Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea.

BACKGROUND: The COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea. METHODS: The analysis was based on a cognitive-emotional model that links fear and anger to the social causes that trigger them and used discursive content from comments posted on YouTube's COVID-19-related videos. A total of 182,915 comments from 1,200 videos were collected between January and December 2020. We performed data analyses and visualizations using R, Netminer 4.0, and Gephi software and calculated Pearson's correlation coefficients between emotions. RESULTS: YouTube videos were analyzed for keywords indicating cognitive assessments of major events related to COVID-19 and keywords indicating negative emotions. Eight topics were identified through topic modeling: causes and risks, perceptions of China, media and information, infection prevention rules, economic activity, school and infection, political leaders, and religion, politics, and infection. The correlation coefficient between fear and anger was 0.462 (p < .001), indicating a moderate linear relationship between the two emotions. Fear was the highest from January to March in the first year of the COVID-19 outbreak, while anger occurred before and after the outbreak, with fluctuations in both emotions during this period. CONCLUSIONS: This study confirmed that social cognitions and negative emotions are intertwined in response to major events related to the COVID-19 pandemic, with each emotion varying individually rather than being ambiguously mixed. These findings could aid in developing social cognition-emotion-based public health strategies through education and communication during future pandemic outbreaks.

Effects of Cissus quadrangularis L. Powder on Proximate Composition, Physicochemical and Textural Properties of Tteokgalbi.

We investigated Cissus quadrangularis L. powder (C) use as a natural additive to Tteokgalbi, a traditional Korean meat-based dish. Five distinct Tteokgalbi samples were treated: one without any additives (negative control, NC), one with 1.00% C (C1), 2.00% C (C2), 4.00% C (C3), and 0.10% ascorbic acid (positive control, PC). C addition resulted in changes in composition, quality, and sensory attributes. Moisture content decreased with higher C levels; crude protein varied among the groups, with C1 having the highest crude protein levels and C3 the lowest. Crude fat decreased with increasing C concentration, whereas the carbohydrate content increased. The water-holding capacity notably decreased in the C3 group, resulting in increased cooking loss with higher C concentrations. C treatment altered color and texture, reducing CIE L* and increasing CIE a* before cooking and increasing CIE L* and CIE a* after cooking. CIE b* decreased before cooking but increased thereafter. C-treated Tteokgalbi was less cohesive, chewy, and brittle compared to the NC. The C treatment increased the total phenolic and flavonoid contents and enhanced radical scavenging capacities. It also affects storage characteristics, lowers pH, and increases 2-thiobarbituric acid reactive substances values. The microbial counts were lower in C2 and C3 after 11 days. These findings suggest the potential use of C as a natural meat additive.

Sex-specific disparities in disease activity scores among patients with axial spondyloarthritis and their implications for evaluating the response to tumor necrosis factor alpha inhibitor therapy.

BACKGROUND: We aimed to investigate whether there are sex differences in disease activity measures among patients with axial spondyloarthritis (axSpA) and to determine any potential impact on the assessment of treatment responses to tumor necrosis factor alpha inhibitors (TNFi). METHODS: Using the Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry data, we compared sex differences in changes in the Bath Ankylosing Spondylitis Disease Activity Score (BASDAI) and Ankylosing Spondylitis Disease Activity Score (ASDAS) levels at baseline and one year after TNFi initiation in patients with axSpA. RESULTS: This study included 1,753 patients with axSpA who started or changed TNFi, of whom 1,343 (76.6%) were male. At baseline, the mean BASDAI and ASDAS scores of all patients were 5.98 and 3.6, respectively. The BASDAI changes between baseline and the one-year follow-up were independently associated with sex (𝛽 = 0.343, p = 0.011), whereas ASDAS was not (𝛽 = 0.079, p = 0.235). When judging the effect of TNFi at one-year of treatment, male patients were more likely to be assessed as effective by the BASDAI-based criterion (ΔBASDAI ≥ 50% or ≥ 2; OR 1.700, 95% CI 1.200-2.406), while the ASDAS-based criterion (ΔASDAS ≥ 1.1) showed no significant difference between sexes (OR 0.993, 95% CI 0.678-1.455), after adjusting for other baseline characteristics. CONCLUSIONS: The changes in disease activity before and after TNFi use were significantly different between sexes when measured by BASDAI, but not ASDAS. TNFi treatment effects may be interpreted differently between sexes depending on the disease activity measure used.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants.

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

Surface Reconstruction with Aprotic Trimethylsulfonium Iodide for Efficient and Stable Perovskite Solar Cells.

Organic ammonium salts are widely used for surface passivation to enhance the photovoltaic (PV) performance and stability of perovskite solar cells (PSCs). However, the protic nature of ammonium units results in the quick degradation of perovskites due to the hydrogen bonding interaction with water molecules. Recently, organo-sulfur compounds have attracted growing interest as passivation layers on three-dimensional perovskites due to their moisture-resistive behavior. Herein, trimethylsulfonium iodide (TMSI), an aprotic S-based organic compound, is employed for surface modification of methylammonium lead iodide-based PSCs to impede moisture penetration, improve charge transfer, and passivate surface defects. The TMSI effectively passivates uncoordinated Pb through Pb···S interactions, and the optimized PSC exhibits a power conversion efficiency (PCE) of 21.03% with an open-circuit voltage of ca. 1.13 V under one-sun illumination, while it reached up to 37.58 and 37.69% under low-intensity indoor illuminations, 1000 and 2000 lx with LED 5000 K, respectively. TMSI-treated cells display enhanced device stability by retaining 92.7% of their initial PCE after 50 days of storage in ambient conditions. This study provides a novel and effective surface reconstruction strategy with aprotic materials to improve PV performance and device stability in PSCs.

Reversible cerebral vasoconstriction syndrome in Guillain-Barre syndrome: a case report and literature review.

BACKGROUND: Reversible cerebral vasoconstriction syndrome (RCVS) is characterized by transient constriction of cerebral arteries, leading to severe headache and potential complications. The association between RCVS and Guillain-Barre syndrome (GBS) is rare and poorly understood and warrants further investigation. METHODS: A detailed case of RCVS in a patient with GBS was presented, followed by a comprehensive literature review. PubMed, Embase, and Google Scholar were searched for relevant cases and studies. RESULTS: The case involved a 62-year-old woman with GBS who developed RCVS. The literature review identified three additional reported cases. RCVS in GBS primarily affected middle-aged women and presented with a variety of neurological symptoms. Neuroimaging showed reversible vasoconstriction in the cerebral arteries, along with other complications such as posterior reversible encephalopathy syndrome, subarachnoid hemorrhage, and infarcts. While the treatment for GBS consisted mainly of intravenous immunoglobulin, specific treatments for RCVS remain unclear. CONCLUSIONS: The coexistence of RCVS and GBS is a rare occurrence. RCVS in GBS may result from the disruption of cerebral vascular tone regulation, possibly influenced by GBS-related dysautonomia and consequent high blood pressure. Recognizing RCVS in GBS patients is critical for appropriate management.

Neurofilament light chain as a biomarker in neuromyelitis optica spectrum disorder: a comprehensive review and integrated analysis with glial fibrillary acidic protein.

BACKGROUND: In the context of neuromyelitis optica spectrum disorder (NMOSD), there are several measures that serve as a biomarker. However, each of the methods has the intrinsic limitations. While neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) have emerged as an additional biomarker for NMOSD, a thorough investigation of their role remains incomplete. Our aim is to provide a comprehensive review of the current literature regarding NfL and GFAP as a biomarker and explore their potential utility in NMOSD. METHODS: We performed a comprehensive search using PubMed and Google Scholar to identify peer-reviewed articles investigating NfL and GFAP as a biomarker in NMOSD. RESULTS: Our search identified 13 relevant studies. NfL consistently showed promise in distinguishing NMOSD patients from healthy individuals, although it had limited specificity in distinguishing NMOSD from other demyelinating diseases. NfL offered certain advantages over GFAP, notably its ability to predict disability worsening during attacks. In contrast, GFAP provided valuable insight, particularly in distinguishing NMOSD from multiple sclerosis and identifying clinical relapses. In addition, GFAP showed predictive potential for future attacks. Some studies even suggested that NfL may serve as an indicator of treatment response in NMOSD. CONCLUSIONS: NfL and GFAP hold promise as biomarkers for NMOSD, demonstrating their usefulness in distinguishing patients from healthy individuals, assessing disease severity, and possibly reflecting treatment response. However, it is important to recognize that NfL and GFAP may, at some point, have different roles.

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.

Background: Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI. Methods: This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation. Results: A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation. Conclusion: An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

Women with Metabolic Syndrome and Unhealthy Lifestyle Factors Are at a Higher Risk for Hyperuricemia.

Hyperuricemia (HUA) has become a significant medical concern due to its complications and links to metabolic syndrome (MetS) and cardiovascular disease (CVD), which result in increased mortality. The pathogenic processes associated with unhealthy behaviors, MetS, and HUA can be cooperative and potentially synergistic in the activation of risk factors. Recent research has shown sex-based differences in the relationship between HUA and its associated risk factors. This study aimed to investigate these differences, particularly in the context of MetS and CVD risk factors and unhealthy lifestyles. We also aimed to evaluate the joint effects of these factors based on sex. We conducted a cross-sectional study using nationally representative survey data from the Korean National Health and Nutritional Examination Survey 2016-2018. We performed multivariable logistic regression analysis, calculating adjusted odds ratios (ORs) with their 95% confidence intervals (CIs). We also conducted subgroup analyses based on sex and the presence of MetS with or without unhealthy lifestyle factors (tobacco use, alcohol intake). We found sex-based differences in the relationships between HUA and MetS, CVD risk factors, and lifestyle behaviors. Our major finding was a significant association between MetS and HUA in both men and women, regardless of alcohol consumption and smoking status, and this association was stronger in women. We also observed a synergistic effect of MetS and lifestyle factors on the risk of HUA, particularly in women, in whom the risk of HUA increased up to four times compared to the reference group. A sex-based clinical strategy for HUA is necessary to reduce related complications and their socio-economic burden.

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.

OBJECTIVES: Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. METHODS: MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. RESULTS: Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. CONCLUSION: The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.

Acute Hemorrhagic Encephalomyelitis in the Context of MOG Antibody-Associated Disease. Comment on Chen et al. Rapid Progressive Fatal Acute Hemorrhagic Encephalomyelitis. Diagnostics 2023, 13, 2481.

The study by Chen et al. of a 56-year-old man diagnosed with acute hemorrhagic encephalomyelitis (AHEM) had a significant impact on us. The authors provided a comprehensive account of their diagnostic journey and emphasized the need to differentiate myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) from AHEM. However, recent research suggests that AHEM may not be an isolated entity, but rather a phenotype within MOGAD. The patient's clinical presentation included MRI brain lesions characteristic of MOGAD in addition to hemorrhagic abnormalities. These findings raise the possibility that AHEM in this case represents a MOGAD phenotype. In conclusion, it is important to recognize the potential association between AHEM and MOGAD, especially when distinct MOGAD brain MRI patterns are present, as in this case.

Molecular Engineering of Photosensitizers for Solid-State Dye-Sensitized Solar Cells: Recent Developments and Perspectives.

Dye-sensitized solar cells (DSSCs) are a feasible alternative to traditional silicon-based solar cells because of their low cost, eco-friendliness, flexibility, and acceptable device efficiency. In recent years, solid-state DSSCs (ss-DSSCs) have garnered much interest as they can overcome the leakage and evaporation issues of liquid electrolyte systems. However, the poor morphology of solid electrolytes and their interface with photoanodes can minimize the device performance. The photosensitizer/dye is a critical component of ss-DSSCs and plays a vital role in the device's overall performance. In this review, we summarize recent developments and performance of photosensitizers, including mono- and co-sensitization of ruthenium, porphyrin, and metal-free organic dyes under 1 sun and ambient/artificial light conditions. We also discuss the various requirements that efficient photosensitizers should satisfy and provide an overview of their historical development over the years.