A Wearable Thin-Film Hydrogel Laser for Functional Sensing on Skin.

Flexible photonics offers the possibility of realizing wearable sensors by bridging the advantages of flexible materials and photonic sensing elements. Recently, optical resonators have emerged as a tool to improve their oversensitivity by integrating with flexible photonic sensors. However, direct monitoring of multiple psychological information on human skin remains challenging due to the subtle biological signals and complex tissue interface. To tackle the current challenges, here, we developed a functional thin film laser formed by encapsulating liquid crystal droplet lasers in a flexible hydrogel for monitoring metabolites in human sweat (lactate, glucose, and urea). The three-dimensional cross-linked hydrophilic polymer serves as the adhesive layer to allow small molecules to penetrate from human tissue to generate strong light--matter interactions on the interface of whispering gallery modes resonators. Both the hydrogel and cholesteric liquid crystal microdroplets were modified specifically to achieve high sensitivity and selectivity. As a proof of concept, wavelength-multiplexed sensing and a prototype were demonstrated on human skin to detect human metabolites from perspiration. These results present a significant advance in the fabrication and potential guidance for wearable and functional microlasers in healthcare.

Residence time distribution in continuous virus filtration.

Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( R 2 > ${R}^{2}\gt $ 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved R 2 > ${R}^{2}\gt $ 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

BACKGROUND: Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE: To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS: A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS: We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION: Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Autonomous Microlasers for Profiling Extracellular Vesicles from Cancer Spheroids.

Self-propelled micro/nanomotors are emergent intelligent sensors for analyzing extracellular biomarkers in circulating biological fluids. Conventional luminescent motors are often masked by a highly dynamic and scattered environment, creating challenges to characterize biomarkers or subtle binding dynamics. Here we introduce a strategy to amplify subtle signals by coupling strong light-matter interactions on micromotors. A smart whispering-gallery-mode microlaser that can self-propel and analyze extracellular biomarkers is demonstrated through a liquid crystal microdroplet. Lasing spectral responses induced by cavity energy transfer were employed to reflect the abundance of protein biomarkers, generating exclusive molecular labels for cellular profiling of exosomes derived from 3D multicellular cancer spheroids. Finally, a microfluidic biosystem with different tumor-derived exosomes was employed to elaborate its sensing capability in complex environments. The proposed autonomous microlaser exhibits a promising method for both fundamental biological science and applications in drug screening, phenotyping, and organ-on-chip applications.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Obesity as a clinical predictor for severe manifestation of dengue: a systematic review and meta-analysis.

BACKGROUND: Severe dengue often leads to poor clinical outcomes and high mortality; as a result, it is of vital importance to find prognostic factors associated with the severe form of dengue. Obesity is known to deteriorate many infectious diseases due to impaired immune responses. Several studies have suggested that obese patients with dengue infection tend to have more severe manifestations with poorer prognosis. However, a firm conclusion could not be drawn due to the varied results of these studies. Here, we aimed to conduct a systematic review and meta-analysis to investigate the association between obesity and dengue severity. METHODS: A literature search for relevant studies was conducted in PubMed, Embase, Ovid Medline and Cochrane from inception to September 9, 2022. The two main keywords were "dengue" and "obesity". Mantel-Haenszel method and random effects model was used to analyze the pooled odds ratio with 95% confidence intervals. RESULTS: A total of 15 article involving a total of 6,508 patients were included in the meta-analysis. Included patients in most studies were hospitalized pediatric patients. Only one study included adulthood data. Three cohort studies, four case-control studies, and one cross-sectional studies found a significant association between obesity and dengue severity. In contrast, three cohort studies, three case-control studies, and one cross-sectional study reported no significant relationship between obesity and dengue severity. Our analysis results showed that patient with obesity is 50% (OR = 1.50; 95%CI: 1.15-1.97) more likely to develop severe manifestation of dengue. CONCLUSION: This meta-analysis revealed that overweight could be a clinical predictor for severe disease for pediatric patients with dengue infection.

Air quality health index (AQHI) based on multiple air pollutants and mortality risks in Taiwan: Construction and validation.

The currently used air quality index (AQI) is not able to capture the additive effects of air pollution on health risks and reflect non-threshold concentration-response relationships, which has been criticized. We proposed the air quality health index (AQHI) based on daily air pollution-mortality associations, and compared its validity in predicting daily mortality and morbidity risks with the existing AQI. We examined the excess risk (ER) of daily elderly (≥65-year-old) mortality associated with 6 air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) in 72 townships across Taiwan from 2006 to 2014 by performing a time-series analysis using a Poisson regression model. Random effect meta-analysis was used to pool the township-specified ER for each air pollutant in the overall and seasonal scenarios. The integrated ERs for mortality were calculated and used to construct the AQHI. The association of the AQHI with daily mortality and morbidity were compared by calculating the percentage change per interquartile range (IQR) increase in the indices. The magnitude of the ER on the concentration-response curve was used to evaluate the performance of the AQHI and AQI, regarding specific health outcomes. Sensitivity analysis was conducted using coefficients from the single- and two-pollutant models. The coefficients of PM2.5, NO2, SO2, and O3 associated with mortality were included to form the overall and season-specific AQHI. An IQR increase in the overall AQHI at lag 0 was associated with 1.90%, 2.96%, and 2.68% increases in mortality, asthma, and respiratory outpatient visits, respectively. The AQHI had higher ERs for mortality and morbidity on the validity examinations than the current AQI. The AQHI, which captures the combined effects of air pollution, can serve as a health risk communication tool to the public.

Influence of microclimate control on the growth of asparagus under greenhouse in tropical climates.

High temperatures have become common in cities in Taiwan, and this phenomenon has spread to surrounding agricultural areas. Tainan, a city located in a tropical climate zone with agriculture as its primary development industry, is one of the cities considerably affected by the high temperature. High temperatures can reduce crop yields and even cause plant death, especially for vulnerable high-value crops, which are severely to microclimate conditions. Asparagus is a high-value crop that has long been cultivated in the Jiangjun District of Tainan. Recently, asparagus has been planted in greenhouses to protect against pests and natural disasters. However, the greenhouses can overheat. To identify the optimal growth environment for asparagus, this study applies vertical monitoring to record the temperature in the greenhouse and the soil moisture content of a control (canal irrigation) and an experimental (drip irrigation) group. When the surface layer of the soil exceeds 33°C, the tender stems of asparagus bloom readily, reducing its commercial value. Therefore, drip irrigation was conducted with cool water (26°C) to reduce soil temperature in summer and warm water (28°C) to increase soil temperature in winter. The study also recorded the growth of asparagus using daily yields measured by farmers during weighing and packing to understand the benefits of controlling the greenhouse microclimate. This study reports a correlation of 0.85 between asparagus yield and temperature and a correlation of 0.86 between asparagus yield and soil moisture content. The use of a drip irrigation system with a water temperature adjustment function not only saves up to 50% of water but also resulted in an average yield increase of 10% through maintaining stable soil moisture content and temperature. Therefore, the findings of this study can be applied to asparagus yields affected by high temperature and can solve the problems of poor quality in summer and low yield in winter.

SARS-CoV-2 infection among healthcare workers whom already received booster vaccination during epidemic outbreak of omicron variant in Taiwan.

BACKGROUND/PURPOSE: Healthcare workers (HCWs) are at risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to occupational exposure. We aim to investigate the prevalence and risk factors of SARS-CoV-2 infection among HCWs during epidemic outbreak of omicron variant in Taiwan. METHODS: Sequential reserved serum samples collected from our previous study during December 2021 and July 2022 were tested for antibodies against SARS-CoV-2 nucleocapsid protein (NP). Diagnosis of SARS-CoV-2 infection was defined as positive either of anti-SARS-CoV-2 nucleoprotein, rapid antigen test or polymerase chain reaction. Retrospective chart review and a questionnaire were used to access the symptoms and risk factors for SARS-CoV-2 infection. RESULTS: Totally 300 participants (69.3% female) with a median age of 37.9 years were enrolled. A significant increase incidence of SARS-CoV-2 infection was found before and during community outbreak (11.91 versus 230.93 per 100,000 person-days, P < 0.001), which was a trend paralleling that observed in the general population. For 61 SARS-CoV-2 infected participants, nine (14.8%) were asymptomatic. Multivariate analysis revealed recent contact with a SARS-CoV-2 infected household (odds ratio [OR], 7.01; 95% confidence interval [95% CI], 3.70-13.30; P < 0.001) and co-existed underlying autoimmune diseases (OR, 4.46; 95% CI, 1.28-15.51; P = 0.019) were significant risk factors associated with acquisition of SARS-CoV-2 infection among HCWs. CONCLUSION: Community factors, such as closely contact with SARS-CoV-2 infected individuals and underlying immune suppression status, were significant factors for acquisition of SARS-CoV-2 infection among HCWs. We suggest the application of appropriate infection control measures for HCWs should be maintained to reduce risk of SARS-CoV-2 infection.

Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process and evaluate the efficacy of anti-amyloid drugs. The lasing wavelength changes accordingly with the amyloid peptide folding behaviors and nanostructure conformations in the droplet resonator. A 3D deep-learning strategy was developed to directly image minute spectral shifts through a far-field camera. By extracting 1D color information and 2D features from the laser images, the progression of the amyloidogenesis process could be monitored using arrays of laser images from microdroplets. The training set, validation set, and test set of the multimodal learning model achieved outstanding classification accuracies of over 95%. This study shows the great potential of deep-learning-empowered peptide microlaser yields for protein misfolding studies and paves the way for new possibilities for high-throughput imaging of cavity biosensing.

Tunable Optical Vortex from a Nanogroove-Structured Optofluidic Microlaser.

Optical vortices with tunable properties in multiple dimensions are highly desirable in modern photonics, particularly for broadly tunable wavelengths and topological charges at the micrometer scale. Compared to solid-state approaches, here we demonstrate tunable optical vortices through the fusion of optofluidics and vortex beams in which the handedness, topological charges, and lasing wavelengths could be fully adjusted and dynamically controlled. Nanogroove structures inscribed in Fabry-Pérot optofluidic microcavities were proposed to generate optical vortices by converting Hermite-Gaussian laser modes. Topological charges could be controlled by tuning the lengths of the nanogroove structures. Vortex laser beams spanning a wide spectral band (430-630 nm) were achieved by alternating different liquid gain materials. Finally, dynamic switching of vortex laser wavelengths in real-time was realized through an optofluidic vortex microlaser device. The findings provide a robust yet flexible approach for generating on-chip vortex sources with multiple dimensions, high tunability, and reconfigurability.

A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases. Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE. Methods: Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction. Results: The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified. Conclusions: A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.

Low-Power Photodetectors Based on PVA-Modified Reduced Graphene Oxide Hybrid Solutions.

Photodetectors based on reduced graphene oxide (rGO) have attracted much attention owing to their simple and low-cost fabrication process. However, the aggregation and defects of rGO flakes still limit the performance of rGO photodetectors. Controlling the composition of rGO has become a vital factor for its prospective applications. For example, the interconnection between rGO and polymers for modified morphologies of rGO films leads to an enhanced performance of devices. In this work, a practical approach to engineer surface uniformity and enhance the performance of a photodetector by modifying the rGO film with hydrophilic polymers poly(vinyl alcohol) (PVA) is reported. Compared with the rGO photodetector, the on/off ratio for the PVA/rGO photodetector shows 3.5 times improvement, and the detectivity shows 53% enhancement even when the photodetector is operated at a low bias of 0.3 V. This study provides an effective route to realize PVA/rGO photodetectors with a low-power operation which shows promising opportunities for the future development of green systems.

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.

Aryl hydrocarbon receptor activation-mediated vascular toxicity of ambient fine particulate matter: contribution of polycyclic aromatic hydrocarbons and osteopontin as a biomarker.

BACKGROUND: Exposure to ambient fine particulate matter (PM2.5) is associated with vascular diseases. Polycyclic aromatic hydrocarbons (PAHs) in PM2.5 are highly hazardous; however, the contribution of PM2.5-bound PAHs to PM2.5-associated vascular diseases remains unclear. The ToxCast high-throughput in vitro screening database indicates that some PM2.5-bound PAHs activate the aryl hydrocarbon receptor (AhR). The present study investigated whether the AhR pathway is involved in the mechanism of PM2.5-induced vascular toxicity, identified the PAH in PM2.5 that was the major contributor of AhR activation, and identified a biomarker for vascular toxicity of PM2.5-bound PAHs. RESULTS: Treatment of vascular smooth muscle cells (VMSCs) with an AhR antagonist inhibited the PM2.5-induced increase in the cell migration ability; NF-κB activity; and expression of cytochrome P450 1A1 (CYP1A1), 1B1 (CYP1B1), interleukin-6 (IL-6), and osteopontin (OPN). Most PM2.5-bound PAHs were extracted into the organic fraction, which drastically enhanced VSMC migration and increased mRNA levels of CYP1A1, CYP1B1, IL-6, and OPN. However, the inorganic fraction of PM2.5 moderately enhanced VSMC migration and only increased IL-6 mRNA levels. PM2.5 increased IL-6 secretion through NF-κB activation; however, PM2.5 and its organic extract increased OPN secretion in a CYP1B1-dependent manner. Inhibiting CYP1B1 activity and silencing OPN expression prevented the increase in VSMC migration ability caused by PM2.5 and its organic extract. The AhR activation potencies of seven PM2.5-bound PAHs, reported in the ToxCast database, were strongly correlated with their capabilities of enhancing the migration ability of VSMCs. Benzo(k)fluoranthene (BkF) contributed the most to the AhR agonistic activity of ambient PM2.5-bound PAHs. The association between PM2.5-induced vascular toxicity, AhR activity, and OPN secretion was further verified in mice; PM2.5-induced intimal hyperplasia in pulmonary small arteries and OPN secretion were alleviated in mice with low AhR affinity. Finally, urinary concentrations of 1-hydroxypyrene, a major PAH metabolite, were positively correlated with plasma OPN levels in healthy humans. CONCLUSIONS: The present study offers in vitro, animal, and human evidences supporting the importance of AhR activation for PM2.5-induced vascular toxicities and that BkF was the major contributor of AhR activation. OPN is an AhR-dependent biomarker of PM2.5-induced vascular toxicity. The AhR activation potency may be applied in the risk assessment of vascular toxicity in PAH mixtures.

Effects of a Pedometer-Based Walking Program in Patients with COPD-A Pilot Study.

Background and objectives: Patients with chronic obstructive pulmonary disease (COPD) suffer from impaired pulmonary function and dyspnea, which result in limited levels of physical activity, and impaired quality of life. Exercise and regular physical activity have been proven to break the vicious circle. The aim of this pilot study is to investigate the effects of a walking program on exercise capacity and quality of life in patients with COPD. Materials and Methods: Patients with COPD were randomly assigned to a pedometer group (PG) or control group (CON). Subjects in the PG walked target steps daily with a pedometer for six weeks. Before and after the program, the following measurements were performed: pulmonary function test (PFT), daily steps, Six-Minute Walk Test (6 MWT), COPD Assessment Test (CAT), and quality of life questionnaire (SF-12). Results: After this walking program, PG (n = 15) significantly improved their daily steps from 4768.4 ± 2643.3 steps to 7042.7 ± 4281.9 steps (p = 0.01). Forced vital capacity (FVC) increased from 2.5 ± 0.7 L to 2.8 ± 0.9 L (p = 0.02). CAT scores decreased from 14.9 ± 8.8 points to 11.5 ± 7.5 points (p = 0.03). In the control group (n = 11), there were no differences in any outcomes after this daily walking program. Conclusions: For patients with COPD, a daily walking program with a pedometer is beneficial in the improvement of pulmonary function, daily steps, and quality of life.

[Feasibility of Single-Breath-Hold Compressed Sensing Real-Time Cine Imaging for Assessment of Ventricular Function and Left Ventricular Strain in Cardiac Magnetic Resonance].

Objective: To explore the feasibility of single-breath-hold compressed sensing real-time cine imaging (CS-cine) in the assessment of ventricular function and left ventricular (LV) strain. Methods: A total of 70 subjects were enrolled prospectively, and all subjects underwent cardiac magnetic resonance imaging (cardiac MRI) using both the standard steady-state free procession cine (sta-cine) acquisition and a prototype CS-cine sequence. For both CS-cine and sta-cine imaging, continuous short-axis cine images were acquired from the base to the apex to cover the entire left ventricle, and long-axis cine images including two-, three-, and four-chamber views were also acquired. The scanning range, number of slices, slice thickness and intervals were kept identical for the two cine images of the same participant. Subjective evaluation of the image quality was performed on all cine images. For both sequences, the conventional function parameters of the left and the right ventricles and LV strain values were assessed with post-processing software analysis. The cine image quality, conventional ventricular function parameters, and LV strain values were compared between the two cine groups and the differences were examined. Inter- and intraobserver agreements for CS-cine images were measured using intraclass correlation coefficient ( ICC). Bland-Altman analysis was performed to assess reproducibility between the two cine methods. Results: The median scanning time of CS-cine was 21 s versus 272 s for sta-cine ( P<0.001). The median image quality scores of two groups were significantly different, 4 points for sta-cine and 2 points for CS-cine ( P<0.001). Bi-ventricular end-diastolic volumes (EDV), stroke volume (SV) and ejection fraction (EF) were significantly smaller in CS-cine ( P<0.001). Nevertheless, no significant differences between the two groups in bi-ventricular ESV or LV mass were observed ( P>0.05). LV strain parameters, including the peak radial strain, peak circumferential strain and peak longitudinal strain derived from LV mid-ventricular slice, were significantly different in the two sequences ( P<0.001). Moreover, CS-cine-derived functional parameters and strain measurements have a good correlation with those of sta-cine (for RV function parameters, and left ventricular PLS, PCS values, more than 95% points fell within the limits of agreement [ LoA]; meanwhile, more than 91% points fell within the LoA for other parameters) and inter- and intraobserver agreements were strong ( ICC=0.88 to 0.99) for CS-cine. Conclusion: CS-cine can well realize the rapid acquisition of cine images for quantitative analysis of cardiac function, and the conventional ventricular function parameters and LV globalized strain values obtained from CS-cine imaging have good reproducibility.

Imaging-Based Optofluidic Biolaser Array Encapsulated with Dynamic Living Organisms.

Optofluidic biolasers have emerged as promising tools for biomedical analysis due to their strong light-matter interactions and miniaturized size. Recent developments in optofluidic lasers have opened a new Frontier in monitoring biological processes. However, most biolasers require precise recording of the lasing spectrum at the single cavity level, which limits its application in high-throughput applications. Herein, a microdroplet laser array encapsulated with living Escherichia coli was printed on highly reflective mirrors, where laser emission images were employed to reflect the dynamic changes in living organisms. The concept of image-based lasing analysis was proposed by quantifying the integrated pixel intensity of the lasing image from whispering-gallery modes. Finally, dynamic interactions between E. coli and antibiotic drugs were compared under fluorescence and laser emission images. The amplification that occurred during laser generation enabled the quantification of tiny biological changes in the gain medium. Laser imaging presented a significant increase in integrated pixel intensity by 2 orders of magnitude. Our findings demonstrate that image-based lasing analysis is more sensitive to dynamic changes than fluorescence analysis, paving the way for high-throughput on-chip laser analysis of living organisms.