C levels may warrant vigilant monitoring and necessary interventions, such as blood pressure control or anticoagulation management, following thrombectomy even in the absence of hemorrhage on immediate post-thrombectomy DECT.

Background: The purpose of this study is to construct a job-exposure matrix for lead that accounts for industry and work processes within industries using a nationwide exposure database. Methods: We used the work environment measurement data (WEMD) of lead monitored nationwide from 2015 to 2016. Industrial hygienists standardized the work process codes in the database to 37 standard process and extracted key index words for each process. A total of 37 standardized process codes were allocated to each measurement based on an automated key word search based on the degree of agreement between the measurement information and the standard process index. Summary statistics, including the arithmetic mean, geometric mean, and 95th percentile level (X95), was calculated according to industry, process, and industry process. Using statistical parameters of contrast and precision, we compared the similarity of exposure groups by industry, process, and industry process. Results: The exposure intensity of lead was estimated for 583 exposure groups combined with 128 industry and 35 process. The X95 value of the “casting” process of the “manufacture of basic precious and non-ferrous metals” industry was 53.29 μg/m3, exceeding the occupational exposure limit of 50 μg/m3. Regardless of the limitation of the minimum number of samples in the exposure group, higher contrast was observed when the exposure groups were by industry process than by industry or process. Conclusion We evaluated the exposure intensities of lead by combination of industry and process. The results will be helpful in determining more accurate information regarding exposure in lead-related epidemiological studies.

Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.