Asthma, a chronic inflammatory disorder of the airway, has features of both heritability as well as environmental influences which can be introduced in utero exposures and modified through aging, and the features may attribute to epigenetic regulation. Epigenetic regulation explains the association between early prenatal maternal smoking and later asthma-related outcomes. Epigenetic marks (DNA methylation, modifications of histone tails or noncoding RNAs) work with other components of the cellular regulatory machinery to control the levels of expressed genes, and several allergy- and asthma-related genes have been found to be susceptible to epigenetic regulation, including genes important to T-effector pathways (IFN-γ, interleukin [IL] 4, IL-13, IL-17) and T-regulatory pathways (FoxP3). Therefore, the mechanism by which epigenetic regulation contributes to allergic diseases is a critical issue. In the past most published experimental work, with few exceptions, has only comprised small observational studies and models in cell systems and animals. However, very recently exciting and elegant experimental studies and novel translational research works were published with new and advanced technologies investigating epigenetic mark on a genomic scale and comprehensive approaches to data analysis. Interestingly, a potential link between exposure to environmental pollutants and the occurrence of allergic diseases is revealed recently, particular in developed and industrialized countries, and endocrine disrupting chemicals (EDCs) as environmental hormone may play a key role. This review addresses the important question of how EDCs (nonylphenol, 4 octylphenol, and phthalates) influences on asthma-related gene expression via epigenetic regulation in immune cells, and how anti-asthmatic agents prohibit expression of inflammatory genes via epigenetic modification. The discovery and validation of epigenetic biomarkers linking exposure to allergic diseases might lead to better epigenotyping of risk, prognosis, treatment prediction, and development of novel therapies.
Acetylation ; Aging ; Animals ; Anti-Asthmatic Agents ; Asthma ; Biomarkers ; Developed Countries ; Endocrine Disruptors ; Environmental Pollutants ; Epigenomics ; Gene Expression ; Histones ; Hypersensitivity ; Interleukin-13 ; Interleukins ; Methylation ; Prognosis ; Smoke ; Smoking ; Statistics as Topic ; Tail ; Translational Medical Research

The prevalence of asthma and allergic disease has increased worldwide over the last few decades. Many common environmental factors are associated with this increase. Several theories have been proposed to account for this trend, especially those concerning the impact of environmental toxicants. The development of the immune system, particularly in the prenatal period, has far-reaching consequences for health during early childhood, and throughout adult life. One underlying mechanism for the increased levels of allergic responses, secondary to exposure, appears to be an imbalance in the T-helper function caused by exposure to the toxicants. Exposure to environmental endocrine-disrupting chemicals can result in dramatic changes in cytokine production, the activity of the immune system, the overall Th1 and Th2 balance, and in mediators of type 1 hypersensitivity mediators, such as IgE. Passive exposure to tobacco smoke is a common risk factor for wheezing and asthma in children. People living in urban areas and close to roads with a high volume of traffic, and high levels of diesel exhaust fumes, have the highest exposure to environmental compounds, and these people are strongly linked with type 1 hypersensitivity disorders and enhanced Th2 responses. These data are consistent with epidemiological research that has consistently detected increased incidences of allergies and asthma in people living in these locations. During recent decades more than 100,000 new chemicals have been used in common consumer products and are released into the everyday environment. Therefore, in this review, we discuss the environmental effects on allergies of indoor and outside exposure.
Adult ; Asthma ; Child ; Humans ; Hypersensitivity ; Immune System ; Immunoglobulin E ; Incidence ; Inflammation* ; Prevalence ; Respiratory Sounds ; Risk Factors ; Smoke ; Smoking ; Tobacco ; Vehicle Emissions

PURPOSE: Epidermal growth factor receptor (EGFR) T790M mutation serves as an important predictor of osimertinib efficacy. However, little is known about how it works among patients with various timings of T790M emergence and treatment. MATERIALS AND METHODS: Advanced EGFR-mutant lung adenocarcinoma patients with positive T790M mutation in tumor were retrospectively enrolled and observed to determine the outcomes of osimertinib treatment. We evaluated the association between patients’ characteristics and the efficacy of osimertinib treatment, particularly with respect to the timing of T790M emergence and osimertinib prescription. RESULTS: A total of 91 patients were enrolled, including 14 (15.4%) with primary and 77 (84.6%) with acquired T790M mutation. The objective response rate and disease controlratewere 60.9% and 85.1%, respectively. The median progression-free survival (PFS) and overall survival were 11.5 months (95% confidence interval [CI], 9.0 to 14.0) and 30.4 months (95% CI, 11.3 to 49.5), respectively. There was no significant difference in response rate and PFS between primary and acquired T790M populations. In the acquired T790M subgroup, patientswho received osimertinib after T790M had been confirmed by rebiopsy had a longer PFS than those with intercalated treatments between rebiopsy and osimertinib prescription (14.0 months [95% CI, 9.0 to 18.9] vs. 7.2 months [95% CI, 3.7 to 10.8]; adjusted hazard ratio, 0.48 [95% CI, 0.24 to 0.98; p=0.043]). Rebiopsy timing did not influence the outcome. CONCLUSION: Osimertinib prescription with intercalated treatment following rebiopsy but not the timing of T790M emergence influenced the treatment outcome. We suggest that it is better to start osimertinib treatment once T790M mutation has been confirmed by biopsy.
Adenocarcinoma ; Biopsy ; Disease-Free Survival* ; Humans ; Lung ; Prescriptions ; Receptor, Epidermal Growth Factor ; Retrospective Studies ; Treatment Outcome

Alzheimer’s disease (AD) is a neurodegenerative disease with progressive memory loss and the cognitive decline. AD is mainly caused by abnormal accumulation of misfolded amyloid β (Aβ), which leads to neurodegeneration via a number of possible mechanisms such as down-regulation of brain-derived neurotrophic factor-tropomyosin-related kinase B (BDNF-TRKB) signaling pathway. 7 ,8-Dihydroxyflavone (7,8-DHF), a TRKB agonist, has demonstrated potential to enhance BDNF-TRKB pathway in various neurodegenerative diseases. T o expand the capacity of flavones as TRKB agonists, two natural flavones quercetin and apigenin, were evaluated. With tryptophan fluorescence quenching assay, we illustrated the direct interaction between quercetin/ apigenin and TRKB extracellular domain. Employing Aβ folding reporter SH-SY5Y cells, we showed that quercetin and apigenin reduced Aβ-aggregation, oxidative stress, caspase-1 and acetylcholinesterase activities, as well as improved the neurite outgrowth. Treatments with quercetin and apigenin increased TRKB Tyr516 and Tyr817 and downstream cAMP-response-element binding protein (CREB) Ser133 to activate transcription of BDNF and BCL2 apoptosis regulator (BCL2), as well as reduced the expression of pro-apoptotic BCL2 associated X protein (BAX). Knockdown of TRKB counteracted the improvement of neurite outgrowth by quercetin and apigenin. Our results demonstrate that quercetin and apigenin are to work likely as a direct agonist on TRKB for their neuroprotective action, strengthening the therapeutic potential of quercetin and apigenin in treating AD.

Objective: The impact of serotonergic system on obsessive-compulsive disorder (OCD) is well studied. However, the correlation between OC presentations and autonomic nervous system (ANS) is still unclear. Furthermore, whether the correlation might be modulated by serotonin is also uncertain. Methods: We recruited eighty-nine healthy subjects. Serotonin transporter (SERT) availability by [ 123 I]ADAM and heart rate variability (HRV) tests were measured. Symptoms checklist-90 was measured for the OC presentations. The interaction between HRV and SERT availability were calculated and the correlation between HRV and OC symptoms were analyzed after stratified SERT level into two groups, split at medium. Results: The interactions were significant in the factors of low frequency (LF), high frequency (HF), and root mean square of successive differences (RMSSD). Furthermore, the significantly negative correlations between OC symptoms and the above HRV indexes existed only in subjects with higher SERT availability. Conclusion OC symptoms might be correlated with ANS regulations in subjects with higher SERT availability.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.