Objectives: The purpose of this study was to compare the demographic, clinical, and criminal characteristics and legal judgments of criminal cases, in which psychiatric evaluation was requested, and to identify factors affecting criminal responsibility judgments. Methods: The medical records of 100 subjects that committed crimes and the judgment records of 51 subjects admitted to Yongin Mental Hospital from February 2005 to February 2021 were analyzed retrospectively. Psychiatric disorder groups were compared with respect to demographic, clinical, criminal characteristics, and legal judgments. Factors affecting criminal responsibility judgments were identified by comparing legal judgment groups. Results: Psychiatric history, criminal motivation, and criminal history differed significantly between psychiatric disorder groups. Differences between the opinions of the appraiser and courts regarding criminal responsibility were significantly dependent on psychiatric disorder type, and there was a consistent tendency according to the psychiatric disorders, but there was a difference of opinion between the two groups. Conclusion The findings of the current study suggest psychiatric diagnoses and symptoms such as psychotic symptoms and cognitive impairments that provoke crime can influence criminal responsibility judgments.

Objective: This study aimed to evaluate the endometrial transcriptomic patterns in the early secretory phase (ESP) and mid-secretory phase (MSP) of the natural menstrual cycle before in vitro fertilization and embryo transfer (IVF-ET). Methods: Thirty patients whose endometrial tissues were obtained from the ESP or MSP of a natural menstrual cycle immediately before IVF-ET were included. Endometrial dating was histologically confirmed as ESP (cycle days 16-18) or MSP (cycle days 19-21), according to the noyes criteria. The patients were divided into two groups depending on the IVF-ET outcome: pregnant (n=14; 7 in ESP and 7 in MSP) or non-pregnant (n=16; 8 in ESP and 8 in MSP). Differentially expressed genes (DEGs) in the MSP, compared to the ESP, were identified using NanoString nCounter (NanoString Technologies, Seattle, WA, USA) data for both the pregnant and non-pregnant groups. Results: Thirteen DEGs in the pregnant group and 11 DEGs in the non-pregnant group were identified in the MSP compared to those in the ESP. In both groups, adrenoceptor alpha 2A, interleukin 1 receptor-associated kinase 2, a disintegrin and metalloproteinase with thrombospondin repeats 15 (ADAMTS15), serpin family E member 1, integrin subunit beta 3, transmembrane protein 252 (TMEM252), huntingtin associated protein 1, C2 calcium-dependent domain containing 4A, and integrin subunit alpha 2 were upregulated in the MSP, compared to the ESP. TMEM37, galactosidase beta 1 like 2, Rho family GTPase 3, and cytochrome P450 family 24 subfamily A member 1 were upregulated in the MSP only in the pregnant group. ADAMTS8 was downregulated and monoamine oxidase A was upregulated in the MSP only in the non-pregnant group. Conclusion Transcriptomic patterns in the endometrium immediately before IVF-ET appear to differ according to the IVF-ET outcome. These novel DEGs, which have not been previously studied, may have functional significance during the window of implantation and serve as potential biomarkers of endometrial receptivity.

Background: Pyruvate dehydrogenase complex (PDHC) deficiency is a rare mitochondrial disorder caused by a genetic mutation affecting the activity of the PDHC enzyme, which plays a major role in the tricarboxylic cycle. Few cases of surgery or anesthesia have been reported. Moreover, there is no recommended anesthetic method.Case: A 24-month-old child with a PDHC deficiency presented to the emergency room with respiratory failure, mental decline, systemic cyanosis, and lactic acidosis. During hospitalization period, the patient presented with pneumothorax, pneumoperitoneum, and multiple air pockets in the heart. Two surgeries were performed under general anesthesia using an inhalational anesthetic agent. The patient was discharged with home ventilation. Conclusions Anesthesiologists should be wary of multiple factors when administering anesthesia to patients with PDHC deficiency, including airway abnormalities, acid-base imbalance, intraoperative fluid management, selection of appropriate anesthetics, and monitoring of lactic acid levels.

Background: The thoracic paravertebral block is an effective analgesic technique for postoperative pain management after breast surgery. The ultrasound-guided retrolaminar block (RLB) is a safer alternative to conventional paravertebral block.Thus, we assessed the analgesic efficacy of ultrasound-guided RLB for postoperative pain management after breast surgery. Methods: Patients requiring breast surgery were randomly allocated to group C (retrolaminar injection with saline) and group R (RLB with local anesthetic mixture).The RLB was performed at the level of T3 with local anesthetic mixture (0.75% ropivacaine 20 mL + 2% lidocaine 10 mL) under general anesthesia before the skin incision. The primary outcome was cumulative morphine consumption using intravenous patient-controlled analgesia (IV-PCA) at 24 hour postoperatively. The secondary outcomes were the visual analogue scale (VAS) scores at 1, 6, 24, and 48 hour postoperatively and the occurrence of adverse events and patient satisfaction after the surgery. Results: Forty-six patients were included, 24 in group C and 22 in group R. The cumulative morphine consumption using IV-PCA did not differ between the two groups (P = 0.631). The intraoperative use of remifentanil was higher in group C than in group R (P = 0.025). The resting and coughing VAS scores at 1 hour postoperatively were higher in group R than in group C (P = 0.011, P= 0.004). The incidence of adverse events and patient satisfaction was not significantly different between the two groups. Conclusions A single injection of ultrasound-guided RLB did not reduce postoperative analgesic requirements following breast surgery.

Objectives: This study examined post-disaster mental health problems and related public perception of disaster-related mental health services. The differences of these perceptions according to the disaster experience and disaster type were also investigated. Methods: Data were collected via telephone and online surveys, and information from 2928 respondents was analyzed. The participants were allocated across age, sex, and residence area. Results: Those who had experienced disasters showed a more negative perception of post-disaster mental health services than those who had not. While natural disaster survivors most often reported financial problems as secondary stressors after a disaster, social disaster survivors were more likely to report mental health problems. Regarding national mental health support for disaster, disaster-experiencing respondents more often tended to prefer mental health services than non-disaster-experiencing respondents. Conclusion The current study can help understand the public perception of disaster-related mental health and the needs of mental health services. These findings could suggest directions and grounds for policies of a national support system for disaster-related mental health.

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.