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.

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.

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.

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.

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.

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.

Periconceptional exposure to air pollution and congenital hypospadias among full-term infants.

BACKGROUND: Evidence regarding whether prenatal exposure to air pollution increases the risk of hypospadias remains limited. The aim of the study is to evaluate the association between exposure to ambient air pollution during early pregnancy and the incidence of hypospadias. METHODS: We conducted a 1:10 case-control study using the Taiwanese Birth Registry database. Male full-term infants reported to have hypospadias were defined as cases, and controls were randomly selected from male full-term infants without any congenital anomaly. The monthly average of ambient air pollutants, including PM10, PM2.5, PM2.5-10, NO2, NOx, O3, and O3 8-h maximum, from 3 months before conception to 6 months post conception was retrieved from air quality monitoring stations and interpolated to the level of township using the kriging method. Multivariable logistic regression models were utilized to evaluate the associations. RESULTS: A total of 200 hypospadias cases, with 2000 healthy controls sampled, were reported during 2007-2014. The results revealed that PM2.5 exposure during the first 3 months after conception (odds ratio [OR] = 1.29, 95% confidence interval [CI]: 1.01-1.65, per interquartile range [IQR] = 15.6 µg/m3) and O3 exposure during the first month after conception (OR = 1.40, 95% CI: 1.08-1.82, per IQR = 8.0 ppb) were associated with a higher incidence of hypospadias. CONCLUSIONS: The results of the study suggest that early gestational exposure to ambient air pollution increases the risk of hypospadias among full-term infants.

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.

Association between air pollution exposure and diabetic retinopathy among diabetics.

BACKGROUND: Exposure to air pollution has been linked to adverse effects on vascular diseases. However, the effects of air pollution exposure on diabetic retinopathy (DR), a vascular disease, have not been studied. OBJECTIVE: To determine the association of ambient air pollution exposure with DR risk. METHODS: Patients newly diagnosed as having diabetes mellitus (DM) during 2003-2012 from Longitudinal Health Insurance Database 2005), a subset of National Health Insurance Research Database, were included as the study cohort. Newly diagnosed DR patients one year or later after DM diagnosis were identified as cases. Kriging was used to interpolate yearly concentrations of air pollutants at township levels and linked with every individual's residence in each year; average concentrations during the follow-up period were then calculated as personal exposure. Conditional logistic regressions with adjustments for age at DM diagnosis and comorbidities were applied. RESULTS: Of newly diagnosed DM cases during 2003-2012, 579 were newly diagnosed as having DR over a mean follow-up period of 5.6 years. The Odds ratio (95% confidence interval) of DR occurrence for every 10-µg/m3 increase in particulate matter with ≤2.5 and 2.5-10-µm diameter was 1.29 (1.11-1.50) and 1.37 (1.17-1.61), respectively. CONCLUSION: In patients with DM, the higher particulate matter exposure, the higher is the DR risk.

Unraveling the Molecular Mechanism of Traditional Chinese Medicine: Formulas Against Acute Airway Viral Infections as Examples.

Herbal medicine, including traditional Chinese medicine (TCM), is widely used worldwide. Herbs and TCM formulas contain numerous active molecules. Basically, they are a kind of cocktail therapy. Herb-drug, herb-food, herb-herb, herb-microbiome, and herb-disease interactions are complex. There is potential for both benefit and harm, so only after understanding more of their mechanisms and clinical effects can herbal medicine and TCM be helpful to users. Many pharmacologic studies have been performed to unravel the molecular mechanisms; however, basic and clinical studies of good validity are still not enough to translate experimental results into clinical understanding and to provide tough evidence for better use of herbal medicines. There are still issues regarding the conflicting pharmacologic effects, pharmacokinetics, drug interactions, adverse and clinical effects of herbal medicine and TCM. Understanding study validation, pharmacologic effects, drug interactions, indications and clinical effects, adverse effects and limitations, can all help clinicians in providing adequate suggestions to patients. At present, it would be better to use herbs and TCM formulas according to their traditional indications matching the disease pathophysiology and their molecular mechanisms. To unravel the molecular mechanisms and understand the benefits and harms of herbal medicine and TCM, there is still much work to be done.

Risk factors for asthma occurrence in children with early-onset atopic dermatitis: An 8-year follow-up study.

BACKGROUND: Children with early-onset atopic dermatitis (AD) are at substantial risk of developing asthma later in life, and identifying the critical window of detrimental exposure is advantageous for implementing preventive actions. The aim of this study was to evaluate the role of exposure to environmental modifiers during pregnancy and early childhood in asthma occurrence in an infantile AD cohort. METHODS: Eligible study participants were selected from the Taiwan Birth Cohort Study, which enrolled 24 200 newborns in 2005. We enrolled those cases who had been diagnosed as having AD before 3 years of age and followed them up till age 8. We excluded those ever diagnosed with asthma before AD onset. The dependent variable was defined in terms of whether the participant was diagnosed as having asthma before 8 years of age. We applied logistic regression models to evaluate the risks of exposure to different determinants in asthma occurrence. RESULTS: A total of 1549 children with AD had completed the 8-year follow-up, and 334 (21.6%) of them had asthma. The results revealed that male sex, lower birth order, maternal asthma history, maternal obesity before pregnancy, and environmental tobacco smoke exposure before 3 years of age were significant risk factors for further development of asthma. Furthermore, food allergy during early life, lower respiratory tract infection, and longer durations of symptomatic AD influenced asthma development later in life. CONCLUSIONS: The findings confirmed the critical determinants for asthma occurrence in infantile AD, which may enable a more personalized approach to the prevention of asthma.

The SDN Approach for the Aggregation/Disaggregation of Sensor Data.

In many Internet of Things (IoT) applications, large numbers of small sensor data are delivered in the network, which may cause heavy traffics. To reduce the number of messages delivered from the sensor devices to the IoT server, a promising approach is to aggregate several small IoT messages into a large packet before they are delivered through the network. When the packets arrive at the destination, they are disaggregated into the original IoT messages. In the existing solutions, packet aggregation/disaggregation is performed by software at the server, which results in long delays and low throughputs. To resolve the above issue, this paper utilizes the programmable Software Defined Networking (SDN) switch to program quick packet aggregation and disaggregation. Specifically, we consider the Programming Protocol-Independent Packet Processor (P4) technology. We design and develop novel P4 programs for aggregation and disaggregation in commercial P4 switches. Our study indicates that packet aggregation can be achieved in a P4 switch with its line rate (without extra packet processing cost). On the other hand, to disaggregate a packet that combines N IoT messages, the processing time is about the same as processing N individual IoT messages. Our implementation conducts IoT message aggregation at the highest bit rate (100 Gbps) that has not been found in the literature. We further propose to provide a small buffer in the P4 switch to significantly reduce the processing power for disaggregating a packet.

Gestational diabetes mellitus was related to ambient air pollutant nitric oxide during early gestation.

BACKGROUND: Ambient air pollution has been linked to the risk of gestational diabetes mellitus (GDM). However, evidence of this association is limited, and no study has examined the effects of nitric oxide (NO). OBJECTIVE: This study investigated the association between air pollution exposure during gestation and GDM. METHODS: The Taiwan Birth Cohort Study database was used to examine the association between the risk of GDM and all routinely monitored air pollutants among 21,248 women who were pregnant during 2004-2005. We further employed a two-pollutant model for confirming the effect of each pollutant on GDM. RESULTS: After the exclusion criteria were applied, 19,606 women were included in the final analysis. Among them, 378 (1.9%) had been diagnosed as having GDM. These women were older and had higher BMIs than the women without GDM. The risks of GDM onset were significantly associated with NO exposure during the first [adjusted OR (aOR): 1.05, 95% confidence interval (CI): 1.02-1.08] and second (aOR: 1.05, 95%CI: 1.02-1.08) trimesters. Under the two-pollutant model, the effect of NO exposure was also significant during the first (aOR: 1.05, 95%CI: 1.02-1.08) and second (aOR: 1.05, 95%CI: 1.02-1.09) trimesters. CONCLUSION: The results indicated that exposure to higher NO levels during pregnancy increases the risk of GDM.

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.

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.

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.

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.

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.