OBJECTIVE To investigate the improvement effects and mechanism of astragaloside Ⅳ （AS- Ⅳ ） on lipopolysaccharide （LPS）-induced neuroinflammation. METHODS BV2 cells were divided into control group， LPS group， AS-Ⅳ groups at concentrations of 20 and 40 μmol/L， and dexamethasone group （2 μmol/L）. Except for control group， neuroinflammation model was established with LPS （1 μg/mL） in other groups after medication. The levels of inflammatory factors [interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， and nitric oxide （NO）] in cell supernatant were measured in each group. Mice were randomly divided into normal group， model group， positive control group （Aspirin enteric-coated tablet， 20 mg/kg）， AS-Ⅳ low- and high-dose groups （10， 20 mg/kg）， with 6 mice in each group. Mice in each group were administered the corresponding drug/normal saline via gavage/intraperitoneal injection， once a day， for 14 consecutive days. Except for normal group， other groups were intraperitoneally injected with LPS （250 μg/kg） 1 hour after daily administration of the drug/normal saline to establish neuroinflammation model. Serum levels of IL-6 and TNF-α were measured 2 h after the last medication； histopathological morphology of cerebral tissue in mice were observed； the co-localization of inducible nitric oxide synthase （iNOS）/ionized calcium binding adapter molecule 1 （Iba1） and CD206/Iba1 in the cerebral cortex region of mice was observed； the expressions of proteins related to the nuclear factor-κB （NF-κB）/mitogen-activated protein kinase （MAPK） signaling pathway in brain tissue of mice were also determined， including NF-κB p65， phosphorylated NF-κB p65（p-NF-κB p65）， p38 MAPK， phosphorylated p38 MAPK （p-p38 MAPK）， extracellular signal-regulated kinase （ERK）， and phosphorylated ERK （p-ERK）. RESULTS In the cell experiments， compared with control group， the levels of IL-6， TNF- α and NO in the cell supernatant of the LPS group were increased significantly （P＜0.05）； compared with LPS group， the levels of IL-6， TNF-α and NO were decreased significantly in the administration groups （P＜0.05）. In the animal experiments， compared with the normal group， the serum levels of IL-6 and TNF- α， the number of iNOS/Iba1 co-localization positive cells in the cerebral cortex， and the phosphorylation levels of p38 MAPK， NF- κB p65 and ERK proteins in brain tissue were all significantly increased/elevated in model group （P＜0.05）； the number of CD206/ Iba1 co-localization positive cells in the cerebral cortex region significantly decreased （P＜0.05）. The neurons in the cerebral cortex and the CA3 region of the hippocampus displayed a disordered arrangement. Compared with model group， above quantitative indexes of mice were all reversed significantly in administration groups （P＜0.05）； the neuronal cells in the cerebral cortex and the CA3 region of the hippocampus exhibited a relatively orderly arrangement. CONCLUSIONS AS-Ⅳ may inhibit the activation of the NF-κB/MAPK signaling pathway， promote the M2-type polarization of microglia， and thereby suppress neuroinflammatory responses.

Objective: To explore the treatment plan for severe papillary defects in the aesthetic zone caused by severe periodontitis, providing a reference for clinical practice. Methods : A patient with severe periodontitis leading to severe papillary defects in the upper anterior teeth from 12 to 23 was treated using interdental tunnel technique combined with personalized connective tissue grafting for periodontal plastic surgery, and stable soft tissue augmentation was achieved. Resin restoration was conducted to modify the crown shape of the aesthetic zone teeth, reconstruct white aesthetics, guide the shaping of the gingival papillae, reduce “black triangles,” and enhance the patient’s confidence in smiling. Results : The patient’s periodontal condition and the regeneration of soft tissues in the aesthetic zone were good, and the smile aesthetics were restored. After a 3-year follow-up, the gingival morphology, color, and texture were good, and the effect was stable. The literature review indicates that for papillary defects in the aesthetic zone, analysis should be conducted based on the following aspects: whether a defect is present in periodontal hard and soft tissues, crown shape, and the distance from the most apical part of the crown contact area to the top of the alveolar crest. Based on the analysis of aesthetic defects and surgical indications, a personalized treatment plan should be designed. Conclusion For patients with obvious papillary defects in the aesthetic zone due to the reduction of periodontal support tissues caused by severe periodontitis, factors such as periodontal hard and soft tissue defects, crown shape, and the distance from the most apical part of the crown contact area to the top of the alveolar crest should be fully considered, and a personalized treatment plan should be formulated after multidisciplinary joint consultation.

Purpose: Although some medications trigger the worsening of myasthenia gravis (MG), their clinical influence on patients with MG has not been significantly evaluated. We aimed to investigate whether the risk of clinical worsening of MG increases after administering cautionary drugs in patients with MG. Materials and Methods: This retrospective case-control study was based on the medical records of patients diagnosed with MG between 2007 and 2020. We analyzed the risk of MG worsening in patients exposed to cautionary drugs during the risk period, defined as 6 months from the first exposure to cautionary drugs. The risk of MG worsening in the exposed patients was compared to that in the non-exposed patients, who were individually matched in a 1:1 ratio with exposed cases for sex, age, thymoma, and autoantibodies. Results: Of the 2002 patients diagnosed with MG, 552 (27.6%) were exposed to cautionary drugs. Neuromuscular blocking agents (320 patients) and beta blockers (66123 person-days) were the most frequently prescribed medications. After exact matching, 220 exposed and 220 non-exposed patients were enrolled. The incidence rate of clinical worsening during the risk period was significantly higher in the exposed patients than in the non-exposed patients (odds ratio=4.09; 95% confidence interval, 1.88–8.90;p<0.001). Clinical worsening was observed in 31 (14.1%) of the exposed patients and in 8 (3.6%) of the non-exposed patients. Conclusion The administration of cautionary drugs increased the risk of clinical worsening in patients with MG. Clinicians should be aware of this risk when cautionary drugs need to be administered.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

Purpose: Although some medications trigger the worsening of myasthenia gravis (MG), their clinical influence on patients with MG has not been significantly evaluated. We aimed to investigate whether the risk of clinical worsening of MG increases after administering cautionary drugs in patients with MG. Materials and Methods: This retrospective case-control study was based on the medical records of patients diagnosed with MG between 2007 and 2020. We analyzed the risk of MG worsening in patients exposed to cautionary drugs during the risk period, defined as 6 months from the first exposure to cautionary drugs. The risk of MG worsening in the exposed patients was compared to that in the non-exposed patients, who were individually matched in a 1:1 ratio with exposed cases for sex, age, thymoma, and autoantibodies. Results: Of the 2002 patients diagnosed with MG, 552 (27.6%) were exposed to cautionary drugs. Neuromuscular blocking agents (320 patients) and beta blockers (66123 person-days) were the most frequently prescribed medications. After exact matching, 220 exposed and 220 non-exposed patients were enrolled. The incidence rate of clinical worsening during the risk period was significantly higher in the exposed patients than in the non-exposed patients (odds ratio=4.09; 95% confidence interval, 1.88–8.90;p<0.001). Clinical worsening was observed in 31 (14.1%) of the exposed patients and in 8 (3.6%) of the non-exposed patients. Conclusion The administration of cautionary drugs increased the risk of clinical worsening in patients with MG. Clinicians should be aware of this risk when cautionary drugs need to be administered.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.