Clinical application of the Panbio™ COVID-19 Ag rapid test device and SSf-COVID19 kit for the detection of SARS-CoV-2 infection

Objective We evaluated the sensitivity and specificity of the Panbio™ COVID-19 Ag rapid test device using nasal swabs and those of the SSf-COVID19 kit, one of RT-PCR tests, using saliva specimens. These tests were compared with RT-PCR tests using nasopharyngeal swabs for the diagnosis of SARS-CoV-2 infection. The three diagnostic tests were simultaneously conducted for patients aged ≥ 18 years, who were about to be hospitalized or had been admitted for COVID-19 confirmed by RT-PCR in two research hospitals from August 20 to October 29, 2021. Nasal swabs were tested using the Panbio™ COVID-19 Ag rapid test device. More than 1 mL of saliva was self-collected and tested using the SSf-COVID19 kit. Results In total, 157 patients were investigated; 124 patients who were about to be hospitalized and 33 patients already admitted for COVID-19. The overall sensitivity and specificity of the Panbio™ COVID-19 Ag rapid test device with nasal swabs were 64.7% (95% confidence interval [CI] 47.9–78.5%) and 100.0% (95% CI 97.0–100.0%), respectively. The median time to confirm a positive result was 180 s (interquartile range 60–255 s). The overall sensitivity and specificity of the SSf-COVID19 kit with saliva specimens were 94.1% (95% CI 80.9–98.4%) and 100.0% (95% CI 97.0–100.0%), respectively.This work was supported by a grant from research fund of Seoul National University Hospital (Grant No. 2021–3148

Classification of the Relationship Between Mandibular Third Molar and Inferior Alveolar Nerve Based on Generated Mask Images

In recent dentistry research, deep learning techniques have been employed for various tasks, including detecting and segmenting third molars and inferior alveolar nerves, as well as classifying their positional relationships. Prior studies using convolutional neural networks (CNNs) have successfully detected the adjacent area of the third molar and automatically classified the relationship between the inferior alveolar nerves. However, deep learning models have limitations in learning the diverse patterns of teeth and nerves due to variations in their shape, angle, and size across individuals. Moreover, unlike object classification, relationship classification is influenced by the proximity of teeth and nerves, making it challenging to accurately interpret the classified samples. To address these challenges, we propose a masking image-based classification system. The primary goal of this system is to enhance the classification performance of the relationship between the third molar and inferior alveolar nerve while providing diagnostic evidence to support the classification. Our proposed system operates by detecting the adjacent areas of the third molar, including the inferior alveolar nerve, in panoramic radiographs (PR). Subsequently, it generates masked images of the inferior alveolar nerve and third molar within the extracted regions of interest. Finally, it performs the classification of the relationship between the third molar and inferior alveolar nerve using these masked images. The system achieved a mean average precision (mAP) of 0.885 in detecting the region of interest in the third molar. Furthermore, the performance of the existing CNN-based positional relationship classification was evaluated using four classification models, resulting in an average accuracy of 0.795. For the segmentation task, the third molar and inferior alveolar nerve in the detected region of interest exhibited a dice similarity coefficient (DSC) of 0.961 and 0.820, respectively. Regarding the proposed masking image-based classification, it demonstrated an accuracy of 0.832, outperforming the existing method by approximately 3%, thus confirming the superiority of our proposed system

Thymosin Beta 4 Inhibits LPS and ATP-Induced Hepatic Stellate Cells via the Regulation of Multiple Signaling Pathways

Risk signals are characteristic of many common inflammatory diseases and can function to activate nucleotide-binding oligomerization (NLR) family pyrin domain-containing 3 (NLRP3), the innate immune signal receptor in cytoplasm. The NLRP3 inflammasome plays an important role in the development of liver fibrosis. Activated NLRP3 nucleates the assembly of inflammasomes, leading to the secretion of interleukin (IL)-1β and IL-18, the activation of caspase-1, and the initiation of the inflammatory process. Therefore, it is essential to inhibit the activation of the NLRP3 inflammasome, which plays a vital role in the immune response and in initiating inflammation. RAW 264.7 and LX-2 cells were primed with lipopolysaccharide (LPS) for 4 h and subsequently stimulated for 30 min with 5 mM of adenosine 5′-triphosphate (ATP) to activate the NLRP3 inflammasome. Thymosin beta 4 (Tβ4) was supplemented to RAW264.7 and LX-2 cells 30 min before ATP was added. As a result, we investigated the effects of Tβ4 on the NLRP3 inflammasome. Tβ4 prevented LPS-induced NLRP3 priming by inhibiting NF-kB and JNK/p38 MAPK expression and the LPS and ATP-induced production of reactive oxygen species. Moreover, Tβ4 induced autophagy by controlling autophagy markers (LC3A/B and p62) through the inhibition of the PI3K/AKT/mTOR pathway. LPS combined with ATP significantly increased thee protein expression of inflammatory mediators and NLRP3 inflammasome markers. These events were remarkably suppressed by Tβ4. In conclusion, Tβ4 attenuated NLRP3 inflammasomes by inhibiting NLRP3 inflammasome-related proteins (NLRP3, ASC, IL-1β, and caspase-1). Our results indicate that Tβ4 attenuated the NLRP3 inflammasome through multiple signaling pathway regulations in macrophage and hepatic stellate cells. Therefore, based on the above findings, it is hypothesized that Tβ4 could be a potential inflammatory therapeutic agent targeting the NLRP3 inflammasome in hepatic fibrosis regulation

Thymosin Beta 4 Inhibits LPS and ATP-Induced Hepatic Stellate Cells via the Regulation of Multiple Signaling Pathways

Risk signals are characteristic of many common inflammatory diseases and can function to activate nucleotide-binding oligomerization (NLR) family pyrin domain-containing 3 (NLRP3), the innate immune signal receptor in cytoplasm. The NLRP3 inflammasome plays an important role in the development of liver fibrosis. Activated NLRP3 nucleates the assembly of inflammasomes, leading to the secretion of interleukin (IL)-1β and IL-18, the activation of caspase-1, and the initiation of the inflammatory process. Therefore, it is essential to inhibit the activation of the NLRP3 inflammasome, which plays a vital role in the immune response and in initiating inflammation. RAW 264.7 and LX-2 cells were primed with lipopolysaccharide (LPS) for 4 h and subsequently stimulated for 30 min with 5 mM of adenosine 5′-triphosphate (ATP) to activate the NLRP3 inflammasome. Thymosin beta 4 (Tβ4) was supplemented to RAW264.7 and LX-2 cells 30 min before ATP was added. As a result, we investigated the effects of Tβ4 on the NLRP3 inflammasome. Tβ4 prevented LPS-induced NLRP3 priming by inhibiting NF-kB and JNK/p38 MAPK expression and the LPS and ATP-induced production of reactive oxygen species. Moreover, Tβ4 induced autophagy by controlling autophagy markers (LC3A/B and p62) through the inhibition of the PI3K/AKT/mTOR pathway. LPS combined with ATP significantly increased thee protein expression of inflammatory mediators and NLRP3 inflammasome markers. These events were remarkably suppressed by Tβ4. In conclusion, Tβ4 attenuated NLRP3 inflammasomes by inhibiting NLRP3 inflammasome-related proteins (NLRP3, ASC, IL-1β, and caspase-1). Our results indicate that Tβ4 attenuated the NLRP3 inflammasome through multiple signaling pathway regulations in macrophage and hepatic stellate cells. Therefore, based on the above findings, it is hypothesized that Tβ4 could be a potential inflammatory therapeutic agent targeting the NLRP3 inflammasome in hepatic fibrosis regulation

Classification of Liver Fibrosis From Heterogeneous Ultrasound Image

With the advances in deep learning, including Convolutional Neural Networks (CNN), automated diagnosis technology using medical images has received considerable attention in medical science. In particular, in the field of ultrasound imaging, CNN trains the features of organs through an amount of image data, so that an expert-level automatic diagnosis is possible only with images of actual patients. However, CNN models are also trained on the features that reflect the inherent bias of the imaging machine used for image acquisition. In other words, when the domain of data used for training is different from that of data applied for an actual diagnosis, it is unclear whether consistent performance can be provided by the domain bias. Therefore, we investigate the effect of domain bias on the model with liver ultrasound imaging data obtained from multiple domains. We have constructed a dataset considering the manufacturer and the year of manufacturing of 8 ultrasound imaging machines. First, training and testing were performed by dividing the entire data, in a commonly used method. Second, we have utilized the training data constructed according to the number of domains for the machine learning process. Then we have measured and compared the performance on internal and external domain data. Through the above experiment, we have analyzed the effect of domains of data on model performance. We show that the performance scores evaluated with the internal domain data and the external domain data do not match. We especially show that the performance measured in the evaluation data including the internal domain was much higher than the performance measured in the evaluation data consisting of the external domain. We also show that 3-level classification performance is slightly improved over 5-level classification by mitigating class imbalance by integrating similar classes. The results highlight the need to develop a new methodology for mitigating the machine bias problem so that the model can work correctly even on external domain data, as opposed to the usual approach of constructing evaluation data in the same domain as the training data

Automated classification of liver fibrosis stages using ultrasound imaging

Abstract Background Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images. Methods We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio. Results Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84. Conclusion In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice

False-positive results of galactomannan assays in patients administered glucose-containing solutions

Abstract Galactomannan (GM) is a polysaccharide cell wall component released by Aspergillus spp., and an immunoenzymatic GM assay is used for the diagnosis of invasive pulmonary aspergillosis. We evaluated the cause of strong positivity for GM in patients with no typical signs of aspergillosis. Repeat assays were performed using different instruments and reagent lots, but there were no differences in results among the assays. Patients with strongly positive GM results were investigated. Medication histories revealed that 14 of 23 patients had been administered total parenteral nutrition solution from one manufacturer and 4 patients had been administered dextrose solution from a different manufacturer before being tested. The results of GM assays conducted on samples of dextrose solution and the glucose fraction of the total parenteral nutrition solution were strongly positive, confirming the causes of the false-positive reactions. We hypothesize that a trace amount of GM was introduced into the glucose-containing solutions because glucoamylase, which is necessary for the saccharification step of glucose synthesis, was derived from Aspergillus niger. To enhance patient care and prevent unnecessary antifungal prescriptions, healthcare providers and manufacturers of healthcare products need to be aware of the possibility of false-positive reactions for GM

Detecting mpox infection in the early epidemic: an epidemiologic investigation of the third and fourth cases in Korea

OBJECTIVES As few mpox cases have been reported in Korea, we aimed to identify the characteristics of mpox infection by describing our epidemiologic investigation of a woman patient (index patient, the third case in Korea) and a physician who was infected by a needlestick injury (the fourth case). METHODS We conducted contact tracing and exposure risk evaluation through interviews with these 2 patients and their physicians and contacts, as well as field investigations at each facility visited by the patients during their symptomatic periods. We then classified contacts into 3 levels according to their exposure risk and managed them to minimize further transmission by recommending quarantine and vaccination for post-exposure prophylaxis and monitoring their symptoms. RESULTS The index patient had sexual contact with a man foreigner during a trip to Dubai, which was considered the probable route of transmission. In total, 27 healthcare-associated contacts across 7 healthcare facilities and 9 community contacts were identified. These contacts were classified into high (7 contacts), medium (9 contacts), and low (20 contacts) exposure risk groups. One high-risk contact was identified as a secondary patient: a physician who was injured while collecting specimens from the index patient. CONCLUSIONS The index patient visited several medical facilities due to progressive symptoms prior to isolation. Although the 2022 mpox epidemic mainly affected young men, especially men who have sex with men, physicians should also consider mpox transmission in the general population for the timely detection of mpox-infected patients

Possibility of Decreasing Incidence of Human Immunodeficiency Virus Infection in Korea

Background: The number of newly diagnosed cases of human immunodeficiency virus (HIV) infection in Korea, which had increased until 2019, has markedly decreased since the coronavirus disease 2019 pandemic started. This study evaluated whether the decrease is due to a reduction in the incidence of HIV infection and/or delayed diagnosis during the pandemic.Materials and Methods: We reviewed the medical records of 587 newly diagnosed patients with HIV infection between February 2018 and January 2022 from four general hospitals, and their characteristics were compared between the prepandemic and pandemic periods. The lapse time from infection to diagnosis was estimated using an HIV modeling tool.Results: The estimated mean times to diagnosis were 5.68 years (95% confidence interval [CI]: 4.45 - 6.51 years) and 5.41 years (95% CI: 4.09 - 7.03 years) before and during the pandemic, respectively (P = 0.016). The proportion of patients with acquired immunodeficiency syndrome-defining illnesses, expected to visit hospitals regardless of the pandemic, decreased from 17.2% before the pandemic to 11.9% during the pandemic (P = 0.086). Conclusion: The decrease in the number of newly diagnosed cases of HIV infection in Korea might have resulted from an actual decrease in the incidence of HIV infection rather than a worsening of underdiagnosis or delayed diagnosis.Y