The cause and manner of death in the Busan and Gyeongnam regions were analyzed using autopsy data performed by the National Forensic Service Busan Institute (NFS BI) for 10 years—from 2014 to 2023. In addition, changes in the number of autopsy cases of elderly individuals aged 65 and older, were analyzed in the Busan and Gyeongnam regions. A total of 6,374 cases were classified, excluding autopsies from the Ulsan area and the Coast Guard, from the NFS BI data. Analysis of the manner of death revealed that 3,203 cases (50.3%) were unnatural deaths; 2,031 cases (31.9%) were natural deaths; and 1,140 cases (17.9%) were deaths of unknown cause. Among the unnatural deaths, accidents were the most common at 1,149 cases (18.0%), followed by suicide at 979 cases (15.4%); and homicide at 583 cases (9.1%). Among natural deaths, heart disease was the most common with 764 cases (37.6%), followed by vascular disease with 351 cases (17.3%). The proportion of the population aged 65 or older in Busan and Gyeongnam has been steadily increasing from 13.7% in 2014 to 21.6% in 2023. Accordingly, the number of autopsies on people aged 65 or older has increased from 72 in 2014 to 174 in 2023, and the number of autopsies on people aged 65 or older accounted for one-quarter of the total number of forensic autopsies commissioned by Busan/Gyeongnam, and performed by NFS BI in 2023. Therefore, we plan to introduce emerging issues relating to population aging and geriatric forensic medicine.

The cause and manner of death in the Busan and Gyeongnam regions were analyzed using autopsy data performed by the National Forensic Service Busan Institute (NFS BI) for 10 years—from 2014 to 2023. In addition, changes in the number of autopsy cases of elderly individuals aged 65 and older, were analyzed in the Busan and Gyeongnam regions. A total of 6,374 cases were classified, excluding autopsies from the Ulsan area and the Coast Guard, from the NFS BI data. Analysis of the manner of death revealed that 3,203 cases (50.3%) were unnatural deaths; 2,031 cases (31.9%) were natural deaths; and 1,140 cases (17.9%) were deaths of unknown cause. Among the unnatural deaths, accidents were the most common at 1,149 cases (18.0%), followed by suicide at 979 cases (15.4%); and homicide at 583 cases (9.1%). Among natural deaths, heart disease was the most common with 764 cases (37.6%), followed by vascular disease with 351 cases (17.3%). The proportion of the population aged 65 or older in Busan and Gyeongnam has been steadily increasing from 13.7% in 2014 to 21.6% in 2023. Accordingly, the number of autopsies on people aged 65 or older has increased from 72 in 2014 to 174 in 2023, and the number of autopsies on people aged 65 or older accounted for one-quarter of the total number of forensic autopsies commissioned by Busan/Gyeongnam, and performed by NFS BI in 2023. Therefore, we plan to introduce emerging issues relating to population aging and geriatric forensic medicine.

The cause and manner of death in the Busan and Gyeongnam regions were analyzed using autopsy data performed by the National Forensic Service Busan Institute (NFS BI) for 10 years—from 2014 to 2023. In addition, changes in the number of autopsy cases of elderly individuals aged 65 and older, were analyzed in the Busan and Gyeongnam regions. A total of 6,374 cases were classified, excluding autopsies from the Ulsan area and the Coast Guard, from the NFS BI data. Analysis of the manner of death revealed that 3,203 cases (50.3%) were unnatural deaths; 2,031 cases (31.9%) were natural deaths; and 1,140 cases (17.9%) were deaths of unknown cause. Among the unnatural deaths, accidents were the most common at 1,149 cases (18.0%), followed by suicide at 979 cases (15.4%); and homicide at 583 cases (9.1%). Among natural deaths, heart disease was the most common with 764 cases (37.6%), followed by vascular disease with 351 cases (17.3%). The proportion of the population aged 65 or older in Busan and Gyeongnam has been steadily increasing from 13.7% in 2014 to 21.6% in 2023. Accordingly, the number of autopsies on people aged 65 or older has increased from 72 in 2014 to 174 in 2023, and the number of autopsies on people aged 65 or older accounted for one-quarter of the total number of forensic autopsies commissioned by Busan/Gyeongnam, and performed by NFS BI in 2023. Therefore, we plan to introduce emerging issues relating to population aging and geriatric forensic medicine.

The cause and manner of death in the Busan and Gyeongnam regions were analyzed using autopsy data performed by the National Forensic Service Busan Institute (NFS BI) for 10 years—from 2014 to 2023. In addition, changes in the number of autopsy cases of elderly individuals aged 65 and older, were analyzed in the Busan and Gyeongnam regions. A total of 6,374 cases were classified, excluding autopsies from the Ulsan area and the Coast Guard, from the NFS BI data. Analysis of the manner of death revealed that 3,203 cases (50.3%) were unnatural deaths; 2,031 cases (31.9%) were natural deaths; and 1,140 cases (17.9%) were deaths of unknown cause. Among the unnatural deaths, accidents were the most common at 1,149 cases (18.0%), followed by suicide at 979 cases (15.4%); and homicide at 583 cases (9.1%). Among natural deaths, heart disease was the most common with 764 cases (37.6%), followed by vascular disease with 351 cases (17.3%). The proportion of the population aged 65 or older in Busan and Gyeongnam has been steadily increasing from 13.7% in 2014 to 21.6% in 2023. Accordingly, the number of autopsies on people aged 65 or older has increased from 72 in 2014 to 174 in 2023, and the number of autopsies on people aged 65 or older accounted for one-quarter of the total number of forensic autopsies commissioned by Busan/Gyeongnam, and performed by NFS BI in 2023. Therefore, we plan to introduce emerging issues relating to population aging and geriatric forensic medicine.

Background: Patients with end-stage renal disease (ESRD) who are on hemodialysis (HD) have reduced vascular com‑ pliance and are likely to develop heart failure (HF). In this study, we estimated the prevalence of HF pre- and post-HD in ESRD using the current guidelines. Methods: We prospectively investigated HF in ESRD patients on HD using echocardiography pre- and post-HD. We used the structural and functional abnormality criteria of the 2021 European Society of Cardiology guidelines. Results: A total of 54 patients were enrolled. The mean age was 62.6 years, and 40.1% were male. Forty-five patients (83.3%) had hypertension, 28 (51.9%) had diabetes, and 20 (37.0%) had ischemic heart disease. The mean N-terminalpro brain natriuretic peptide BNP (NT-proBNP) level was 12,388.8 ± 2,592.2 pg/dL. The mean ideal body weight was 59.3 kg, mean hemodialysis time was 237.4 min, and mean real filtration was 2.8 kg. The mean left ventricular ejection fraction (LVEF) was 62.4%, and mean left ventricular end-diastolic diameter was 52.0 mm in pre-HD. Post-HD echocardiography showed significantly lower left atrial volume index (33.3 ± 15.9 vs. 40.6 ± 17.1, p = 0.030), tricuspid regurgitation jet V (2.5 ± 0.4 vs. 2.8 ± 0.4 m/s, p < 0.001), and right ventricular systolic pressure (32.1 ± 10.3 vs. 38.4 ± 11.6, p = 0.005) compared with pre-HD. There were no differences in LVEF, E/E′ ratio, or left ventricular global longitudinal strain. A total of 88.9% of pre-HD patients and 66.7% of post-HD patients had either structural or functional abnor‑ malities in echocardiographic parameters according to recent HF guidelines (p = 0.007). Conclusions Our data showed that the majority of patients undergoing hemodialysis satisfy the diagnostic criteria for HF according to current HF guidelines. Pre-HD patients had a 22.2% higher incidence in the prevalence of func‑ tional or structural abnormalities as compared with post-HD 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.

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.

When death occurs, the supply of adenosine triphosphate through respiration ceases, and rigor mortis begins approximately 20 minutes after death. The underlying mechanisms of rigor mortis and cadaveric spasm are assumed to be similar. However, unlike rigor mortis, cadaveric spasm is a very rare phenomenon in which muscle stiffness develops almost immediately after death. Herein we describe a 27-year-old male with suspected cadaveric spasm. A forensic pathologist concluded that the cause of death was a head injury due to a fall. When the body was discovered, a cigarette remained in the mouth, suggesting a cadaveric spasm. Some opinions deny the existence of cadaveric spasm because there is no precise pathophysiological mechanism to support it. Cadaveric spasm could not be confirmed in the present case; however, while the complete mechanism is unclear, as in this case, it is sometimes difficult to rule out the presence of cadaveric spasm in forensic investigations. Therefore, a comprehensive forensic examination is necessary, and forensic examiners should be cautious.

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.