Chrysoeriol, a flavonoid naturally found in several plants, including Danggui Susan, a traditional herbal medicine, exhibits promising anti-inflammatory and antioxidant properties. Its potential to prevent cardiovascular diseases, primarily through inhibiting platelet activation and aggregation, has attracted significant interest. This study aimed to investigate the molecular mechanisms underlying the antiplatelet effects of chrysoeriol. The compound effectively suppressed collagen-induced platelet aggregation without inducing cytotoxicity. Chrysoeriol elevated intracellular levels of cyclic AMP (cAMP) and cyclic GMP (cGMP), enhanced inositol 1,4,5-trisphosphate receptor (IP 3R) phosphorylation, and reduced cytosolic calcium (Ca2+ ) mobilization, all of which contributed to its antiplatelet action. Furthermore, chrysoeriol inhibited the phosphorylation of PI3K, Akt, JNK, and p38 MAPK, pathways involved in the activation of cytosolic phospholipase A2 (cPLA 2) and thromboxane A2 (TXA2) production. These effects were accompanied by reduced TXA2 production and secretion of dense granules (ATP and serotonin). Chrysoeriol also impaired thrombin-induced clot retraction, further suggesting its capacity to regulate platelet responses and cytoskeletal rearrangements. These findings highlight chrysoeriol’s multi-target mechanisms, including modulation of cyclic nucleotides, kinase pathways, and platelet function, offering potential as a therapeutic agent to prevent thrombotic cardiovascular events.

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. Methods: In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. Results: The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). Conclusion Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

Postmortem computed tomography (PMCT) is used in forensic medicine worldwide due to its ability to non-invasively visualize injuries, hemorrhage, and estimate volume. In the autopsy of infants, assessing nutritional conditions such as weight is crucial for identifying neglect. This study aims to evaluate the usefulness of retrospectively estimating the weight of Korean infants using PMCT-based volume and multiplication factors, even when the body has been cremated. A total of 44 cases of infant death (under 12 months) were analyzed. PMCT images were obtained before autopsy. Autopsy records and documentation provided by the police at the time of autopsy were reviewed to determine the weight (g) of the infant. PMCT-based infant volumes (mL) were estimated using a three-dimensional semi-automatic segmentation method. Multiplication factors (g/mL) were calculated by dividing the weight recorded at autopsy by the PMCT-based volume, yielding a mean of 1.047 g/mL, ranging from 1.014 g/mL to 1.085 g/mL. The mean absolute error compared to weights recorded at autopsy was 95 g. Significant discrepancies were observed between weights recorded at the scene or medical center and those measured at autopsy. This study demonstrates that PMCT-based weight estimation for Korean infants is a reliable method and has the potential for retrospectively validating incorrect weight measurements and addressing inconsistencies in recorded weight data.

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.

Background: Exercise capacity is associated with lung function decline in chronicobstructive pulmonary disease (COPD) patients, but a discrepancy between exercisecapacity and airflow limitation exists. This study aimed to explore factors contributingto this discrepancy in COPD patients. Methods: Data for this prospective study were obtained from the Korean COPD SubgroupStudy. The exercise capacity and airflow limitation were assessed using the6-minute walk distance (6-MWD; m) and forced expiratory volume in 1 second (FEV1).Participants were divided into four groups: FEV1 >50%+6-MWD >350, FEV1 >50%+6-MWD ≤350, FEV1 ≤50%+6-MWD >350, and FEV1 ≤50%+6-MWD ≤350 and their clinicalcharacteristics were compared. Results: A total of 883 patients (male:female, 822:61; mean age, 68.3±7.97 years) wereenrolled. Among 591 patients with FEV1 >50%, 242 were in the 6-MWD ≤350 group, andamong 292 patients with FEV1 ≤50%, 185 were in the 6-MWD >350 group. The multipleregression analyses revealed that male sex (odds ratio [OR], 8.779; 95% confidence interval[CI], 1.539 to 50.087; p=0.014), current smoking status (OR, 0.355; 95% CI, 0.178to 0.709; p=0.003), and hemoglobin levels (OR, 1.332; 95% CI, 1.077 to 1.648; p=0.008)were significantly associated with discrepancies in exercise capacity and airflow limitationin patients with FEV1 >50%. Meanwhile, in patients with FEV1 ≤50%, diffusioncapacity of carbon monoxide (OR, 0.945; 95% CI, 0.912 to 0.979; p=0.002) was significantlyassociated with discrepancies between exercise capacity and airflow limitation. Conclusion The exercise capacity of COPD patients may be influenced by factors otherthan airflow limitation, so these aspects should be considered when assessing andtreating patients.

Background: and Purpose Early- and late-onset Alzheimer’s disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer’s disease (AD). Methods: The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging.Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls. Results: Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden. Conclusions The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Purpose: Alpha-fetoprotein (AFP) is a prognostic marker for hepatocellular carcinoma (HCC). We investigated the prognostic value of AFP levels in patients who achieved complete response (CR) to transarterial chemoembolization (TACE) for HCC. Materials and Methods: Between 2005 and 2018, 890 patients with HCC who achieved a CR to TACE were recruited. An AFP responder was defined as a patient who showed elevated levels of AFP (>10 ng/mL) during TACE, but showed normalization or a >50% reduction in AFP levels after achieving a CR. Results: Among the recruited patients, 569 (63.9%) with naïve HCC and 321 (36.1%) with recurrent HCC after complete resection were treated. Before TACE, 305 (34.3%) patients had multiple tumors, 219 (24.6%) had a maximal tumor size >3 cm, and 22 (2.5%) had portal vein tumor thrombosis. The median AFP level after achieving a CR was 6.36 ng/mL. After a CR, 473 (53.1%) patients experienced recurrence, and 417 (46.9%) died [median progression-free survival (PFS) and overall survival (OS) of 16.3 and 62.8 months, respectively]. High AFP levels at CR (>20 ng/mL) were independently associated with a shorter PFS [hazard ratio (HR)=1.403] and OS (HR=1.284), together with tumor multiplicity at TACE (HR=1.518 and 1.666, respectively). AFP non-responders at CR (76.2%, n=359 of 471) showed a shorter PFS (median 10.5 months vs. 15.5 months, HR=1.375) and OS (median 41.4 months vs. 61.8 months, HR=1.424) than AFP responders (all p=0.001). Conclusion High AFP levels and AFP non-responders were independently associated with poor outcomes after TACE. AFP holds clinical implications for detailed risk stratification upon achieving a CR after TACE.