Chemically Driven Clearance of Amyloid Aggregates by Polyfunctionalized Furo[2,3-b:4,5-b']dipyridine-Chalcone Hybrids to Ameliorate Memory in an Alzheimer Mouse Model.

The aberrant assembly of amyloid-ß (Aß) is implicated in Alzheimer's disease (AD). Recent clinical outcomes of Aß-targeted immunotherapy reinforce the notion that clearing Aß burden is a potential therapeutic approach for AD. Herein, to develop drug candidates for chemically driven clearance of Aß aggregates, we synthesized 51 novel polyfunctionalized furo[2,3-b:4,5-b']dipyridine-chalcone hybrid compounds. After conducting two types of cell-free anti-Aß functional assays, Aß aggregation prevention and Aß aggregate clearance, we selected YIAD-0336, (E)-8-((1H-pyrrol-2-yl)methylene)-10-(4-chlorophenyl)-2,4-dimethyl-7,8-dihydropyrido[3',2':4,5]furo[3,2-b]quinolin-9(6H)-one, for further in vivo investigations. As YIAD-0336 exhibited a low blood-brain barrier penetration profile, it was injected along with aggregated Aß directly into the intracerebroventricular region of ICR mice and ameliorated spatial memory in Y-maze tests. Next, YIAD-0336 was orally administered to 5XFAD transgenic mice with intravenous injections of mannitol, and YIAD-0336 significantly removed Aß plaques from the brains of 5XFAD mice. Collectively, YIAD-0336 dissociated toxic aggregates in the mouse brain and hence alleviated cognitive deterioration. Our findings indicate that chemically driven clearance of Aß aggregates is a promising therapeutic approach for AD.

Interfacial Stabilization by Prelithiated Trithiocyanuric Acid as an Organic Additive in Sulfide-based All-Solid-State Lithium Metal Batteries.

Sulfide-based all-solid-state battery (ASSB) with a lithium metal anode (LMA) is a promising candidate to surpass conventional Li-ion batteries owing to their inherent safety against fire hazards and potential to achieve a higher energy density. However, the narrow electrochemical stability window and chemical reactivity of the sulfide solid electrolyte towards the LMA results in interfacial degradation and poor electrochemical performance. In this direction, we introduce an organic additive approach, that is the mixing of prelithiated trithiocyanuric acid, Li3TCA, with Li6PS5Cl, to establish a stable interface while preserving high ionic conductivity. Including 2.5 wt% Li3TCA alleviates the decomposition of the electrolyte on the lithium metal interface, decreasing the Li2S content in the solid-electrolyte interface (SEI) thus forming a more stable interface. In Li|Li symmetric cells this enables a rise in the critical current density from 1.0 to 1.9 mA cm-2 and stable cycling for over 750 hours at a high current density of 1.0 mA cm-2. This approach also enables Li|NbO-NCM811 full cell to operate more than 500 cycles at 0.3C.

Comparative Analysis of the Psychosocial Symptoms and Experiences of Bereaved Parents and Parents of Children Who Survived the Sewol Ferry Accident After 5 Years: A Qualitative Interview Study.

BACKGROUND: This study aimed to investigate the psychosocial symptoms and experiences of bereaved parents of victims and parents of survivors of the Sewol Ferry accident five years after the accident. METHODS: In-depth interviews of 186 bereaved parents of victims or survivors of the Sewol Ferry accident were conducted. We elicited and categorized meaning units relevant to the psychological, cognitive, and physical traits of the participants from these interviews. Differences in responses between bereaved parents and survivors' parents and between genders were examined using frequency analyses and χ² tests. RESULTS: Data were organized under seven headings: observed attitude and impression of participants, difficulties due to mental health problems, difficulties due to physical pain, difficulties in relationships, negative changes following the incident, positive changes following the incident, and help needed. Within these headings, 27 themes, 60 sub-themes, and 80 meaning units were elicited. CONCLUSION: This study explored the psychiatric, physical, and relational problems reported by bereaved parents and those of survivors as well as major changes in their personal and social lives after the Sewol Ferry accident. Differences in responses according to gender were also identified. The results from this study could inform and facilitate the implementation of intervention measures, such as long-term psychological evaluation, to bereaved parents of victims or survivors of disasters.

Unsuccessful reduction of high-frequency alpha activity during cognitive activation in schizophrenia.

AIMS: Electroencephalogram (EEG) alpha activity during resting state reflects the 'readiness' of an individual to respond to the environment; this includes the performance of cognitive processes. Alpha activity is reported to be attenuated in schizophrenia (SCZ). Understanding the interaction between alpha activity during rest and when cognitively engaged may provide insights into the neural circuitry, which is dysfunctional in SCZ. This study investigated the changes of alpha activity between resting state and cognitive engagement in SCZ patients. METHODS: Thirty-four SCZ patients and 29 healthy controls (HC) were recruited. EEG was performed in the resting state and during an auditory P300 task. All experimental procedures followed the relevant institutional guidelines and regulations. RESULTS: In SCZ, high-frequency alpha activity was reduced in the resting state. High-frequency alpha source density was decreased in both the resting-state and a P300 task condition in patients, compared to HC. HC, but not SCZ patients, showed a reduction in high-frequency alpha source density during the P300 task compared to the resting state. The negative correlation between high-frequency alpha source density in the resting state and positive symptoms was significant. CONCLUSIONS: High-frequency alpha activity in SCZ patients and its unsuccessful reduction during cognitive processing may be biological markers of SCZ.

Linear and Nonlinear Dynamics of Heart Rate Variability are Correlated with Purpose in Life and Degree of Optimism in Anxiety Disorder Patients.

Although heart rate variability (HRV) may be a crucial marker of mental health, how it is related to positive psychological factors (i.e. attitude to life and positive thinking) is largely unknown. Here we investigated the correlation of HRV linear and nonlinear dynamics with psychological scales that measured degree of optimism and happiness in patients with anxiety disorders. Results showed that low- to high-frequency HRV ratio (LF/HF) was increased and the HRV HF parameter was decreased in subjects who were more optimistic and who felt happier in daily living. Nonlinear analysis also showed that HRV dispersion and regulation were significantly correlated with the subjects' optimism and purpose in life. Our findings showed that HRV properties might be related to degree of optimistic perspectives on life and suggests that HRV markers of autonomic nervous system function could reflect positive human mind states.

A Deep Learning Driven Simulation Analysis of the Emotional Profiles of Depression Based on Facial Expression Dynamics.

Objective: : Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods: : Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results: : Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). Conclusion: : This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.

Auditory imagery modulates frequency-specific areas in the human auditory cortex.

Neural responses in early sensory areas are influenced by top-down processing. In the visual system, early visual areas have been shown to actively participate in top-down processing based on their topographical properties. Although it has been suggested that the auditory cortex is involved in top-down control, functional evidence of topographic modulation is still lacking. Here, we show that mental auditory imagery for familiar melodies induces significant activation in the frequency-responsive areas of the primary auditory cortex (PAC). This activation is related to the characteristics of the imagery: when subjects were asked to imagine high-frequency melodies, we observed increased activation in the high- versus low-frequency response area; when the subjects were asked to imagine low-frequency melodies, the opposite was observed. Furthermore, we found that A1 is more closely related to the observed frequency-related modulation than R in tonotopic subfields of the PAC. Our findings suggest that top-down processing in the auditory cortex relies on a mechanism similar to that used in the perception of external auditory stimuli, which is comparable to early visual systems.

Rationally Designed Solution-Processible Conductive Carbon Additive Coating for Sulfide-based All-Solid-State Batteries.

Sulfide-based all-solid-state batteries (ASSBs) have emerged as promising candidates for next-generation energy storage systems owing to their superior safety and energy density. A conductive agent is necessarily added in the cathode composite of ASSBs to facilitate electron transport therein, but it causes the decomposition of the solid electrolyte and ultimately the shortening of lifetime. To resolve this dilemmatic situation, herein, we report a rationally designed solution-processible coating of zinc oxide (ZnO) onto vapor-grown carbon fiber as a conductive agent to reduce the contact between the carbon additive and the solid electrolyte and still maintain electron pathways to the active material. ASSBs with the carbon additive with an optimal coating of ZnO have markedly improved cycling performance and rate capability compared to those with the bare conductive agent, which can be attributed to hindering the decomposition of the solid electrolytes. The results highlight the usefulness of controlling the interparticle contacts in the composite cathodes in addressing the challenging interfacial degradation of sulfide-based ASSBs and improving their key electrochemical properties.

Increase in convective extreme El Niño events in a CO2 removal scenario.

Convective extreme El Niño (CEE) events, characterized by strong convective events in the eastern Pacific, are known to have a direct link to anomalous climate conditions worldwide, and it has been reported that CEE will occur more frequently under greenhouse warming. Here, using a set of CO2 ramp-up and ramp-down ensemble experiments, we show that frequency and maximum intensity of CEE events increase further in the ramp-down period from the ramp-up period. These changes in CEE are associated with the southward shift of the intertropical convergence zone and intensified nonlinear rainfall response to sea surface temperature change in the ramp-down period. The increasing frequency of CEE has substantial impacts on regional abnormal events and contributed considerably to regional mean climate changes to the CO2 forcings.

Effect of Home-based Self-administered Transcranial Direct Stimulation in Patients with Mild to Moderate Major Depressive Disorder: A Single-arm, Multicentral Trial.

Objective: Although the effects and safety of transcranial direct current stimulation (tDCS) treatment in depressive patients are largely investigated, whether the self-administration of tDCS treatment at patient's home is comparable to clinic-based treatment is still unknown. Methods: In this single-arm, multi-center clinical trial, 61 patients with mild to moderate major depressive disorder were enrolled. tDCS treatment was delivered at the patient's home once a day, 5 to 7 times a week for 6 weeks, and each session lasted for 30 minutes. The primary outcome was a total Beck-Depression Inventory-II score, and no concurrent antidepressants were used. Results: The remission rates in both Full-Analysis (FA) (n = 61) and Per-Protocol (PP) (n = 43) groups were statistically significant (FA: 57.4% [0.44-0.70], PP: 62.8% [0.47-0.77]; percent [95% confidence interval]). The degree of depression- related symptoms was also significantly improved in 2, 4, and 6 weeks after the treatment when compared with baseline. There was no significant association between treatment compliance and remission rate in both FA and PP groups. Conclusion: These results suggest that acute treatment of patient-administered tDCS might be effective in improving the subjective feeling of depressive symptoms in mild to moderate major depressive disorder patients.

Effects of Long-acting Injectable 3-Monthly Paliperidone Palmitate on the Clinical and Social Performance of Patients with Schizophrenia.

Objective: To investigate the effects of long-acting injectable 3-monthly paliperidone palmitate on the clinical and social functioning of patients with schizophrenia. Methods: This study enrolled patients with schizophrenia receiving long-acting injectable 1-monthly paliperidone palmitate for at least 4 months and who subsequently received 3-monthly paliperidone palmitate. Accordingly, 418 patients were followed up for 24 weeks. Their clinical symptoms and social functioning were measured using the Clinical Global Impression-Severity of Illness and Personal and Social Performance scales. Results: The Personal and Social Performance total score was significantly higher after 3-monthly paliperidone palmitate treatment than at baseline (baseline vs. week 24: 54.3 ± 18.0 vs. 61.0 ± 14.5 [mean ± standard deviation]; p < 0.001; Wilcoxon signed-rank test); the proportion of patients in the mildly ill group (scores 71-100) also increased significantly (baseline vs. week 24: 16.5% vs. 20.6%; p < 0.001; McNemar-Bowker test). The mean Clinical Global Impression-Severity of Illness score decreased significantly (baseline vs. week 24: 3.7 ± 1.0 vs. 3.4 ± 0.9; p < 0.001; Wilcoxon signed-rank test), as did the proportion of patients in the severely ill group (baseline vs. week 24: 4.1% vs. 2.1%; p < 0.001; McNemar-Bowker test). Conclusion: Continuous 3-monthly paliperidone palmitate treatment significantly enhances the personal and social performance of patients with schizophrenia and reduces the proportion of those with severe illness. These findings suggest that long-acting injectable antipsychotic administration at intervals longer than 1 month might improve the social functioning of and promote return to activities of daily living in patients with schizophrenia.

Development of depression detection algorithm using text scripts of routine psychiatric interview.

Background: A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients. Methods: Seventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject's representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts. Results: A machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001). Conclusion: This is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons.

Disturbances of motivational balance in chronic schizophrenia during decision-making tasks.

AIM: The role of feedback processing in decision-making has been assessed in psychiatric patients using the Iowa Gambling Task (IGT). Although impaired performance on the IGT has been documented extensively in schizophrenia patients, the neuropsychological mechanisms underlying the performance deficits have not yet been elucidated. Therefore, the aim of this study was to investigate the neuropsychological origins of impaired decision-making in schizophrenia patients using various versions of the IGT. METHODS: Thirty chronic schizophrenia patients and 33 healthy subjects underwent computerized versions of the IGT, the Variant Gambling Task (VGT), and the Shuffled Gambling Task (SGT) to assess the contributions of motivational balance and reversal learning on IGT performance. In addition, performance on the Wisconsin Card-Sorting Test (WCST) was assessed. RESULTS: The schizophrenia patients exhibited deficits on the IGT and SGT, particularly in later trials. No significant group difference was detected on the VGT due to the improved performance of schizophrenia patients in the earlier trials. Performance on the gambling tasks in the schizophrenia group did not correlate with performance on the WCST or with the severity of clinical symptoms. CONCLUSION: Deficits in motivational balance, but not reversal learning, play a dominant role in the impaired decision-making of patients with schizophrenia.

Decreased Health-Seeking Behaviors in People With Depressive Symptoms: The Korea National Health and Nutrition Examination Survey 2014, 2016, and 2018.

OBJECTIVE: The purpose of this study was to investigate the effects of depressive symptoms on health-seeking behaviors using the large epidemiological study data of the Korea National Health and Nutrition Examination (KNHANES). METHODS: Data from the Korea National Health and Nutrition Examination Survey (KNHANES), which is a large-scale national survey, were used in this study. The Patient Health Questionnaire-9 (PHQ-9) was used to assess the depressive state of the participants. Specialized self-reported questionnaires that included questions about health-seeking behaviors were also performed. To examine the relationships between depression and health-seeking behaviors, complex sample logistic regression models with control for covariates were used. RESULTS: There was a significant association between decreased health-seeking behaviors and depressive symptoms in adults (odds ratio [OR]: 3.11, 95% confidence interval [CI]: 2.44-3.96). The association was found to be especially strong in males (OR: 2.63, 95% CI: 1.69-4.10) versus in females (OR: 2.49, 95% CI: 1.90-3.27). With regard to age group, younger adults (19-44 years of age) showed the highest OR (OR: 3.07, 95% CI: 2.12-4.45). CONCLUSION: Our findings support the idea that there is a significant association between health-seeking behaviors and depressive symptoms in the Korean population. These results suggest that individuals with decreased health-seeking behaviors could be evaluated for depressive symptoms.

Effect of Self-administered Transcranial Direct Stimulation in Patients with Major Depressive Disorder: A Randomized, Single-blinded Clinical Trial.

OBJECTIVE: In numerous studies that have addressed transcranial direct current stimulation (tDCS) devices, participants visit the hospital regularly and undergo stimulation directed by health professionals. This method has the advantage of being able to deliver accurate stimuli in a controlled environment, but it does not adopt the merits of tDCS portability and applicability. Thus, it may be necessary to investigate how self-administered tDCS treatment at home affects depression- related symptoms. METHODS: In this randomized, single-blinded clinical trial, 58 patients with major depressive disorder were assigned to active and sham tDCS stimulation groups, and treatment responses were evaluated biweekly over six weeks. Both active and sham tDCS treatment group were treated with escitalopram. All participants were instructed the protocol and usage of at-home tDCS device, and self-administered tDCS treatment at their home. RESULTS: The beck-depression inventory score decreased significantly as treatment progressed, and the degree of symptom improvement was significantly higher in the active group than in the sham tDCS group. There were no significant differences between the two groups in other indices, including the Hamilton Depression Scale. CONCLUSION: These results suggest that patient-administered tDCS treatment might be effective in improving subjective symptoms of depression.

Room-Temperature Anode-Less All-Solid-State Batteries via the Conversion Reaction of Metal Fluorides.

All-solid-state batteries (ASSBs) that employ anode-less electrodes have drawn attention from across the battery community because they offer competitive energy densities and a markedly improved cycle life. Nevertheless, the composite matrices of anode-less electrodes impose a substantial barrier for lithium-ion diffusion and inhibit operation at room temperature. To overcome this drawback, here, the conversion reaction of metal fluorides is exploited because metallic nanodomains formed during this reaction induce an alloying reaction with lithium ions for uniform and sustainable lithium (de)plating. Lithium fluoride (LiF), another product of the conversion reaction, prevents the agglomeration of the metallic nanodomains and also protects the electrode from fatal lithium dendrite growth. A systematic analysis identifies silver (I) fluoride (AgF) as the most suitable metal fluoride because the silver nanodomains can accommodate the solid-solution mechanism with a low nucleation overpotential. AgF-based full cells attain reliable cycling at 25 °C even with an exceptionally high areal capacity of 9.7 mAh cm-2 (areal loading of LiNi0.8 Co0.1 Mn0.1 O2  = 50 mg cm-2 ). These results offer useful insights into designing materials for anode-less electrodes for sulfide-based ASSBs.

Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms.

Objective: Predicting prognosis of in-hospital patients is critical. However, it is challenging to accurately predict the life and death of certain patients at certain period. To determine whether machine learning algorithms could predict in-hospital death of critically ill patients with considerable accuracy and identify factors contributing to the prediction power. Materials and Methods: Using medical data of 1,384 patients admitted to the Surgical Intensive Care Unit (SICU) of our institution, we investigated whether machine learning algorithms could predict in-hospital death using demographic, laboratory, and other disease-related variables, and compared predictions using three different algorithmic methods. The outcome measurement was the incidence of unexpected postoperative mortality which was defined as mortality without pre-existing not-for-resuscitation order that occurred within 30 days of the surgery or within the same hospital stay as the surgery. Results: Machine learning algorithms trained with 43 variables successfully classified dead and live patients with very high accuracy. Most notably, the decision tree showed the higher classification results (Area Under the Receiver Operating Curve, AUC = 0.96) than the neural network classifier (AUC = 0.80). Further analysis provided the insight that serum albumin concentration, total prenatal nutritional intake, and peak dose of dopamine drug played an important role in predicting the mortality of SICU patients. Conclusion: Our results suggest that machine learning algorithms, especially the decision tree method, can provide information on structured and explainable decision flow and accurately predict hospital mortality in SICU hospitalized patients.

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES.

Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data. Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers. Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively. Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.

Decreased cardiovascular death in schizophrenia patients treated with antipsychotics: A Korean national cohort study.

BACKGROUND: Patients with schizophrenia have a reduced life expectancy, but the association between antipsychotic usage and cause of death is uncertain. METHODS: The authors observed associations of antipsychotic usage with the mortality rate and cause of death in a population-based cohort of the Korean National Health Insurance Service database from 2003 to 2017. A total of 86,923 patients with schizophrenia were categorized by the total duration of antipsychotic prescription after schizophrenia diagnosis into treated (n = 77,139) and untreated (n = 9784) groups. The main outcome was all-cause mortality; causes of death included cardiovascular disease, pulmonary disease, diabetes, cancer, accident, suicide and homicide. RESULTS: The numbers of all-cause deaths and deaths from individual causes were significantly lower in the antipsychotic-treated group than in the untreated group (all cases, p < 10-4). When adjusted for covariates (age, sex, income, body mass index, alcohol consumption, hypertension, cancer and cerebral stroke), mortality rates due to ischemic heart disease (hazard ratio, HR, 0.38 [95% CI, 0.18-0.77]) and stroke (HR, 0.39 [95% CI, 0.19-0.80]) were significantly lower in the antipsychotic-treated group. Among 4 atypical antipsychotics (olanzapine, risperidone, aripiprazole and quetiapine), only aripiprazole was associated with a decreased mortality risk relative to olanzapine (HR, 0.55 [95% CI, 0.32-0.96]). CONCLUSIONS: Schizophrenia patients constantly prescribed antipsychotics had significantly lower rates of death from certain cardiovascular illnesses than untreated patients. Aripiprazole-treated schizophrenia was associated with a decreased risk of death compared with olanzapine-treated disease.