Urinary catheter placement and adverse urinary outcomes with a focus on elevated risk in men with indwelling Foley catheters.

BACKGROUND: Prior studies indicated a link between urinary catheter use and urinary complications, highlighting the need for comprehensive, gender-specific investigations. This study explored the association through a national retrospective cohort, emphasizing gender disparities and long-term outcomes. METHODS: Our study utilized data from the entire population covered by Taiwan's National Health Insurance Research Database from 2000 to 2017. We included 148,304 patients who had undergone Foley catheter placement and their propensity-scores matched controls in the study. We evaluated urinary complications, which encompassed urinary tract cancer, urolithiasis, urethral stricture, obstructive uropathy, reflux uropathy, fistula, diverticulum, caruncle, false passage, prolapsed urethral mucosa, urinary tract rupture, and urinary tract infection. These were assessed using the Fine and Gray sub-distribution proportional hazards model to compare between the Foley and non-Foley groups. Sensitivity analyses were conducted with different matching ratios. RESULTS: In the study, the non-Foley group presented a marginally higher mean age (75.24 ± 10.47 years) than the Foley group (74.09 ± 10.47 years). The mean duration of Foley catheterization was 6.1 ± 4.19 years. Men with Foley catheterization exhibited the highest adjusted sub-distribution hazard ratios for urinary tract cancer (6.57, 95% CI: 5.85-7.37), followed by women with Foley catheterization (4.48, 95% CI: 3.98-5.05), and men without catheterization (1.58, 95% CI: 1.39-1.8) in comparison with women without the procedure. Furthermore, men with Foley catheterization were found to be at the greatest risk for complications such as urolithiasis, urethral stricture, obstructive and reflux uropathy, fistula, diverticulum, caruncle, false passage, prolapsed urethral mucosa, and urinary tract rupture. Conversely, women with urinary catheterization were most susceptible to urinary tract infections. CONCLUSIONS: The evidence confirms that urinary catheterization significantly increases urinary complications, particularly among men. Our study underscores the crucial need for healthcare providers to carefully evaluate the necessity of catheterization, aim to shorten its duration whenever feasible, and strictly adhere to established protocols to minimize complications.

Andrographolide Relieves Post-Operative Wound Pain but Affects Local Angiogenesis.

Andrographolide (Andro), the major constituent of Andrographis paniculata Nees (Acanthaceae), is was known to reduces inflammatory reaction. In the current study, the ability of Andro to reduce pain sensation in a rat post-operative wound model was explored. The hind paws of 18 Sprague-Dawley rats (SD) bearing post-operative wounds received the following three treatments: Saline, Andro via direct injection into the paw (Andro-injected) and Tablet containing Andro + poly (lactic-co-glycolic acid) (PLGA) (Andro-tablet). Von Frey tests assessed mechanical allodynia at 1, 3, 5 h and 1-, 2-, 3-, 4-, and 5-days post-operation. Behavioral analyses were performed to measure reaction threshold and reaction frequencies. Immunoreactivity of p-ERK and GluR1 was examined in the dorsal horn of the spinal cord. Histopathological and immunostaining studies were conducted on paw epidermis to observe the gross morphology and angiogenesis. The threshold for inducing allodynia increased and the reaction frequency reduced in the Andro-injected group compared to the saline-group, at 3 h post-surgery and the effect lasted between 3-4 days. The threshold for inducing pain and reaction frequency for the Andro-tablet group did not differ from the saline-treated group. The levels of p-ERK and GluR1 in the dorsal horn were reduced after Andro treatment. No significant difference in wound healing index was observed between saline and Andro-injected groups, but CD-31 staining showed less angiogenesis in the Andro-injected group. Andro significantly reduced mechanical allodynia compared to saline treatment, both in shorter and longer time frames. Furthermore, Andro influenced the expression of p-ERK and GluR1 in the dorsal horn, and the angiogenesis process in the wound healing area.

Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.

RATIONALE AND OBJECTIVES: Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS: Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS: When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION: Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.

Deferoxamine accelerates endothelial progenitor cell senescence and compromises angiogenesis.

Senescence reduces the circulating number and angiogenic activity of endothelial progenitor cells (EPCs), and is associated with aging-related vascular diseases. However, it is very time-consuming to obtain aged cells (~1 month of repeated replication) or animals (~2 years) for senescence studies. Here, we established an accelerated senescence model by treating EPCs with deferoxamine (DFO), an FDA-approved iron chelator. Four days of low-dose (3 µM) DFO induced senescent phenotypes in EPCs, including a senescent pattern of protein expression, impaired mitochondrial bioenergetics, altered mitochondrial protein levels and compromised angiogenic activity. DFO-treated early EPCs from young and old donors (< 35 vs. > 70 years old) displayed similar senescent phenotypes, including elevated senescence-associated ß-galactosidase activity and reduced relative telomere lengths, colony-forming units and adenosine triphosphate levels. To validate this accelerated senescence model in vivo, we intraperitoneally injected Sprague-Dawley rats with DFO for 4 weeks. Early EPCs from DFO-treated rats displayed profoundly senescent phenotypes compared to those from control rats. Additionally, in hind-limb ischemic mice, DFO pretreatment compromised EPC angiogenesis by reducing both blood perfusion and capillary density. DFO thus accelerates EPC senescence and appears to hasten model development for cellular senescence studies.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.

Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas. Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient. Results: Gross total resection (Simpson grades I-III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003). Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.

Effects and safety of body positioning on back pain after transcatheter arterial chemoembolization in people with hepatocellular carcinoma: A randomized controlled study.

BACKGROUND: People with hepatocellular carcinoma who undergo transcatheter arterial chemoembolization usually experience back pain due to lie supine for at least 4 hours to avoid bleeding and hematoma. Body positioning is an effective and safe method for decreasing back pain in people with transfemoral cardiac catheterization; however, its effects and safety among patients with high bleeding tendency are unknown. OBJECTIVE: To investigate whether body positioning could decrease back pain without increasing the chance of bleeding after transcatheter arterial chemoembolization. DESIGN: A single-blind randomized controlled trial (ClinicalTrials.gov No.: NCT03784469). METHODS: A total of 78 people with liver cancer who had undergone chemoembolization through the femoral artery were enrolled. Each person was randomly assigned to either the control or intervention group (each consisted of 39 participants). The control group received the usual care, remaining flat and lying in a supine position, whereas the intervention group had their positions changed in the second and fourth hour after chemoembolization. Participants' pain level was rated by using numerical rating scale -11 (score from 0 to 10), bleeding was measured by using volume of blood (cc.) in gauze and hematoma size in diameter (cm), and satisfaction was self-rated from 1 to 5. Repeated-measure analysis of variance (ANOVA) was used to compare the difference in pain levels over time within each group and independent t test to compare the mean difference of pain between groups at 5 endpoints, both methods with Bonferroni adjustment. Independent t test, chi-squared test, and Fisher's exact test compared postembolization discomfort, puncture sites bleeding, satisfaction between groups. RESULTS: Significant changes of pain levels over time in both intervention [F(2.93, 111.20)=7.64, p<.001] and control groups [F(2.66, 101.17)=20.55, p<.001]. The intervention group had a significantly lower mean pain score in the second hour (t = -2.838, p = .006) and fourth hour (t = -4.739, p < .001) when patients turning to the side than did the control group lying supine. Furthermore, patients in the intervention group had significantly higher satisfaction than did those in the control group (t = -2.422, p = .018). No hematoma and significant difference of post-procedural bleeding between groups. CONCLUSION: Changing patients' body positions in bed after transcatheter arterial chemoembolization is a safe and effective method of decreasing back pain, and increasing patients' satisfaction, without increasing the complications of bleeding and hematoma. Clinicians should change the positions of people with hepatocellular carcinoma 2 hours after they receive transcatheter arterial chemoembolization.

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas.

OBJECTIVES: A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs. METHODS: Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features. RESULTS: Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001). CONCLUSIONS: Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.

Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

RATIONALE AND OBJECTIVES: Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI. MATERIALS AND METHODS: Two datasets were used. The training set was MRI of 286 patients with unilateral breast cancer. The segmentation was done on the contralateral normal breasts. The ground truth for the breast and fibroglandular tissue (FGT) was obtained by using a template-based segmentation method. The U-net deep learning algorithm was implemented to analyze the training set, and the final model was obtained using 10-fold cross-validation. The independent validation set was MRI of 28 normal volunteers acquired using four different MR scanners. Dice Similarity Coefficient (DSC), voxel-based accuracy, and Pearson's correlation were used to evaluate the performance. RESULTS: For the 10-fold cross-validation in the initial training set of 286 patients, the DSC range was 0.83-0.98 (mean 0.95 ± 0.02) for breast and 0.73-0.97 (mean 0.91 ± 0.03) for FGT; and the accuracy range was 0.92-0.99 (mean 0.98 ± 0.01) for breast and 0.87-0.99 (mean 0.97 ± 0.01) for FGT. For the entire 224 testing breasts of the 28 normal volunteers in the validation datasets, the mean DSC was 0.86 ± 0.05 for breast, 0.83 ± 0.06 for FGT; and the mean accuracy was 0.94 ± 0.03 for breast and 0.93 ± 0.04 for FGT. The testing results for MRI acquired using four different scanners were comparable. CONCLUSION: Deep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI. It may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.

In Vivo Real-Time Discrimination Among Glioma, Infiltration Zone, and Normal Brain Tissue via Autofluorescence Technology.

BACKGROUND: Surgery is the first-line therapy for glioblastoma. There is evidence that extent of resection is significantly associated with patient survival. Unfortunately, optimal surgical resection is usually limited because of the difficulty in discriminating tumor-infiltrated region and normal brain tissue. This study aimed to develop a tool to distinguish between infiltration zone and normal tissue in real time during glioma surgery. METHODS: In an in vivo study, C6 glioma cells were implanted into the left cerebral hemispheres of 6 rats to mimic tumorigenesis. A newly designed optical fiber-embedded needle probe was used to measure the autofluorescence of both cerebral hemispheres at various depths 5 days after the implantation. These rats were then sacrificed, and both cerebral hemispheres were removed for histopathologic analysis. RESULTS: Comparative analyses of corresponding areas by histopathology and autofluorescence revealed highly significant (P < 0.001) differences among the normal tissue, infiltration zone, tumors, and the contralateral cerebral hemispheres. The area of the receiver operating characteristic curve was 0.978, and the sensitivity and specificity of tumor delineation were 93.9% and 94.4%, respectively. CONCLUSIONS: The newly designed in vivo fiber-optic probe can distinguish tumor-infiltration zones from normal brain tissue in this in vivo study. Therefore, it may help neurosurgeons to increase extent of resection without damaging normal brain tissue and thus potentially improve the patients' survival and quality of life.

Anthocyanins from roselle extract arrest cell cycle G2/M phase transition via ATM/Chk pathway in p53-deficient leukemia HL-60 cells.

Cell cycle regulation is an important issue in cancer therapy. Delphinidin and cyanidin are two major anthocyanins of the roselle plant (Hibiscus sabdariffa). In the present study, we investigated the effect of Hibiscus anthocyanins (HAs) on cell cycle arrest in human leukemia cell line HL-60 and the analyzed the underlying molecular mechanisms. HAs extracted from roselle calyces (purity 90%) markedly induced G2/M arrest evaluated with flow cytometry analysis. Western blot analyses revealed that HAs (0.1-0.7 mg mL-1 ) induced G2/M arrest via increasing Tyr15 phosphorylation of Cdc2, and inducing Cdk inhibitors p27 and p21. HAs also induced phosphorylation of upstream signals related to G2/M arrest such as phosphorylation of Cdc25C tyrosine phosphatase at Ser216, increasing the binding of pCdc25C with 14-3-3 protein. HAs-induced phosphorylation of Cdc25C could be activated by ATM checkpoint kinases, Chk1, and Chk2. We first time confirmed that ATM-Chk1/2-Cdc25C pathway as a critical mechanism for G2/M arrest in HAs-induced leukemia cell cycle arrest, indicating that this compound could be a promising anticancer candidate or chemopreventive agents for further investigation. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 1290-1304, 2017.

Tacrolimus suppresses atopic dermatitis-associated cytokines and chemokines in monocytes.

BACKGROUND: Calcineurin inhibitors (CNIs) exhibit remarkable efficacy in atopic dermatitis (AD). Tacrolimus, one type of CNI, is prevalently used to treat AD. AD is a chronic inflammatory disease that exhibits predominant infiltration of T-helper type 2 (Th2) cell in the acute phase and a mixed Th1 and Th0 cell pattern in chronic lesions. Cytokines such as tumor necrosis factor-α (TNF-α), Th2-related chemokines [e.g., macrophage-derived chemokine (MDC)/CCL22 and I-309/CCL1], Th1-related chemokines [e.g., interferon γ-induced protein 10 (IP-10)/CXCL10], and neutrophil chemoattractant growth-related oncogene-α (GRO-α)/CXCL1 are involved in the pathogenesis of AD. However, whether tacrolimus modulates the expression of AD-associated cytokines and chemokines remains unknown. The intracellular mechanisms of tacrolimus are also unclear. METHODS: Human monocytic cell line THP-1 cells were pretreated with tacrolimus and stimulated with lipopolysaccharide (LPS). The MDC, I-309, IP-10, GRO-α, and TNF-α concentrations of the cell supernatants were measured using enzyme-linked immunosorbent assay. Intracellular signaling was investigated using the Western blot analysis. RESULTS: Tacrolimus suppressed the expression of MDC, IP-10, I-309, GRO-α, and TNF-α in LPS-stimulated THP-1 cells in a dose- and time-dependent manner. All three mitogen-activated protein kinase (MAPK) inhibitors and the nuclear factor-κB inhibitor suppressed LPS-induced MDC, I-309, and TNF-α expressions in THP-1 cells. Only MAPK inhibitors suppressed LPS-induced expression of IP-10 and GRO-α. Tacrolimus suppressed the LPS-induced phosphorylation of MAPK-extracellular signal-related kinase (ERK). CONCLUSION: Tacrolimus suppressed LPS-induced MDC, I-309, IP-10, GRO-α, and TNF-α expressions in monocytes through the MAPK-ERK pathway; thus, tacrolimus may yield therapeutic efficacy by modulating AD-associated cytokines and chemokines.

Epigenetic regulation in allergic diseases and related studies.

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.