A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.

Improving deep-learning electrocardiogram classification with an effective coloring method.

Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative approach to ECG classification by incorporating demographic information from patients' medical histories through a colorization technique. Our proposed method maps demographic features onto the (R, G, B) color space through normalized scaling. Each demographic feature corresponds to a distinct color, allowing for different ECG leads to be colored. This approach preserves the relationships between data by maintaining the color correlations in the statistical features, enhancing ECG analytics and supporting precision medicine. We conducted experiments with PTB-XL dataset and achieved 1%-6% improvements in the area under the receiving operator characteristic curve performance compared with other methods for various classification problems. Notably, our method excelled in multiclass and challenging classification tasks. The combined use of color features and the original waveform shape features enhanced prediction accuracy for various deep learning models. Our findings suggest that colorization is a promising avenue for advancing ECG classification and diagnosis, contributing to improved prediction and diagnosis of cardiovascular diseases and ultimately enhancing clinical outcomes.

Effect and mechanism of quercetin or quercetin-containing formulas against COVID-19: From bench to bedside.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the global coronavirus disease 2019 (COVID-19) pandemic since 2019. Immunopathogenesis and thromboembolic events are central to its pathogenesis. Quercetin exhibits several beneficial activities against COVID-19, including antiviral, anti-inflammatory, immunomodulatory, antioxidative, and antithrombotic effects. Although several reviews have been published, these reviews are incomplete from the viewpoint of translational medicine. The authors comprehensively evaluated the evidence of quercetin against COVID-19, both basically and clinically, to apply quercetin and/or its derivatives in the future. The authors searched the PubMed, Embase, and the Cochrane Library databases without any restrictions. The search terms included COVID-19, SARS-CoV-2, quercetin, antiviral, anti-inflammatory, immunomodulatory, thrombosis, embolism, oxidative, and microbiota. The references of relevant articles were also reviewed. All authors independently screened and reviewed the quality of each included manuscript. The Cochrane Risk of Bias Tool, version 2 (RoB 2) was used to assess the quality of the included randomized controlled trials (RCTs). All selected studies were discussed monthly. The effectiveness of quercetin against COVID-19 is not solid due to methodological flaws in the clinical trials. High-quality studies are also required for quercetin-containing traditional Chinese medicines. The low bioavailability and highly variable pharmacokinetics of quercetin hinder its clinical applications. Its positive impact on immunomodulation through reverting dysbiosis of gut microbiota still lacks robust evidence. Quercetin against COVID-19 does not have tough clinical evidence. Strategies to improve its bioavailability and/or to develop its effective derivatives are needed. Well-designed RCTs are also crucial to confirm their effectiveness in the future.

Size-selective adhesion of calcium oxalate monohydrate crystals to lipid membranes.

The retention of calcium oxalate monohydrate (COM) crystals on cell membranes is pivotal in kidney stone formation. However, the mechanisms underlying COM attachment to neutral lipid membranes remain unclear. In this study, we demonstrate that COM exhibits size-selective adhesion to fluid lipid membranes composed of lipids with distinct sizes. Specifically, the (100) facet of COM induces the formation of new domains and establishes strong adhesion in the 18:1 (Δ9-Cis) PC (DOPC) membrane, while the (010) facet induces domains with strong adhesion in the 16:0-14:0 PC membrane. This selectivity is linked to the compatibility of the area per lipid in DOPC with the unit cell area of the (100) facet and the area per lipid in 16:0-14:0 PC with the (010) facet. Our Raman spectroscopic analyses reveal that the lipid acyl chains within these induced domains exhibit a higher degree of ordering compared to the typical fluid state of the membrane. This ordered structural alignment, combined with the lateral size-matching effect, suggests the potential formation of molecular arrays within the lipid bilayer that are in harmony with the lattice dimension of COM. To elucidate the strong adhesion between calcium oxalate and the phospholipid head group in the absence of a direct molecular structural correspondence, we propose that crystal water associated with COM can form hydrogen bonds with the phospholipid head group. Using structure visualization software, we demonstrate the feasibility of such hydrogen bonding networks. The formation of this network could serve to stabilize and enhance the attachment of COM to the lipid membrane. This mediation by water molecules offers a plausible explanation for the pronounced affinity at the interface.

Urinary heavy metals and attention-deficit/hyperactivity symptoms of preschool children: a mixed-exposure analysis.

The neurotoxic effects of certain heavy metals are well established, but only a few studies have investigated the joint effect of concurrent exposure to multiple ones. The study aims to evaluate the association between mixed exposure to neurotoxic metals and the psychosocial behavior of preschool children. Using a stratified sampling strategy, we recruited participants from 105 kindergartens in 41 townships of Taiwan and excluded those with blood lead levels ≥ 3.5 µg/L. The first-morning void urines were collected and analyzed for cadmium, manganese, arsenic, chromium, lead, and nickel concentrations using inductively coupled plasma mass spectrometry. We applied the parentally reported Strengths and Difficulties Questionnaire (SDQ) and Swanson, Nolan, and Pelham IV (SNAP-IV) scales to evaluate the psychosocial behaviors. Multiple linear regressions were utilized to evaluate the associations between each heavy metal and the outcomes, while the mixed effect of concurrent exposure was estimated by using a Quantile g-computation approach. A total of 977 preschool children were included in the study, and the mean (SD) age was 5.7 (0.7) years old. In single pollutant models, we observed adverse effects of urinary manganese, nickel, arsenic, and lead on the specific subsets of SDQ. Furthermore, the combined effect of six heavy metals significantly affected the hyperactivity/inattention symptoms (beta = 0.46, 95% CI: 0.13-0.78, with all metals increased by one quartile), and chromium and lead were the two major contributors. Similar detrimental effects of urinary cadmium and lead were also observed in the SNAP-IV subsets, although the joint effect analysis was not significant. The study provided evidence that concurrent exposure to multiple heavy metals may exert increased risks of hyperactivity/inattention in children compared to single pollutant exposure. Further studies are needed to verify our findings regarding mixed exposure to multiple neurotoxic metals.

Risk of type 2 diabetes after diagnosed gestational diabetes is enhanced by exposure to PM2.5.

BACKGROUND: Air pollution and gestational diabetes mellitus (GDM) are both associated with increased diabetes mellitus (DM) occurrence. However, whether air pollutants modify the effects of GDM on the occurrence of DM has been unknown. This study aims to determine whether the effect of GDM on DM development can be modified by exposure to ambient air pollutants. METHODS: Women with one singleton birth delivery during 2004-14 according to the Taiwan Birth Certificate Database (TBCD) were included as the study cohort. Those newly diagnosed as having DM 1 year or later after childbirth were identified as DM cases. Controls were selected among women without DM diagnosis during follow-up. Personal residence was geocoded and linked with interpolated concentrations of air pollutants into township levels. Conditional logistic regression was used to determine the odds ratio (OR) of pollutant exposure and GDM, adjusting for age, smoking and meteorological variables. RESULTS: There were 9846 women who were newly diagnosed as having DM over a mean follow-up period of 10.2 years. We involved them and the 10-fold matching controls involved in our final analysis. The OR (odds ratio) (95% confidence interval, 95% CI) of DM occurrence per interquartile range increased in particulate matter (PM) smaller than or equal to 2.5 µm (PM2.5) and ozone (O3) was 1.31 (1.22-1.41) and 1.20 (1.16-1.25), respectively. The effects of PM exposure on DM development were significantly higher in the GDM group (OR: 2.46, 95% CI: 1.84-3.30) than in the non-GDM group (OR: 1.30, 95% CI: 1.21-1.40). CONCLUSIONS: Exposure to high levels of PM2.5 and O3 elevates the risk of DM. GDM acted synergistically in DM development with exposure to PM2.5 but not with that to O3.

Comparison of the Outcomes between Systematic Lymph Node Dissection and Lobe-Specific Lymph Node Dissection for Stage I Non-small Cell Lung Cancer.

BACKGROUND: This study compares the surgical and long-term outcomes, including disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS), between lobe-specific lymph node dissection (L-SND) and systematic lymph node dissection (SND) among patients with stage I non-small cell lung cancer (NSCLC). METHODS: In this retrospective study, 107 patients diagnosed with clinical stage I NSCLC undergoing video-assisted thoracic surgery lobectomy (exclusion of the right middle lobe) from January 2011 to December 2018 were enrolled. The patients were assigned to the L-SND (n = 28) and SND (n = 79) groups according to the procedure performed on them. Demographics, perioperative data, and surgical and long-term oncological outcomes were collected and compared between the L-SND and SND groups. RESULTS: The mean follow-duration was 60.6 months. The demographic data and surgical outcomes and long-term oncological outcomes were not significantly different between the two groups. The 5-year OS of the L-SND and SND groups was 82% and 84%, respectively. The 5-year DFS of the L-SND and SND groups was 70% and 65%, respectively. The 5-year CSS of the L-SND and SND groups was 80% and 86%, respectively. All the surgical and long-term outcomes were not statistically different between the two groups. CONCLUSION: L-SND showed comparable surgical and oncologic outcomes with SND for clinical stage I NSCLC. L-SND could be a treatment choice for stage I NSCLC.

Feature-enhanced adversarial semi-supervised semantic segmentation network for pulmonary embolism annotation.

This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism (PE) lesion areas in computed tomography pulmonary angiogram (CTPA) images. In the current study, all of the PE CTPA image segmentation methods were trained by supervised learning. However, when CTPA images come from different hospitals, the supervised learning models need to be retrained and the images need to be relabeled. Therefore, this study proposed a semi-supervised learning method to make the model applicable to different datasets by the addition of a small number of unlabeled images. By training the model with both labeled and unlabeled images, the accuracy of unlabeled images was improved and the labeling cost was reduced. Our proposed semi-supervised segmentation model included a segmentation network and a discriminator network. We added feature information generated from the encoder of the segmentation network to the discriminator so that it could learn the similarities between the prediction label and ground truth label. The HRNet-based architecture was modified and used as the segmentation network. This HRNet-based architecture could maintain a higher resolution for convolutional operations to improve the prediction of small PE lesion areas. We used a labeled open-source dataset and an unlabeled National Cheng Kung University Hospital (NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised learning model, and the resulting mean intersection over union (mIOU), dice score, and sensitivity reached 0.3510, 0.4854, and 0.4253, respectively, on the NCKUH dataset. Then we fine-tuned and tested the model with a small number of unlabeled PE CTPA images in a dataset from China Medical University Hospital (CMUH) (IRB number: CMUH110-REC3-173). Comparing the results of our semi-supervised model with those of the supervised model, the mIOU, dice score, and sensitivity improved from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively. In conclusion, our semi-supervised model can improve the accuracy on other datasets and reduce the labor cost of labeling with the use of only a small number of unlabeled images for fine-tuning.

Hospital Occupational Health Service Network and Reporting Systems in Taiwan From 2008 to 2021.

OBJECTIVE: To evaluate the provision of occupational health services at hospitals following the establishment of the Network of Occupational Disease and Injury Service in 2007. METHODS: A review of occupational health services during 2008 to 2021 was conducted. The medical practice-based reporting system was compared with the database of compensated occupational diseases (ODs) from Taiwan's Labor Insurance. RESULTS: First-time outpatient visits for occupational evaluation and total visits to Network of Occupational Disease and Injury Service health care institutions increased from 1777 and 9435 to 12,092 and 23,210, respectively. Reported ODs increased from 1626 to 2043 with a peak of 2791. Up to 1380 workers evaluated for work resumption in 2021. Similar to the increasing trend in reported ODs, the number of compensated ODs increased over years. CONCLUSION: The health service needs of Taiwanese workers from OD evaluation to vocational rehabilitation are increasing.

Exposure to Air Pollution and Survival in Follow-Up after Hepatocellular Carcinoma.

Introduction: Air pollutants are classified as carcinogens by the International Agency for Research on Cancer. Long-term exposure to ambient particulate matter with an aerodiameter of 2.5 µm or lower (PM2.5) has been reported to be linked with increased mortality due to hepatocellular carcinoma (HCC). However, the effects of air pollutants other than PM2.5 on HCC-related mortality have not been fully investigated. Accordingly, we conducted this study to assess the effect of long-term exposure to air pollutants (PM2.5 and nitrogen dioxide [NO2]) on HCC-related mortality. Method: In 2005, the Taiwan Liver Cancer Network (TLCN) was established by the National Research Program for Genomic Medicine to recruit liver cancer patients from 5 major medical centers in northern, central, and southern Taiwan. The TLCN had successfully recruited 9,344 patients by the end of 2018. In this study, we included 1,000 patients randomly sampled from the TLCN to assess the effect of exposure to air pollutants on HCC mortality after HCC diagnosis. Daily averages of PM2.5 and NO2 concentrations were retrieved from 77 air quality-monitoring stations and interpolated to the townships of patients' residences by using the Kriging method. The effect of air pollutants on HCC survival was assessed using a Cox proportional hazards model. Results: A total of 940 patients were included in the analysis. After adjusting for potential confounders and mutually adjusting for co-pollutants, we observed that the hazards ratio (95% confidence interval) for HCC-related mortality for every 1-µg/m3 increase in PM2.5 concentration was 1.11 (1.08-1.14) and that for every 1-ppb increase in NO2 concentration was 1.08 (1.03-1.13). Conclusion: Our study suggests that long-term exposure to PM2.5 and NO2 was associated with decreased survival time in patients with HCC in Taiwan.

Novel Paired Cell Lines for the Study of Lipid Metabolism and Cancer Stemness of Hepatocellular Carcinoma.

There are few well-characterized syngeneic murine models for hepatocellular carcinoma (HCC), which limits immunological studies and the development of immunotherapies for HCC. We previously established an oncogene-induced spontaneous HCC mouse model based on transposon-mediated oncogene (AKT and NRASV12) insertion into the genome of hepatocytes to induce tumorigenesis. Two tumor clones with different levels of lipid droplets (LDs) showed similar in vitro growth but distinctive in vivo phenotypes, including divergent proliferative capability and varying induction of myeloid-derived suppressor cells (MDSCs). The two clones showed distinct gene expression related to lipid metabolism, glycolysis, and cancer stemness. Endogenous fatty acid (FA) synthesis and exogenous monounsaturated fatty acid (MUFA) consumption promoted both tumor proliferation and cancer stemness, and upregulated c-Myc in the HCC cell lines. Moreover, the LDhi HCC cell line expressed a higher level of type II IL-4 receptor, which promoted tumor proliferation through binding IL-4 or IL-13. The chromosomal DNA of two tumor clones, NHRI-8-B4 (LDhi) and NHRI-1-E4 (LDlo) showed five identical AKT insertion sites in chromosomes 9, 10, 13, 16 and 18 and two NRAS integration sites in chromosomes 2 and 3. Herein, we describe two novel HCC cell lines with distinct features of lipid metabolism related to cancer stemness and differential interplay with the immune system, and present this syngeneic HCC mouse model as a practical tool for the study of cancer stemness and discovery of new therapies targeting liver cancers.

Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy.

Drug inventory management is an important part of hospital management. The large amounts of drug data in hospitals bring challenges to optimizing the setting values for the safety stock and the maximum inventory of each drug. This study combined a two-stage clustering method with an inventory policy (s, S) and established a simulation optimization model for the case hospital's outpatient pharmacy. This research used the simulation optimization software Arena OptQuest, developed by Rockwell Automation Inc (Rockwell Automation, Coraopolis, PA, USA), in order to determine the minimum and maximum values (s, S) of the best stock amounts for each drug under the considerations of cost and related inventory constraints. The research results showed that the minimum and maximum inventory settings for each drug in the simulation model were better than those set by the case outpatient pharmacy system. The average inventory cost was reduced by 55%, while the average inventory volume was reduced by 68%. The proposed method can improve management efficiency and inventory costs of hospital pharmacies without affecting patient services and increasing the inventory turnover rate of the drugs.

Living proximity to petrochemical industries and the risk of attention-deficit/hyperactivity disorder in children.

Evidence regarding the negative neurodevelopmental effects of compound exposure to petrochemicals remains limited. We aimed to evaluate the association between exposure to petrochemical facilities and generated emissions during early life and the risk of attention-deficit/hyperactivity disorder (ADHD) development in children. We conducted a population-based birth cohort study using the 2004 to 2014 Taiwanese Birth Certificate Database and verified diagnoses of ADHD using the National Health Insurance Database. The level of petrochemical exposure in each participant's residential township was evaluated using the following 3 measurements: distance to the nearest petrochemical industrial plant (PIP), petrochemical exposure probability (accounting for monthly prevailing wind measurements), and monthly benzene concentrations estimated using kriging-based land-use regression models. We applied Cox proportional hazard models to evaluate the association. During the study period, 48,854 out of 1,863,963 children were diagnosed as having ADHD. The results revealed that residents of townships in close proximity to PIPs (hazard ratio [HR] = 1.20, 95% confidence interval [CI]: 1.16-1.23, <3 vs. ≥10 km), highly affected by petrochemical-containing prevailing winds (HR = 1.12, 95% CI: 1.08-1.16, ≥40% vs. <10%), and with high benzene concentrations (HR = 1.26, 95% CI: 1.23-1.29, ≥0.75 vs. <0.55 ppb) were consistently associated with the increased risk of ADHD development in children. The findings of the sensitivity analysis remained robust, particularly for the 2004 to 2009 birth cohort and for models accounting for a longer duration of postnatal exposure. This work provided clear evidence that living near petrochemical plants increases the risk of ADHD development in children. Further studies are warranted to confirm our findings.

Attention deficit hyperactivity disorder among children related to maternal job stress during pregnancy in Taiwan: a prospective cohort study.

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral disorders. Although studies have suggested relationships between ADHD in children and maternal psychosocial stress during pregnancy, little is known about the effects of work-related mental stress. Considering the increasing number of pregnant women who continue to work during the gestation period, this study investigated whether work-related stress during pregnancy is related to offspring ADHD. METHODS: The Taiwan Birth Cohort Study followed selected representative mother-infant pairs in a face-to-face interview since a child was 6 months old. A total of 10,556 working pregnant women who completed follow-up 8 years later were included. Whether the 8-year-old child had ever received a diagnosis of ADHD were inquired. Self-reported job stress during pregnant period was obtained 6 months after delivery. Factors including perinatal and socioeconomic factors as well as the mother's job conditions were further analyzed with logistic regression. RESULTS: Among those who continued working during pregnancy, 3850 (36.5%) mothers reported having job stress during pregnancy, and 210 (2.0%) of the children were diagnosed as having ADHD before 8 years of age. Compared with mothers who reported no job stress, the adjusted odds ratio of child ADHD was 1.91 (95% CI 1.21-3.07) for mothers with "very stressful" jobs during pregnancy and 1.53 (95% CI 1.04-2.25) for mothers with "rather stressful" jobs. CONCLUSION: Among pregnant female workers, higher levels of job stress were related to the higher occurrence of ADHD in their children.

Proximity to petrochemical industrial parks and risk of chronic glomerulonephritis.

This study determined whether individuals residing near petrochemical industrial parks (PIPs) have a higher risk of chronic glomerulonephritis (CGN). We performed population-based 1:4 case-control study by using Taiwan's National Health Insurance Research Database from 2000 to 2016. The subjects were aged 20-65 years, residing in western Taiwan, and did not have a history of any renal or urinary system disease in 2000. The case cohort included those who had at least three outpatient visits or one hospitalization between 2001 and 2016 with codes for CGN as per International Classification of Diseases (ICD)-Ninth and Tenth Revisions. Controls were randomly sampled age-, sex-, and urbanization-matched individuals without renal and urinary system diseases. Petrochemical exposure was evaluated by the distance to the nearest PIP of the residential township, and petrochemical exposure probability was examined considering the monthly prevailing wind direction. Conditional logistic regression was used to determine the association between petrochemical exposure and CGN risk. A total of 320,935 subjects were included in the final analysis (64,187 cases and 256,748 controls). After adjustment for potential confounders, living in townships within a 3-km radius of PIPs was associated with a higher risk of CGN (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [CI] = 1.28-1.37). Compared with townships more than 20 km away from PIPs, those within 10 km of PIPs were associated with significantly increased risks of CGN in a dose-dependent manner. When prevailing wind was considered, townships with high exposure probability were associated with a significantly increased risk of CGN. We found that those residing near PIPs or with high petrochemical exposure probability had a higher risk of CGN. These findings highlight the need for monitoring environmental nephrotoxic substances and the renal health of residents living near PIPs.

Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.

BACKGROUND: Rapid on-site cytologic evaluation (ROSE) helps to improve the diagnostic accuracy in endobronchial ultrasound (EBUS) procedures. However, cytologists are seldom available to perform ROSE in many institutions. Recent studies have investigated the application of deep learning in cytologic image analysis. As such, the present study analyzed lung cytologic images obtained by EBUS procedures, and employed deep-learning methods to distinguish between benign and malignant cells and to semantically segment malignant cells. METHODS: Ninety-seven patients who underwent 104 EBUS procedures were enrolled. Four hundred and ninety-nine lung cytologic images obtained via ROSE, including 425 malignant and 74 benign, and most malignant were lung adenocarcinoma (64.3%). All the images were used to train a residual network model with 101 layers (ResNet101), with suitable hyperparameters selected to classify benign and malignant lung cytologic images. An HRNet model was also employed to mark the area of malignant cells. Automatic patch-cropping was adopted to facilitate dataset preparation. RESULTS: Malignant cells were successfully classified by ResNet101 with 98.8% classification accuracy, 98.8% sensitivity, and 98.8% specificity in patch-based classification; 95.5% classification accuracy in image-based classification; and 92.9% classification accuracy in patient-based classification. Malignant cell area was successfully marked by HRNet with a mean intersection over union of 89.2%. The automatic cropping method enabled the system to complete diagnosis within 1 s. CONCLUSIONS: This is the first study to combine lung cytologic image deep-learning classification with semantic segmentation. The model was optimized for high accuracy and the automatic cropping facilitates the clinical application of our model. The success in both lung cytologic images classification and semantic segmentation on our dataset shows a promising result for clinical application in the future.

An Advanced Noise Reduction and Edge Enhancement Algorithm.

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.

Maternal proximity to petrochemical industrial parks and risk of premature rupture of membranes.

BACKGROUND: Living near petrochemical industries has been reported to increase the risks of adverse birth outcomes, such as low birth weight and preterm delivery. However, evidence regarding the role of petrochemical exposure in pregnancy complications remains limited. This study evaluated the association between maternal proximity to petrochemical industrial parks (PIPs) during pregnancy and the occurrence of premature rupture of membranes (PROM). METHODS: We performed a population-based 1:3 case-control study by using the 2004-2014 Taiwanese Birth Certificate Database. Birth records reported as stillbirth or bearing congenital anomalies were excluded. Cases were newborns reported to have PROM, whereas controls were randomly sampled from those without any pregnancy complications by matching birth year and urbanization index of the residential township. The proximity to PIPs was evaluated by calculating the distance to the nearest PIP of the maternal residential township during pregnancy. Furthermore, petrochemical exposure opportunity, accounting for monthly prevailing wind direction, was quantified during the entire gestational period. We applied conditional logistic regression models to evaluate the associations. RESULTS: In total, 29371 PROM cases were reported during the study period, with a corresponding 88113 healthy controls sampled. The results revealed that living within a 3-km radius of PIPs during pregnancy would increase the risk of PROM (odds ratio [OR] = 1.76, 95% CI: 1.66-1.87). Furthermore, compared with the lowest exposed group, those with high petrochemical exposure opportunity had a significantly increased risk of PROM occurrence (OR = 1.69-1.75). The adverse effects remained robust in the subgroup analysis for both term- and preterm-PROM. CONCLUSIONS: The results of the present work provide evidence that living near PIPs during pregnancy would increase the risk of PROM, and additional studies are warranted to confirm our findings.

Hyperactivity disorder in children related to traffic-based air pollution during pregnancy.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurobehavioral disorders in childhood globally. Between the two components of ADHD, hyperactivity disorder is more prevalent than inattention during early childhood. Although some investigations have implied a relationship between childhood ADHD and gestational exposure to air pollution, the evidence is limited. The aim of this study was to investigate the association between gestational exposure to air pollution exposure and hyperactivity disorder in childhood in a population-based birth cohort. METHODS: The Taiwan Birth Cohort Study started from all deliveries of Taiwan in 2005 by the birth registry, and recruited representative 12% of all mother-infant pairs by two-stage stratified sampling. At age of 8 years in each child, their main caretaker was inquired whether the child had ever received a hyperactivity diagnosis from a physician or other specialist, like special needs educator. Exposure to air pollutants during gestation was estimated through ordinary kriging based on data from air monitoring stations of Environmental Protection Administration, Taiwan. Logistic regression was used to determine adjusted odds ratios (aORs) of hyperactivity disorder in relation to air pollutants. RESULTS: A total of 16,376 mother-infant pairs were included in the final analysis; 374 (2.3%) of the children had received a diagnosis of hyperactivity before 8 years of age. The occurrence of hyperactivity was significantly related to prenatal nitrogen oxide (NOx), but not to particulate matter 10 µm or less in diameter or sulfur dioxide. Further analysis to separate effects by nitrogen dioxide (NO2) and/or nitric oxide (NO) showed that only NO was significantly related to hyperactivity [aOR per interquartile range (3.14 ppb): 1.26, 95% confidence interval: 1.09-1.46]. CONCLUSIONS: In conclusion, our study found childhood hyperactivity disorder to be positively associated with prenatal NO exposure. Further confirmation on potential hazardous effects of NO and investigation on potential mechanisms are warranted.

Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction.

BACKGROUND: Ambient air pollutants can increase cardiovascular mortality. One possible mechanism is the effect on the autonomic balance of the cardiovascular system. Studies on acute effects of particulate matter (PM) exposure on heart rate variability (HRV), a surrogate marker for autonomic balance, in patients with prior myocardial infarction (MI) revealed inconsistent results. METHOD: We prospectively enrolled participants with acute MI. These participants received a 24-hour Holter electrocardiography examination and echocardiography six months after the index MI. Linear [standard deviation of all normal to normal intervals, standard deviation of NN intervals (SDNN), and a low-frequency to high-frequency ratio (LF/HF)] and non-linear parameters of heart rate variability [multiscale entropy (MSE)] were calculated to show autonomic balance. Data for PM2.5, PM2.5-10, and PM10, were obtained from a fixed-site station in Taiwan. Linear mixed effect models were used to estimate acute effects (within 0-3 days) of PM exposure (per 10 µg/m3) on heart rate variability. RESULTS: A total of 90 participants were enrolled in this study with a mean age of 58.7 (13.3) and 83 (92.2%) male participants. Traditional HRV parameters, SDNN and LF/HF, were positively correlated with two-day lagged PM2.5-10 and PM10 [adjusted beta coefficient: SDNN: 130.3 and 58.5; LH/HF: 0.32 and 0.21 (all p < or = 0.01)]. MSE slopes 1-5 were negatively correlated with same-day PM2.5-10 and PM10 (adjusted beta coefficient -0.011 (p = 0.01) and -0.005 (p = 0.02), respectively). The left ventricular ejection fraction was negatively correlated with one-day lagged PM2.5-10, and PM10 (adjusted beta coefficient -0.49 and -0.4, respectively; both p < 0.05), after adjusting for MI size. CONCLUSION: Our results suggest that coarse PM may acutely affect cardiac autonomic balance. MSE is a sensitive marker for detecting changes in autonomic imbalance in patients with prior MI following acute PM exposure.