Care burden of parents of adult children with mental illness: The role of associative stigma.

OBJECTIVE: Parents of offspring with mental illness must endure endless child care burden despite their old age, and must cope with associative stigma. This study analyzed the mediator effect of associative stigma on relationships between the main stressors, psychiatric symptoms and lowered social function of offspring with mental illness, assessed by the parents, and their care burden. METHOD: 215 parents caring for an adult child with mental illness in Korea were surveyed (Mage=60.68, SD=13.58; 74.4% mothers). They were asked to assess the psychiatric symptoms and social function of their offspring, the stigma they experienced, and the objective/subjective care burdens they felt. RESULTS: Our findings suggest that the symptoms and function of offspring directly affect the care burden of parents, but also have an indirect effect mediated by associative stigma. Among the predictor variables, symptoms have a greater effect on the subjective/objective burden and associative stigma than social function. CONCLUSIONS: We suggest strategies for parents to overcome associative stigma and emphasize the professional endeavor required to meet the service needs of elderly parents taking care of an adult child with mental illness.

Psychotic Symptoms in Patients With Major Neurological Diseases.

Neurological diseases often manifest with neuropsychiatric symptoms such as depression, emotional incontinence, anger, apathy and fatigue. In addition, affected patients may also experience psychotic symptoms such as hallucinations and delusions. Various factors contribute to the development of psychotic symptoms, and the mechanisms of psychosis are similar, but still differ among various neurological diseases. Although psychotic symptoms are uncommon, and have been less well investigated, they may annoy patients and their families as well as impair the patients' quality of life and increase the caregiver burden. Therefore, we need to appropriately identify and treat these psychotic symptoms in patients with neurological diseases.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.

BACKGROUNDS AND OBJECTIVE: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks. STUDY DESIGN: This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and 'None' without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). RESULTS: Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. CONCLUSION: Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases.

Two-Color Pixel Patterning for High-Resolution Organic Light-Emitting Displays Using Photolithography.

Nowadays, the display industry is endeavoring to develop technology to provide large-area organic light-emitting diode (OLED) display panels with 8K or higher resolution. Although the selective deposition of organic molecules through shadow masks has proven to be the method of choice for mobile panels, it may not be so when independently defined high-resolution pixels are to be manufactured on a large substrate. This technical challenge motivated us to adopt the well-established photolithographic protocol to the OLED pixel patterning. In this study, we demonstrate the two-color OLED pixels integrated on a single substrate using a negative-tone highly fluorinated photoresist (PR) and fluorous solvents. Preliminary experiments were performed to examine the probable damaging effects of the developing and stripping processes upon a hole-transporting layer (HTL). No significant deterioration in the efficiency of the develop-processed device was observed. Efficiency of the device after lift-off was up to 72% relative to that of the reference device with no significant change in operating voltage. The procedure was repeated to successfully obtain two-color pixel arrays. Furthermore, the patterning of 15 µm green pixels was accomplished. It is expected that photolithography can provide a useful tool for the production of high-resolution large OLED displays in the near future.

The impact of self-stigma on self-esteem among persons with different mental disorders.

BACKGROUND: This study assumes that just as public stigma differs depending on types of mental disorder, so too does self-stigma. AIMS: This study aims to compare self-stigma among persons with schizophrenia, alcohol use disorder and gambling disorder, and thereby analyze the effects of self-stigma on their self-esteem. METHODS: A total of 321 Korean adults involved in community mental services for schizophrenia (N = 116), alcohol use disorder (N = 102) and gambling disorder (N = 103) were surveyed (Mage = 40.74, standard deviation (SD) = 10.10, 83.8% male, 16.2% female). Participants were questioned on self-stigma and self-esteem. One-way analysis of variance (ANOVA) was used to compare the self-stigma by mental disorder type. Furthermore, in order to analyze the effects of self-stigma on self-esteem with subjects' age and educational background controlled, hierarchical regression analysis was used. RESULTS: The self-stigma of gambling disorder group was highest not only in overall self-stigma but also some of its subscales - alienation, stereotype endorsement and stigma resistance - followed by alcohol use disorder group and schizophrenia group. In all three groups, self-stigma had a negative effect on self-esteem, while stigma resistance of subscales was the most important predictor. In addition to stigma resistance, alienation was a predictor in the schizophrenia group, alienation and social withdrawal in the alcohol use disorder group and social withdrawal was a significant predictor in the gambling disorder group. Therefore, the predictors of self-esteem differed depending on the type of mental disorder. CONCLUSION: Based on these results, we suggest cognitive-behavioral intervention to raise subject awareness of the unjust social stigma and boost self-advocacy to resist the stigma.