Relationship between continuous EEG monitoring findings and prognostic factors in patients with status epilepticus.

BACKGROUND AND PURPOSE: Little information is available regarding the use of continuous electroencephalography (cEEG) monitoring findings to predict the prognosis of patients with status epilepticus, which could aid in prognostication. This study investigated the relationship between cEEG monitoring findings and various prognostic indicators in patients with status epilepticus. METHODS: We reviewed the clinical profiles and cEEG monitoring data of 28 patients with status epilepticus over a ten-year period. Patient demographics, etiology, EEG features, duration of hospital stay, number of antiseizure medications, and outcome measures were analyzed. Functional outcomes were assessed using the modified Rankin Scale (mRS), which evaluates the degree of daily living impairment and dependence on others resulting from neurological injury. RESULTS: Patients exhibiting electrographic status epilepticus (ESE) demonstrated significantly longer duration of status epilepticus (77.75 ± 58.25 vs. 39.86 ± 29.81 h, p = 0.024) and total length of hospital stay (13.00 ± 6.14 vs. 8.14 ± 5.66 days, p = 0.038) when compared to those with ictal-interictal continuum (IIC). Individuals who displayed any increase in modified Rankin Scale (mRS) score between their premorbid state and discharge also had significantly longer duration of status epilepticus (74.09 ± 34.94 vs. 51.56 ± 54.25 h, p = 0.041) and total length of hospital stay (15.89 ± 6.05 vs. 8.05 ± 4.80 days, p = 0.004) when compared to those who showed no difference. The most prevalent etiology of status epilepticus in our study was chronic structural brain lesions. CONCLUSIONS: This suggests that ESE may serve as a predictor of prolonged duration of status epilepticus and increased hospitalization among patients with status epilepticus.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model's performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs. 0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

Reliability and Validity of a Tablet-Based Neuropsychological Test (the Hellocog) for Screening Dementia.

OBJECTIVE: To address the gap in timely diagnosis of dementia due to limited screening tools, we investigated the validity and reliability of the Hellocog, computerized neuropsychological test based on tablets for screening dementia. The higher the probability score on the Hellocog, the higher the likelihood of dementia. METHODS: This study included 100 patients with dementia and 100 individuals with normal cognition who were aged 60 years or older and free of other major psychiatric, neurological, or medical conditions. They administered the Hellocog on a tablet under the supervision of a neuropsychologist. To determine test-retest reliability, 20 took the Hellocog again after 4 weeks. Diagnostic performance was assessed using the receiver operator characteristics (ROC) analysis. RESULTS: The Hellocog showed adequate internal consistency (Cronbach's alpha=0.69) and good test-retest reliability (intraclass correlation coefficient=0.86, p<0.001). Participants with dementia scored higher on the Hellocog than those with normal cognition (p<0.001), confirming its high criterion validity. Strong correlations with the Mini-Mental Status Examination (MMSE) score and the total score of the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery (CERAD-TS) highlight the concurrent validity of the Hellocog. The area under the ROC curve for dementia of the Hellocog was excellent (0.971) and comparable to that of the MMSE and CERAD-TS. The sensitivity and specificity for dementia were 0.945 and 0.872%, respectively, which were slightly better than those of the MMSE and CERAD-TS. CONCLUSION: Hellocog stands out as a valid and reliable tool for self-administered dementia screening, with promise for improving early detection of dementia.

Validity of the BOT-2 Short Form for Korean School-Age Children: A Preliminary Study.

The Bruininks-Oseretsky Test of Motor Proficiency Second Edition (BOT-2) is the most common motor assessment in Korea. The BOT-2-Short Form (SF) is preferred over the complete form (CF) in settings with limited time. The present study aimed to assess the validity of the BOT-2 SF in Korean school-age children. First, we verified that the BOT-2 SF reflects developmental changes in motor skills. Second, we compared the BOT-2 SF scores to those of the BOT-2 CF. A total of 283 Korean school-age children performed the BOT-2. The differences in the BOT-2 SF point according to age group (7 years, 8-9 years, and 10-12 years) were analyzed. A correlation analysis of the standard scores between the BOT-2 SF and CF was conducted. The sensitivity and specificity of the BOT-2 SF were calculated in reference to its CF. Overall, the BOT-2 SF point scores increased with age. The correlation between the total scores of the BOT-2 SF and CF was strong. The BOT-2 SF had a sensitivity of 83% and specificity of 92%. This study has demonstrated the validity of the BOT-2 SF in Korean school-age children. The BOT2 SF can be useful in screening Korean school-age children with motor skills problems.

Assessment of the occurrence of the second generation of Mythimna loreyi Duponchel (Lepidoptera: Noctuidae) using temperature-dependent developmental and oviposition models.

A significant crop pest, Mythimna loreyi, migrates annually to Korea and has been frequently observed in rice and corn fields. However, the phenology of this pest, particularly in relation to its ecological interactions and host crop seasons in Korea, remains poorly understood. This study aims to clarify the timing of the second generation of M. loreyi in Korea to enhance pest management strategies. To achieve this, we developed temperature-dependent models for developmental and ovipositional rates, studying these processes across five constant temperatures (15, 20, 25, 30, and 35°C). Our models, which showed a high correlation with observed data (r2 ≥ 0.93), include a theoretical approach that combines the developmental variation of immatures with the necessary degree-days for 50% egg laying and complete egg development. These predictions allow for the forecasting of the second generation's occurrence, with relatively small deviations (one to three days) observed at two different field sites. The insights from this study are critical for both understanding the ecology of M. loreyi and for informing practical management decisions, such as optimal placement of barriers to prevent immigration and strategies for controlling local populations.

A novel APP splice variant-dependent marker system to precisely demarcate maturity in SH-SY5Y cell-derived neurons.

SH-SY5Y, a neuroblastoma cell line, can be converted into mature neuronal phenotypes, characterized by the expression of mature neuronal and neurotransmitter markers. However, the mature phenotypes described across multiple studies appear inconsistent. As this cell line expresses common neuronal markers after a simple induction, there is a high chance of misinterpreting its maturity. Therefore, sole reliance on common neuronal markers is presumably inadequate. The Alzheimer's disease (AD) central gene, amyloid precursor protein (APP), has shown contrasting transcript variant dynamics in various cell types. We differentiated SH-SY5Y cells into mature neuron-like cells using a concise protocol and observed the upregulation of total APP throughout differentiation. However, APP transcript variant-1 was upregulated only during the early to middle stages of differentiation and declined in later stages. We identified the maturity state where this post-transcriptional shift occurs, terming it "true maturity." At this stage, we observed a predominant expression of mature neuronal and cholinergic markers, along with a distinct APP variant pattern. Our findings emphasize the necessity of using a differentiation state-sensitive marker system to precisely characterize SH-SY5Y differentiation. Moreover, this study offers an APP-guided, alternative neuronal marker system to enhance the accuracy of the conventional markers.

Clinical Characteristics and Treatment Outcomes of Pulmonary Diseases Caused by Coinfections With Multiple Nontuberculous Mycobacterial Species.

BACKGROUND: Coinfections with multiple nontuberculous mycobacterial (NTM) species have not been widely studied. We aimed to evaluate the clinical characteristics and treatment outcomes in patients with NTM-pulmonary disease (PD) caused by coinfection with multiple NTM species. METHODS: We retrospectively reviewed patients with NTM-PD at a tertiary referral hospital in Korea between March 2012 and December 2018. Coinfection was defined as two or more species of NTM pathogens isolated from the same respiratory specimen or different specimens within three months. RESULTS: Among 1,009 patients with NTM-PD, 147 (14.6%) NTM coinfections were observed (average age 64.7 years, 69.4% women). NTM species were identified more frequently (median 6 vs. 3 times, P < 0.001) in the coinfection group than in the single species group, and follow-up duration was also longer in the coinfection group (median 44.9 vs. 27.1 months, P < 0.001). Mycobacterium avium complex (MAC) and M. abscessus and M. massiliense (MAB) were the dominant combinations (n = 71, 48.3%). For patients treated for over six months in the MAC plus MAB group (n = 31), sputum culture conversion and microbiological cure were achieved in 67.7% and 41.9% of patients, respectively. We divided the MAC plus MAB coinfection group into three subgroups according to the target mycobacteria; however, no statistical differences were found in the treatment outcomes. CONCLUSION: In NTM-PD cases, a significant number of multiple NTM species coinfections occurred. Proper identification of all cultured NTM species through follow-up is necessary to detect multispecies coinfections. Further research is needed to understand the nature of NTM-PD in such cases.

Predictive validity of the Developmental Coordination Disorder Questionnaire as a screening tool to identify motor skill problems: A systematic review and meta-analysis.

BACKGROUND: The Developmental Coordination Disorder Questionnaire (DCDQ) has been used to screen children who probably have developmental coordination disorder (DCD). AIMS: We systematically reviewed studies on the predictive validity of the DCDQ and performed a meta-analysis on its diagnostic accuracy. METHODS AND PROCEDURES: Literature was searched through four electronic databases: MEDLINE, Embase, CINAHL, and PsycArticles. A total of 27 studies was selected based on the inclusion criteria. The sensitivity and specificity of the DCDQ were assessed using summary receiver operating characteristic (sROC) curves. Subgroup analyses were conducted according to the DCDQ type, reference standard, and participant type. OUTCOMES AND RESULTS: Overall, the DCDQ has a sensitivity of 0.70 and a specificity of 0.77, showing moderate diagnostic accuracy (area under the curve, 0.80). Subgroup analysis showed that the revised version of the DCDQ had higher diagnostic accuracy than the original version. When the reference standard was the Diagnostic and Statistical Manual of Mental Disorders, the sensitivity and specificity of the DCDQ were 0.87 and 0.83, respectively. The diagnostic accuracy was higher in clinical samples compared to the general population. CONCLUSIONS AND IMPLICATIONS: This study demonstrated that the DCDQ has adequate diagnostic accuracy, suggesting it can help screen children with motor skill deficits.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Microfluidic Isolation of Neuronal-Enriched Extracellular Vesicles Shows Distinct and Common Neurological Proteins in Long COVID, HIV Infection and Alzheimer's Disease.

Long COVID (LongC) is associated with a myriad of symptoms including cognitive impairment. We reported at the beginning of the COVID-19 pandemic that neuronal-enriched or L1CAM+ extracellular vesicles (nEVs) from people with LongC contained proteins associated with Alzheimer's disease (AD). Since that time, a subset of people with prior COVID infection continue to report neurological problems more than three months after infection. Blood markers to better characterize LongC are elusive. To further identify neuronal proteins associated with LongC, we maximized the number of nEVs isolated from plasma by developing a hybrid EV Microfluidic Affinity Purification (EV-MAP) technique. We isolated nEVs from people with LongC and neurological complaints, AD, and HIV infection with mild cognitive impairment. Using the OLINK platform that assesses 384 neurological proteins, we identified 11 significant proteins increased in LongC and 2 decreased (BST1, GGT1). Fourteen proteins were increased in AD and forty proteins associated with HIV cognitive impairment were elevated with one decreased (IVD). One common protein (BST1) was decreased in LongC and increased in HIV. Six proteins (MIF, ENO1, MESD, NUDT5, TNFSF14 and FYB1) were expressed in both LongC and AD and no proteins were common to HIV and AD. This study begins to identify differences and similarities in the neuronal response to LongC versus AD and HIV infection.

Mobile monitoring system detects the disease activity pattern and shows the association with clinical outcomes in patients with newly diagnosed Crohn's disease.

We aimed to determine whether Crohn's disease (CD) activity patterns assessed via a web-based symptom diary can help predict clinical outcomes in patients with newly diagnosed CD. Patients diagnosed with CD within the preceding 3 months were prospectively enrolled at four tertiary centers. All patients recorded their symptoms on a website using a smartphone at least once a week. The index outcomes were disease-related admission and surgery during follow-up. The disease activity from enrollment to outcome or last follow-up was reviewed for pattern analysis. Cox regression analysis was used to identify the predictors of disease outcomes. A total of 102 patients were enrolled. During a median follow-up period of 42 months, 25 (24.5%) and 6 (5.9%) patients required admission and surgery, respectively. Poor activity pattern was an independent predictor of disease-related hospitalization (adjusted hazard ratio [aHR], 3.96; 95% confidence interval [CI] 1.5-10.45; p = 0.005). A poor activity pattern (aHR, 19.48; 95% CI 1.86-203.95; p = 0.013) and female sex (aHR, 11.28; 95% CI 1.49-85.01; p = 0.018) were found to be independent predictors of bowel resection. CD disease activity patterns monitored through the mobile monitoring system may help predict clinical outcomes, such as disease-related hospitalization and surgery, in patients with newly diagnosed CD.

Glutamine-mediated epigenetic regulation of cFLIP underlies resistance to TRAIL in pancreatic cancer.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising anticancer agent because it kills cancer cells while sparing normal cells. However, many cancers, including pancreatic ductal adenocarcinoma (PDAC), exhibit intrinsic or acquired resistance to TRAIL, and the molecular mechanisms underlying TRAIL resistance in cancers, particularly in PDAC, remain unclear. In this study, we demonstrated that glutamine (Gln) endows PDAC cells with resistance to TRAIL through KDM4C-mediated epigenetic regulation of cFLIP. Inhibition of glutaminolysis significantly reduced the cFLIP level, leading to TRAIL-mediated formation of death-inducing signaling complexes. Overexpression of cFLIP dramatically rescued PDAC cells from TRAIL/Gln deprivation-induced apoptosis. Alpha-Ketoglutarate (aKG) supplementation significantly reversed the decrease in the cFLIP level induced by glutaminolysis inhibition and rescued PDAC cells from TRAIL/Gln deprivation-induced apoptosis. Knockdown of glutamic-oxaloacetic transaminase 2, which facilitates the conversion of oxaloacetate and glutamate into aspartate and aKG, decreased aKG production and the cFLIP level and activated TRAIL-induced apoptosis. AKG-mediated epigenetic regulation was necessary for maintaining a high level of cFLIP. Glutaminolysis inhibition increased the abundance of H3K9me3 in the cFLIP promoter, indicating that Gln-derived aKG production is important for Jumonji-domain histone demethylase (JHDM)-mediated cFLIP regulation. The JHDM KDM4C regulated cFLIP expression by binding to its promoter, and KDM4C knockdown sensitized PDAC cells to TRAIL-induced apoptosis. The present findings suggest that Gln-derived aKG production is required for KDM4C-mediated epigenetic regulation of cFLIP, which leads to resistance to TRAIL.

Comparative Pharmacokinetics of Gentamicin C1, C1a and C2 in Healthy and Infected Piglets.

Gentamicin, an aminoglycoside antibiotic, is a mixture of therapeutically active C1, C1a, C2 and other minor components. Despite its decades-long use in pigs and other species, its intramuscular (IM) pharmacokinetics/pharmacodynamics (PKs/PDs) are unknown in piglets. Furthermore, the PKs of many drugs differ between healthy and sick animals. Therefore, we investigated the PKs of gentamicin after a single IM dose (10 mg/kg) in healthy piglets and piglets that were intranasally co-infected with Actinobacillus pleuropneumoniae and Pasteurella multocida (PM). The plasma concentrations were measured using validated liquid chromatography/mass spectrometry. The gentamicin exposure was 36% lower based on the area under the plasma concentration-time curve and 16% lower based on the maximum plasma concentration (Cmax) in the infected piglets compared to the healthy piglets, while it was eliminated faster (shorter half-life and larger clearance) in the infected piglets compared to the healthy piglets. The clearance and volume of distribution were the highest for the C1 component. C1, C1a and C2 accounted for 22-25%, 33-37% and 40-42% of the total gentamicin exposure, respectively. The PK/PD target for the efficacy of aminoglycosides (Cmax/minimum inhibitory concentration (MIC) > 10) could be exceeded for PM, with a greater magnitude in the healthy piglets. We suggest integrating this PK information with antibiotic susceptibility data for other bacteria to make informed antibiotic and dosage regimen selections against piglet infections.

Biomarkers as Endpoints in Clinical Trials for Alzheimer's Disease.

Background: Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. Objective: This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. Methods: In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase. Results: Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-ß (Aß), blood Aß and amyloid-PET; in phase 2, they were MRI, CSF Aß, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aß. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). Conclusions: The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3.

In Silico and In Vitro multiple analysis approach for screening naturally derived ligands for red seabream aryl hydrocarbon receptor.

The aryl hydrocarbon receptor (AHR) is a key ligand-dependent transcription factor that mediates the toxic effects of compounds such as dioxin. Recently, natural ligands of AHR, including flavonoids, have been attracting physiological and toxicological attention as they have been reported to regulate major biological functions such as inflammation and anti-cancer by reducing the toxic effects of dioxin. Additionally, it is known that natural AHR ligands can accumulate in wildlife tissues, such as fish. However, studies in fish have investigated only a few ligands in experimental fish species, and the AHR response of marine fish to natural AHR ligands of various other structures has not been thoroughly investigated. To explore various natural AHR ligands in marine fish, which make up the most fish, it is necessary to develop new screening methods that consider the specificity of marine fish. In this study, we investigated the response of natural ligands by constructing in vitro and in silico experimental systems using red seabream as a model species. We attempted to develop a new predictive model to screen potential ligands that can induce transcriptional activation of red seabream AHR1 and AHR2 (rsAHR1 and rsAHR2). This was achieved through multiple analyses using in silico/ in vitro data and Tox21 big data. First, we constructed an in vitro reporter gene assay of rsAHR1 and rsAHR2 and measured the response of 10 representatives natural AHR ligands in COS-7 cells. The results showed that FICZ, Genistein, Daidzein, I3C, DIM, Quercetin and Baicalin induced the transcriptional activity of rsAHR1 and rsAHR2, while Resveratrol and Retinol did not induce the transcriptional activity of rsAHR isoforms. Comparing the EC50 values of the respective compounds in rsAHR1 and rsAHR2, FICZ, Genistein, and Daidzein exhibited similar isoform responses, but I3C, Baicalin, DIM and Quercetin show the isoform-specific responses. These results suggest that natural AHR ligands have specific profiling and transcriptional activity for each rsAHR isoform. In silico analysis, we constructed homology models of the ligand binding domains (LBDs) of rsAHR1 and rsAHR2 and calculated the docking energies (U_dock values) of natural ligands with measured in vitro transcriptional activity and dioxins reported in previous studies. The results showed a significant correlation (R2=0.74(rsAHR1), R2=0.83(rsAHR2)) between docking energy and transcriptional activity (EC50) value, suggesting that the homology model of rsAHR1 and rsAHR2 can be utilized to predict the potential transactivation of ligands. To broaden the applicability of the homology model to diverse compound structures and validate the correlation with transcriptional activity, we conducted additional analyses utilizing Tox21 big data. We calculated the docking energy values for 1860 chemicals in both rsAHR1 and rsAHR2, which were tested for transcriptional activation in Tox21 data against human AHR. By comparing the U_dock energy values between 775 active compounds and 1085 inactive compounds, a significant difference (p<0.001) was observed between the U_dock energy values in the two groups, suggesting that the U_dock value can be applied to distinguish the activation of compounds. Furthermore, we observed a significant correlation (R2=0.45) between the AC50 of Tox21 database and U_dock values of human AHR model. In conclusion, we calculated equations to translate the results of an in silico prediction model for ligand screening of rsAHR1 and rsAHR2 transactivation. This ligand screening model can be a powerful tool to quantitatively estimate AHR transactivation of major marine agents to which red seabream may be exposed. The study introduces a new screening approach for potential natural AHR ligands in marine fish, based on homology model-docking energy values of rsAHR1 and rsAHR2, with implications for future agonist development and applications bridging in silico and in vitro data.

The characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders.

BACKGROUND: Recent studies have indicated that clinical high risk for psychosis (CHR-P) is highly specific for psychotic disorders other than pluripotential to various serious mental illnesses. However, not all CHR-P develop psychotic disorder only, and psychosis can occur in non-psychotic disorders as well. Our prospective cohort study aims to investigate the characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders. METHODS: The SPRIM study is a prospective naturalistic cohort program that focuses on the early detection of those at risk of developing serious mental illness, including psychosis (CHR-P), bipolar (CHR-B), and depressive disorder (CHR-D), as well as undifferentiated risk participants (UCHR). Our study has a longitudinal design with a baseline assessment and eight follow-up evaluations at 6, 12, 18, 24, 30, 36, 42, and 48 months to determine whether participants have transitioned to psychosis or mood disorders. RESULTS: The SPRIM sample consisted of 90 CHR participants. The total cumulative incidence rate of transition was 53.3% (95% CI 32.5-77.2). CHR-P, CHR-B, CHR-D, and UCHR had cumulative incidence rates of 13.7% (95% CI 3.4-46.4), 52.4% (95% CI 28.1-81.1), 66.7% (95% CI 24.6-98.6) and 54.3% (95% CI 20.5-93.1), respectively. The cumulative incidence of psychosis, bipolar, and depressive disorder among all participants was 3.3% (95% CI 0.8-11.5), 45.7% (95% CI 24.4-73.6), and 11.2% (95% CI 3.1-36.2), respectively. CONCLUSIONS: Our study suggests that the concept of pluripotent high-risk for a diverse range of psychiatric disorders is an integrative approach to examining transdiagnostic interactions between illnesses with a high transition rate and minimizing stigma.

Adiposity, Body Composition Measures, and Breast Cancer Risk in Korean Premenopausal Women.

Objective: To investigate the association between body composition parameters and breast cancer (BC) risk in premenopausal women. Design, Setting, and Participants: Prospective cohort study using data from the Kangbuk Samsung Cohort Study. Participants were women aged 20 to 54 years who were enrolled from 2011 to 2019 and followed up for BC development until December 31, 2020. Data were analyzed from June to August 2023. Exposures: Trained nurses conducted anthropometric measurements and assessed body composition using segmental bioelectric impedance analysis. The analysis encompassed adiposity measures such as body mass index (BMI), waist circumference, and body composition parameters, including muscle mass, fat mass, ratio of muscle mass to weight, ratio of fat mass to weight, and fat mass index. Main outcomes and measures: Adjusted hazard ratios (aHR) for BC during the follow-up period. Results: Among 125 188 premenopausal women, the mean (SD) age was 34.9 (6.3) years. During a mean (range) follow-up of 6.7 (0.5-9.9) years, 1110 incident BC cases were identified. The mean (SD) BMI and waist circumference were 21.6 (3.1) and 75.3 (8.2) cm, respectively. Higher BMI and waist circumference were associated with decreased risk, with an aHR of 0.89 (95% CI, 0.84-0.95) per SD increase in BMI and 0.92 (95% CI, 0.86-0.98) per SD increase in waist circumference. A higher ratio of fat mass to weight was associated with decreased BC risk (aHR, 0.92; 95% CI, 0.86-0.99 per SD increase), whereas the opposite trend was observed for the ratio of muscle mass to weight, with an aHR of 1.08 (95% CI, 1.02-1.15) per SD increase. The results remained consistent even after additional adjustments for height in the model. The fat mass index was also inversely associated with BC risk, with an HR of 0.90 (95% CI, 0.85-0.97) per SD increase. Conclusions and Relevance: In this cohort study of premenopausal women, a higher level of adiposity, represented by increased BMI, waist circumference, and fat mass, was consistently associated with decreased breast cancer risk. Conversely, muscle mass and its ratio to weight displayed opposite or inconsistent patterns. These findings suggest an inverse association between excess adiposity and the risk of BC in premenopausal women, confirming earlier findings that BMI is an indirect measure of adiposity.

Ursodeoxycholic acid alleviates atopic dermatitis-associated inflammatory responses in HaCaT and RBL-2H3 cells and DNCB/DFE-treated mice.

AIMS: Ursodeoxycholic acid (UDCA) is a hydrophilic dihydroxy bile acid used for cholestatic liver disease and exhibits antioxidant, antitumor, and anti-inflammatory effects. However, its potential effects on atopic dermatitis (AD) have not been elucidated. This study aimed to evaluate the efficacy of UDCA in inhibiting the inflammatory response and alleviating lesions in AD-like mice. MAIN METHODS: To investigate the efficacy of UDCA in AD-like inflammatory responses, tumor necrosis factor-alpha (TNF-α)- and interferon-gamma (IFN-γ)-stimulated HaCaT cells and anti-dinitrophenyl immunoglobulin E (DNP-IgE)- and human serum albumin (HSA)-stimulated RBL-2H3 cells were used to investigate the levels of inflammatory factors and their mechanisms. AD-like lesions were induced by applying DNCB/DFE to mice. The effect of UDCA administration in AD-like mice was analyzed by assessing organ weight, serum IgE and inflammatory cytokine levels, and histopathological changes using immunohistochemical and immunofluorescent staining. KEY FINDINGS: In HaCaT cells, UDCA significantly diminished TARC, MDC, MCP-1, and IL-6 expression by inhibiting the phosphorylation of nuclear NF-κB and cytoplasmic IκB, and also increased the levels of skin barrier protein. In RBL-2H3 cells, UDCA reduced ß-hexosaminidase and IL-4 levels. In AD-like mice, UDCA suppressed organ hypertrophy, ear edema, SCORAD index, DFE-specific IgE levels, inflammatory cytokine levels, skin hypertrophy, mast cell invasion, skin barrier loss, and thymic stromal lymphopoietin-positive areas. SIGNIFICANCE: UDCA suppressed the expression of pro-inflammatory cytokines by keratinocytes and mast cells. It also alleviated atopy by suppressing symptoms without organ toxicity in AD-like mice. UDCA may be an effective and safe treatment for AD.