A Novel Ferrocene-Linked Thionine as a Dual Redox Mediator for the Electrochemical Detection of Dopamine and Hydrogen Peroxide.

We introduce a novel dual redox mediator synthesized by covalently linking ferrocene dicarboxylic acid (FcDA) and thionine (TH) onto a pre-treated glassy carbon electrode. This unique structure significantly enhances the electro-oxidation of dopamine (DA) and the reduction of hydrogen peroxide (H2O2), offering a sensitive detection method for both analytes. The electrode exhibits exceptional sensitivity, selectivity, and stability, demonstrating potential for practical applications in biosensing. It facilitates rapid electron transfer between the analyte and the electrode surface, detecting H2O2 concentrations ranging from 1.5 to 60 µM with a limit of detection (LoD) of 0.49 µM and DA concentrations from 0.3 to 230 µM with an LoD of 0.07 µM. The electrode's performance was validated through real-sample analyses, yielding satisfactory results.

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.

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.

Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis.

BACKGROUND: The Kellgren-Lawrence (KL) grading system is the most widely used method to classify the severity of osteoarthritis (OA) of the knee. However, due to ambiguity of terminology, the KL system showed inferior inter- and intra-observer reliability. For a more reliable evaluation, we recently developed novel deep learning (DL) software known as MediAI-OA to extract each radiographic feature of knee OA and to grade OA severity based on the KL system. METHODS: This research used data from the Osteoarthritis Initiative for training and validation of MediAI-OA. 44,193 radiographs and 810 radiographs were set as the training data and used as validation data, respectively. This AI model was developed to automatically quantify the degree of joint space narrowing (JSN) of medial and lateral tibiofemoral joint, to automatically detect osteophytes in four regions (medial distal femur, lateral distal femur, medial proximal tibia and lateral proximal tibia) of the knee joint, to classify the KL grade, and present the results of these three OA features together. The model was tested by using 400 test datasets, and the results were compared to the ground truth. The accuracy of the JSN quantification and osteophyte detection was evaluated. The KL grade classification performance was evaluated by precision, recall, F1 score, accuracy, and Cohen's kappa coefficient. In addition, we defined KL grade 2 or higher as clinically significant OA, and accuracy of OA diagnosis were obtained. RESULTS: The mean squared error of JSN rate quantification was 0.067 and average osteophyte detection accuracy of the MediAI-OA was 0.84. The accuracy of KL grading was 0.83, and the kappa coefficient between the AI model and ground truth was 0.768, which demonstrated substantial consistency. The OA diagnosis accuracy of this software was 0.92. CONCLUSIONS: The novel DL software known as MediAI-OA demonstrated satisfactory performance comparable to that of experienced orthopedic surgeons and radiologists for analyzing features of knee OA, KL grading and OA diagnosis. Therefore, reliable KL grading can be performed and the burden of the radiologist can be reduced by using MediAI-OA.

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.

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.

Monomelic Multifocal Neuropathy: An Unrecognized Electrophysiological Feature of Hereditary Neuropathy with Liability to Pressure Palsies in Childhood.

Hereditary neuropathy with liability to pressure palsies (HNPP) is well defined in adults, but its clinical and electrophysiological features in childhood have not been well characterized. We describe a case of HNPP in a child with the unique electrophysiological presentation, affecting only one upper extremity.

Clinical Validation of an Artificial Intelligence Model for Detecting Distal Radius, Ulnar Styloid, and Scaphoid Fractures on Conventional Wrist Radiographs.

This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887-0.918), 0.925 (95% C.I. 0.911-0.939), and 0.808 (95% C.I. 0.748-0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66-0.83) to 0.85 (95% C.I. 0.77-0.93) and from 0.71 (95% C.I. 0.62-0.80) to 0.80 (95% C.I. 0.71-0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed.

Prediction of Fishman's skeletal maturity indicators using artificial intelligence.

The present study aimed to evaluate the performance of automated skeletal maturation assessment system for Fishman's skeletal maturity indicators (SMI) for the use in dental fields. Skeletal maturity is particularly important in orthodontics for the determination of treatment timing and method. SMI is widely used for this purpose, as it is less time-consuming and practical in clinical use compared to other methods. Thus, the existing automated skeletal age assessment system based on Greulich and Pyle and Tanner-Whitehouse3 methods was further developed to include SMI using artificial intelligence. This hybrid SMI-modified system consists of three major steps: (1) automated detection of region of interest; (2) automated evaluation of skeletal maturity of each region; and (3) SMI stage mapping. The primary validation was carried out using a dataset of 2593 hand-wrist radiographs, and the SMI mapping algorithm was adjusted accordingly. The performance of the final system was evaluated on a test dataset of 711 hand-wrist radiographs from a different institution. The system achieved a prediction accuracy of 0.772 and mean absolute error and root mean square error of 0.27 and 0.604, respectively, indicating a clinically reliable performance. Thus, it can be used to improve clinical efficiency and reproducibility of SMI prediction.