Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge. METHODS AND RESULTS: We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty-two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time-independent and 16 time-dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow-up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points. CONCLUSIONS: Our deep learning-based model using real-world data could provide valid predictions of HF rehospitalization in 1 year follow-up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.

Transcranial direct current stimulation improves heart rate variability: A systematic review and meta-analysis.

BACKGROUND: Heart rate variability (HRV) is a useful tool for evaluating cardiovascular autonomic nervous system (ANS) functions. This systematic review and meta-analysis examined the potential effects of transcranial direct current stimulation (tDCS) protocols on HRV parameters. METHODS: This study acquired 97 comparisons from 24 qualified studies for data synthesis. Using standardized mean difference (SMD), individual and overall effect sizes were estimated to show differences in HRV variables between active tDCS and sham stimulation conditions. More positive effect size values indicated that active tDCS caused greater increases in HRV than sham stimulation. Furthermore, moderator variable analyses were performed to determine whether changes in HRV variables differed depending on (a) task types (physical stress versus psychological stress versus resting condition), (b) targeted brain regions, (c) stimulation polarity, (d) characteristics of participants, and (e) specific HRV variables. Finally, we used meta-regression analyses to determine whether different tDCS parameters (i.e., the number of tDCS sessions, stimulation duration, and density) were associated with changes in HRV patterns. RESULTS: The random-effects model meta-analysis showed that tDCS protocols significantly improved HRV variables (SMD = 0.400; P < 0.001). Moreover, for increasing HRV during the physical stress task (SMD = 1.352; P = 0.001), anodal stimulation on the M1 was effective, while combined polarity stimulation on the PFC improved HRV during the psychological stress task (SMD = 0.550; P < 0.001) and resting condition (SMD = 0.192; P = 0.012). Additional moderator variables and meta-regression analyses failed to show that tDCS protocols had positive effects in certain conditions, such as different stimulus polarity, characteristics of participants, specific HRV variables, and tDCS parameters. CONCLUSION: These findings tentatively suggest that using tDCS protocols to stimulate optimal targeted brain areas may be effective in improving HRV patterns potentially related to cardiovascular ANS functions.

KDM5A/B contribute to HIV-1 latent infection and survival of HIV-1 infected cells.

Combinational antiretroviral therapy (cART) suppresses human immunodeficiency virus type 1 (HIV-1) viral replication and pathogenesis in acquired immunodeficiency syndrome (AIDS) patients. However, HIV-1 remains in the latent stage of infection by suppressing viral transcription, which hinders an HIV-1 cure. One approach for an HIV-1 cure is the "shock and kill" strategy. The strategy focuses on reactivating latent HIV-1, inducing the viral cytopathic effect and facilitating the immune clearance for the elimination of latent HIV-1 reservoirs. Here, we reported that the H3K4 trimethylation (H3K4me3)-specific demethylase KDM5A/B play a role in suppressing HIV-1 Tat/LTR-mediated viral transcription in HIV-1 latent cells. Furthermore, we evaluated the potential of KDM5-specific inhibitor JQKD82 as an HIV-1 "shock and kill" agent. Our results showed that JQKD82 increases the H3K4me3 level at HIV-1 5' LTR promoter regions, HIV-1 reactivation, and the cytopathic effects in an HIV-1-latent T cell model. In addition, we identified that the combination of JQKD82 and AZD5582, a non-canonical NF-κB activator, generates a synergistic impact on inducing HIV-1 lytic reactivation and cell death in the T cell. The latency-reversing potency of the JQKD82 and AZD5582 pair was also confirmed in peripheral blood mononuclear cells (PBMCs) isolated from HIV-1 aviremic patients and in an HIV-1 latent monocyte. In latently infected microglia (HC69) of the brain, either deletion or inhibition of KDM5A/B results in a reversal of the HIV-1 latency. Overall, we concluded that KDM5A/B function as a host repressor of the HIV-1 lytic reactivation and thus promote the latency and the survival of HIV-1 infected reservoirs.

A Comparative Investigation of Functional Connectivity Utilizing Electroencephalography in Insomnia Patients with and without Restless Leg Syndrome

Objective: The current study aimed to identify distinctive functional brain connectivity characteristics that differentiate patients with restless legs syndrome (RLS) from those with primary insomnia. Methods: Quantitative electroencephalography (QEEG) was employed to analyze connectivity matrices using the phaselocking value technique. A total of 107 patients with RLS (RLS group) and 17 patients with insomnia without RLS (primary insomnia group) were included in the study. Demographic variables were compared using t tests and chi-square tests, while differences in connectivity were examined through multiple analyses of covariance. Correlation analysis was conducted to explore the relationship between connectivity and the severity of RLS. Results: The results indicated significant differences in the primary somatosensory cortex (F = 4.377, r = 0.039), primary visual cortex (F = 4.215, r = 0.042), and anterior prefrontal cortex (F = 5.439, r = 0.021) between the RLS and primary insomnia groups. Furthermore, the connectivity of the sensory cortex, including the primary somatosensory cortex (r = -0.247, p = 0.014), sensory association cortex (r = -0.238, p = 0.028), retrosplenial region (r = -0.302, p = 0.002), angular gyrus (r = -0.258, p = 0.008), supramarginal gyrus (r = -0.230, p = 0.020), primary visual cortex (r = -0.275, p = 0.005) and secondary visual cortex (r = -0.226, p = 0.025) exhibited an inverse association with RLS symptom severity. Conclusion: The prefrontal cortex, primary somatosensory cortex, and visual cortex showed potential as diagnostic biomarkers for distinguishing RLS from primary insomnia. These findings indicate that QEEG-based functional connectivity analysis shows promise as a valuable diagnostic tool for RLS and provides insights into its underlying mechanisms. Further research is needed to explore this aspect further.

Difference between Quantitative Electroencephalography, Loudness Dependence of Auditory Evoked Potential, and Mismatch Negativity between a Manic and a Depressive Episode in a Single Bipolar Patient with Mixed Features.

This study compares the changes in Quantitative electroencephalography (QEEG), loudness dependence of auditory evoked potentials (LDAEP), and mismatch negativity (MMN) in the case of bipolar depression, mania, and euthymia in a single patient. the characteristic of QEEG in this patient with mixed depression was an increase in alpha; in mixed mania, there was little increase in alpha, and the decrease in delta, theta, and beta was noticeable. LDAEP increased more in the manic phase than in the depressive phase. In contrast, MMN decreased more in the manic than in the depressive phase. After remission of mania, QEEG, LDAEP, and MMN were re-measured. Compared with the manic phase, the decrease in delta, theta, and beta bands in the occipital, temporal, and parietal lobes improved significantly. The LDAEP decreased from LDAEP 1.67 to 0.97. However, in spite of the euthymic phase, MMN amplitude showed a further decrease, from -1.7 to -0.9. In conclusion, using QEEG, LDAEP, and MMN can help clinicians predict a patient's bipolar state and evaluate serotonin intensity and cognitive function, enabling customized treatment. However, there are still few consistent research results; therefore, there is a need to utilize a larger sample size.

A blockchain-based healthcare data marketplace: prototype and demonstration.

Objectives: This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods: We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results: We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion: Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion: The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

Development of a dual immunochromatographic test strip to detect E2 and Erns antibodies against classical swine fever.

Background: It is essential to consider a practical antibody test to successfully implement marker vaccines and validate vaccination efficacy against classical swine fever virus (CSFV). The test should include a serological antibody assay, combined with a tool for differentiating infected from vaccinated animals (DIVA). The immunochromatographic test strip (ICS) has been exclusively designed for detecting CSFV E2 antibodies while lacking in detecting Erns antibodies, which can be employed and satisfy DIVA strategy. This study developed a novel ICS for detecting CSFV E2/Erns dual-antibody. The effectiveness of ICS in evaluating the DIVA capability of two novel chimeric pestivirus vaccine candidates was assessed. Methods: Recombinant E2 or Erns protein was transiently expressed in the plant benthamiana using Agrobacterium tumefaciens. ICS was subsequently assembled, and goat anti-rabbit IgG and recombinant CSFV E2 or Erns protein were plated onto the nitrocellulose membrane as control and test lines, respectively. The sensitivity and specificity of ICS were evaluated using sera with different neutralizing antibody titers or positive for antibodies against CSFV and other pestiviruses. The coincidence rates for detecting E2 and Erns antibodies between ICS and commercial enzyme-linked immunosorbent assay (ELISA) kits were also computed. ICS performance for DIVA capability was evaluated using sera from pigs vaccinated with conventional vaccine or chimeric vaccine candidates. Results: E2 and Erns proteins were successfully expressed in N. benthamiana-produced recombinant proteins. ICS demonstrated high sensitivity in identifying CSFV E2 and Erns antibodies, even at the low neutralizing antibody titers. No cross-reactivity with antibodies from other pestiviruses was confirmed using ICS. There were high agreement rates of 93.0 and 96.5% between ICS and two commercial ELISA kits for E2 antibody testing. ICS also achieved strong coincidence rates of 92.9 and 89.3% with two ELISA kits for Erns antibody detection. ICS confirmed the absence of CSFV Erns-specific antibodies in sera from pigs vaccinated with chimeric vaccine candidates. Conclusion: E2 and Erns proteins derived from the plant showed great potential and can be used to engineer a CSFV E2/Erns dual-antibody ICS. The ICS was also highly sensitive and specific for detecting CSFV E2 and Erns antibodies. Significantly, ICS can fulfill the DIVA concept by incorporating chimeric vaccine candidates.

Preventive effect of aripiprazole once-monthly on relapse into mood episodes in bipolar disorder: A multicenter, one-year, retrospective, mirror image study.

BACKGROUND: We conducted a one-year, retrospective, mirror-image study to investigate the clinical effectiveness and safety of aripiprazole once monthly (AOM) in patients with bipolar disorder (BD). We compared pre-treatment conditions with outcomes after 12 months of AOM treatment. METHODS: Seventy-five bipolar patients were recruited from 12 hospitals in Korea. We included 75 patients with BD who had received at least three AOM treatments from September 2019 to September 2022 and had accessible electronic medical record (EMRs) for the year before and after the baseline visit. RESULTS: The overall number of mood episodes significantly decreased from a mean of 1.5 ± 1.2 episodes pre-AOM to 0.5 ± 1.2 episodes post-AOM. Manic episodes significantly decreased from 0.8 ± 0.8 episodes pre-AOM to 0.2 ± 0.5 episodes post-AOM, and depressive episodes significantly decreased from 0.5 ± 0.8 episodes pre-AOM to 0.2 ± 0.6 episodes post-AOM (p = 0.017). Moreover, the number of psychiatric medications and pills and the proportion of patients treated with complex polypharmacy were significantly decreased post-AOM. LIMITATIONS: The small sample size was insufficient to fully represent the entire population of individuals with BD, and potential selection bias was introduced due to only including subjects who received AOM three or more times. CONCLUSION: The results of this study suggest that AOM can reduce mood episode relapse and may be clinically beneficial in the treatment of BD patients, potentially reducing issues associated with polypharmacy in some individuals.

Non-suicidal Self-injury and Quantified Electroencephalogram in Adolescents and Young Adults with Depression.

Objective: : This study investigated the association between non-suicidal self-injury (NSSI) and quantified electroencephalogram (QEEG) in patients with depression. We aimed to identify clinical features of NSSI and differences in QEEG findings. Methods: : This retrospective study used the medical records of 52 inpatients with major depressive episodes, aged from 15 to 30. The patients were categorized according to their history of NSSI. Their main diagnosis and sex were also considered. To evaluate clinical symptoms, self-reported scales were used. The absolute power and the Z-scores of various waves were included. Results: : NSSI was associated with suicidal ideations (p = 0.001) and trauma history (p = 0.014). In the binary logistic regression analysis, the Z-score of absolute alpha power was higher on the FP2 node (p = 0.029), lower on the F4 node (p = 0.029) in the NSSI group. The absolute high beta power in the NSSI group was higher on the FP2 and the F3 node, but lower on the F7 and F8 node. Patients with NSSI showed higher Z-score of the absolute delta power at the FP2 node (p = 0.044). The absolute gamma power was higher on the FP2 (p = 0.012) and the F3 node (0.043), lower on the FP1 (p = 0.019) and the F7 node (0.018) in the NSSI group. The absolute high gamma power at the FP2 (p = 0.017) and F8 nodes (p = 0.045) were higher in the NSSI group. Conclusion: : Patients with NSSI may have clinical features distinct from those of patients without NSSI. QEEG results have shown some differences, although it is less applicable due to some limitations.

Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study.

BACKGROUND: Many studies have evaluated stroke using claims data; most of these studies have defined ischemic stroke using an operational definition following the rule-based method. Rule-based methods tend to overestimate the number of patients with ischemic stroke. OBJECTIVES: We aimed to identify an appropriate algorithm for identifying stroke by applying machine learning (ML) techniques to analyze the claims data. METHODS: We obtained the data from the Korean National Health Insurance Service database, which is linked to the Ilsan Hospital database (n = 30,897). The performance of prediction models (extreme gradient boosting [XGBoost] or gated recurrent unit [GRU]) was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under precision-recall curve (AUPRC), and calibration curve. RESULTS: In total, 30,897 patients were enrolled in this study, 3145 of whom (10.18%) had ischemic stroke. XGBoost, a tree-based ML technique, had the AUROC was 94.46% and AUPRC was 92.80%. GRU showed the highest accuracy (99.81%), precision (99.92%) and recall (99.69%). CONCLUSIONS: We proposed recurrent neural network-based deep learning techniques to improve stroke phenotyping. This can be expected to produce rapid and more accurate results than the rule-based methods.

Clinical characteristics of open-angle glaucoma progression with peripapillary microvasculature dropout in different locations.

OBJECTIVE: The study attempted to identify clinical characteristics associated with structural progression in open-angle glaucoma (OAG) in the presence of MvD in different locations. METHODS: A total of 181 consecutive OAG eyes (follow-up 7.3 ± 4.0 years), which demonstrated peripapillary choroidal MvD (defined as a focal capillary loss with no visible microvascular network in choroidal layer) on optical coherence tomography (OCT) angiography (OCTA), were divided based on the location of MvD. Structural progression was determined using trend-based analysis of the Guided Progression Analysis software of Cirrus OCT. RESULTS: MvD was identified in the temporal quadrant in 110 eyes (temporal MvD; 60.5 ± 12.6 years), and in the inferior quadrant in 71 eyes (inferior MvD; 60.3 ± 11.1 years). After adjusting for age, average intraocular pressure (IOP) and baseline retinal nerve fibre layer (RNFL) thickness and visual field mean deviation, inferior MvD eyes showed faster rates of thinning in the inferior RNFL (mean (95% CI); -0.833 (-1.298 to -0.367)) compared to temporal MvD eyes (-0.144 (-0.496 to 0.207)) when long-term IOP fluctuation was larger than the median value (1.7 mmHg; P = 0.022). Long-term IOP fluctuations were independently associated with inferior RNFL thinning in eyes with inferior MvD (P = 0.002) but not in eyes with temporal MvD. CONCLUSIONS: In OAG eyes, the rates of RNFL and GCIPL thinning were comparable regardless of MvD locations. However, inferior MvD is associated with faster RNFL and GCIPL thinning in the same quadrant when long-term IOP fluctuation is present. Structural progression in the presence of temporal MvD was less associated with IOP fluctuation.

Relationship between Loudness Dependence of Auditory-evoked Potentials and Suicidality.

Objective: : Serotonin concentration is associated with suicide in patients with major depressive disorder. Loudness dependence of auditory-evoked potentials (LDAEPs), a representative neurophysiological indicator, is related to serotonin activity. Therefore, this study aimed to investigate the relationship between LDAEPs and suicidal ideation, suicide attempts, and the severity of depression. Methods: : We evaluated the scalp N1, P2, and N1/P2 LDAEPs along with standardized low-resolution brain electromagnetic tomography (sLORETA)-localized N1, P2, and N1/P2 LDAEPs of 221 patients with major depressive disorder. The demographic and clinical data of the patients, including data on suicidal ideation and previous suicide attempts, were obtained from clinical interviews and medical records. The severity of depression was assessed using the Beck Depression Inventory and Hamilton Depression Rating Scale, whereas suicidal ideation was evaluated using the Beck Scale for Suicidal Ideation (BSS). Results: : The total BSS score was associated with low N1/P2 LDAEP (p = 0.045), whereas P2 sLORETA-LDAEP was associated with lower previous suicide attempts (p = 0.043). In addition, suicide attempt was correlated with an elevated P2 left sLORETA-LDAEP (coefficient = 4.638, p = 0.038). Conclusion: : These findings suggest that suicidal ideation is associated with decreased LDAEP, whereas suicide attempt is associated with increased LDAEP.

Field evaluation of novel plant-derived porcine circovirus type 2 vaccine related to subclinical infection.

BACKGROUND: The objective of this field trial was to evaluate the efficacy of a new plant-based porcine circovirus type 2a (PCV2a) vaccine. This vaccine was a recombinant capsid subunit PCV2a vaccine based on the Nicotiana benthamiana expression system. METHODS: Three farms were selected for the study based on their history of subclinical PCV2 infection. A total of 40 18-day-old pigs were randomly allocated to either vaccinated or unvaccinated groups (20 pigs per group; 10 = male and 10 = female). Pigs received a 2.0-mL dose of the plant-based PCV2a vaccine intramuscularly at 21 days of age in accordance with the manufacturer's recommendations, whereas unvaccinated pigs were administered a single dose of phosphate buffered-saline at the same age. RESULTS: Vaccination had a positive effect on pig growth performance compared to that of unvaccinated pigs on all three of the farms. Vaccination of pigs with a plant-based PCV2a vaccine induced high levels of neutralizing antibodies titres against PCV2d and PCV2d-specific interferon-γ secreting cells which resulted in the reduction of PCV2d viral load and reduced lymphoid lesions severity. CONCLUSIONS: The results of this field trial demonstrated cross-protection of PCV2d by a plant-based PCV2a vaccine and a positive effect of pig growth performance with vaccination.

Models of vimentin organization under actin-driven transport.

Intermediate filaments form an essential structural network, spread throughout the cytoplasm, and play a key role in cell mechanics, intracellular organization, and molecular signaling. The maintenance of the network and its adaptation to the cell's dynamic behavior relies on several mechanisms implicating cytoskeletal crosstalk which are not fully understood. Mathematical modeling allows us to compare several biologically realistic scenarios to help us interpret experimental data. In this study we observe and model the dynamics of the vimentin intermediate filaments in single glial cells seeded on circular micropatterns following microtubule disruption by nocodazole treatment. In these conditions, the vimentin filaments move towards the cell center and accumulate before eventually reaching a steady state. In the absence of microtubule-driven transport, the motion of the vimentin network is primarily driven by actin-related mechanisms. To model these experimental findings, we hypothesize that vimentin may exist in two states, mobile and immobile, and switch between the states at unknown (either constant or nonconstant) rates. Mobile vimentin is assumed to advect with either constant or nonconstant velocity. We introduce several biologically realistic scenarios using this set of assumptions. For each scenario, we use differential evolution to find the best parameter sets resulting in a solution that most closely matches the experimental data and then the assumptions are evaluated using the Akaike information criterion. This modeling approach allows us to conclude that our experimental data are best explained by a spatially dependent trapping of intermediate filaments or a spatially dependent speed of actin-dependent transport.

Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer.

Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer.

Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.

Rifampin - Risperidone and Divalproex Drug-drug Interaction: A Case Report.

Rifampin is a potent hepatic cytochrome enzyme inducer, promoting the metabolism of many drugs. Here, we describe a case wherein rifampin-induced drug interactions affected the clinical improvement of a patient on psychiatric drugs for bipolar disorder. He was administered divalproex, risperidone, quetiapine, and clonazepam, along with anti-tuberculosis drugs HERZ, containing 600 mg rifampin. Despite taking 900 mg/day divalproex, his serum valproate levels were below 2 µg/ml, and his manic symptom persisted. Therefore, the antipsychotic risperidone (5 mg) was replaced with olanzapine (20 mg). Following this, his manic symptoms improved rapidly. Rifampin is a potent CYP3A and CYP2D6 inducer and is known to significantly reduce serum risperidone levels. Thus, even a high dose of risperidone did not induce a significant clinical effect, which was observed immediately after replacing with olanzapine. Therefore, drug interactions may have had a significant effect on clinical outcomes. Clinicians should be cognizant of drug interactions when treating psychiatric patients on rifampin therapy. The case has been sufficiently revised to protect the patient's personal information.

Zygote structure enables pluripotent shape-transforming deployable structure.

We propose an algorithmic framework of a pluripotent structure evolving from a simple compact structure into diverse complex 3D structures for designing the shape-transformable, reconfigurable, and deployable structures and robots. Our algorithmic approach suggests a way of transforming a compact structure consisting of uniform building blocks into a large, desired 3D shape. Analogous to a fertilized egg cell that can grow into a preprogrammed shape according to coded information, compactly stacked panels named the zygote structure can evolve into arbitrary 3D structures by programming their connection path. Our stacking algorithm obtains this coded sequence by inversely stacking the voxelized surface of the desired structure into a tree. Applying the connection path obtained by the stacking algorithm, the compactly stacked panels named the zygote structure can be deployed into diverse large 3D structures. We conceptually demonstrated our pluripotent evolving structure by energy-releasing commercial spring hinges and thermally actuated shape memory alloy hinges, respectively. We also show that the proposed concept enables the fabrication of large structures in a significantly smaller workspace.

The Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD): Changes in Preferred Treatment Strategies and Medications over 20 Years and Five Editions.

BACKGROUND: The Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) is an expert consensus guideline for depressive disorder created in 2002, and since then, four revisions (2006, 2012, 2017, 2021) have been published. In this study, changes in the content of the KMAP-DD survey and recommendations for each period were examined. METHODS: The development process of the KMAP-DD was composed of two stages. First, opinions from experts with abundant clinical experience were gathered through surveys. Next, a final guideline was prepared through discussion within the working committee regarding the suitability of the results with reference to recent clinical studies or other guidelines. RESULTS: In mild depressive symptoms, antidepressant (AD) monotherapy was preferred, but when severe depression or when psychotic features were present, a combination of AD and atypical antipsychotics (AD + AAP) was preferred. AD monotherapy was preferred in most clinical subtypes. AD monotherapy was preferred for mild depressive symptoms, and AD + AAP was preferred for severe depression and depression with psychotic features in children, adolescents, and the elderly. CONCLUSIONS: This study identified the changes in the KMAP-DD treatment strategies and drug preferences in each period over the past 20 years. This work is expected to aid clinicians in establishing effective treatment strategies.