Efficacy and safety of tirofiban in patients with acute ischemic stroke treated with endovascular thrombectomy: A frequentist and Bayesian meta-analysis.

BACKGROUND: Tirofiban is an antiplatelet treatment approved for acute coronary syndrome, but it has not been rigorously evaluated for efficacy and safety in patients with acute ischemic stroke (AIS) treated with endovascular thrombectomy (EVT). METHODS: Electronic databases were systematically searched for studies conducted from January 1, 2015, to July 31, 2021, that evaluated tirofiban administration for patients with AIS treated with EVT in comparison with control. Risk ratios (RRs) and confidence intervals (CIs) were estimated for favorable functional outcomes (FFOs), mortality, and symptomatic intracranial hemorrhage (SICH), each 90 days after AIS. Bayesian hierarchical modeling was performed to obtain posterior RR and its 95% highest posterior density (HPD) for validation. RESULTS: Compared with controls, tirofiban users exhibited increased FFOs (RR, 1.18; 95% CI, 1.08-1.30), decreased mortality (RR, 0.77; 95% CI, 0.64-0.92), and no difference in SICH (RR, 0.97; 95% CI, 0.77-1.23). Tirofiban users in the postbolus infusion subgroup exhibited increased FFOs (RR, 1.20; 95% CI, 1.07-1.35), decreased mortality (RR, 0.71; 95% CI, 0.58-0.88), and no increase in SICH (RR, 0.97; 95% CI, 0.72-1.29). The bolus-only subgroup showed no differences in FFO, mortality, or SICH between the tirofiban and control groups. Consistent results were obtained for posterior density of FFO (posterior RR, 1.20; 95% HPD, 1.06-1.34), mortality (posterior RR, 0.77; 95% HPD, 0.63-0.92), and SICH (posterior RR, 0.98; 95% HPD, 0.71-1.26). CONCLUSION: For patients with AIS treated with EVT, tirofiban improved FFOs, decreased mortality, and did not increase SICH compared with controls; postbolus infusion for administering tirofiban was more favored than the bolus-only regimen.

Spatial-temporal distribution and pedestrian exposure assessment of size-fractionated particles on crosswalk of urban intersection.

Vehicles tend to produce more pollutants especially particles at an urban intersection than other segments. Meanwhile, pedestrians at an intersection are inevitably exposed to high particle level and suffered from the health problem. Especially, some particles can deposit in different thoracic areas of the respiratory system and cause serious health problems. Hence, in this paper, the particles from 0.3 to 10 µm in 16 channels were measured to compare the spatio-temporal characteristics of them on the crosswalk and the roadside. Based on the roadside of fixed measurements, submicron particles (< 1 µm) are discovered to have a high relation with traffic signal and exhibit a bimodal distribution pattern in the green phase. On the crosswalk of mobile measurements, submicron particles present decreasing trend along the crosswalk while crossing. Additionally, mobile measurements were conducted across six time intervals that correspond to different pedestrian's journey when passing the crosswalk. The results showed that all size particles in the first three journeys present high concentrations than that in other journeys. Furthermore, pedestrian exposure to all 16 channel particles was assessed. The total and regional deposition fractions of these particles in different sizes and age groups are determined. What ought to be paid attention to is that these real-world measurement results contribute to advancing the understanding of pedestrian exposure to size-fractionated particles on crosswalk and assisting the pedestrian to make better informed choice so as to limit particle exposure in these pollution hotspots.

Transmission mitigation of COVID-19: Exhaled contaminants removal and energy saving in densely occupied space by impinging jet ventilation.

The pandemic of COVID-19 and its transmission ability raise much attention to ventilation design as indoor-transmission outstrips outdoor-transmission. Impinging jet ventilation (IJV) systems might be promising to ventilate densely occupied large spaces due to their high jet momentum. However, their performances in densely occupied spaces have rarely been explored. This study proposes a modified IJV system and evaluates its performance numerically in a densely occupied classroom mockup. A new assessment formula is also proposed to evaluate the nonuniformity of target species CO2. The infector is assumed as the manikin with the lowest tracer gas concentration in the head region. The main results include: a) Indoor air quality (IAQ) in the classroom is improved significantly compared with a mixing ventilation system, i.e., averaged CO2 in the occupied zone (OZ) is reduced from 1287 ppm to 1078 ppm, the OZ-averaged mean age of air is reduced from 439 s to 177 s; b) The mean infection probability is reduced from 0.047% to 0.027% with an infector, and from 0.035% to 0.024% with another infector; c) Cooling coil load is reduced by around 21.0%; d) Overall evaluation indices meet the requirements for comfortable environments, i.e., the temperature difference between head and ankle is within 3 °C and the OZ-averaged predictive mean vote is in the range of -0.5 - 0.5; e) Thermal comfort level and uniformity are decreased, e.g., overcooling near diffuser at ankle level. Summarily, the target system effectively improves IAQ, reduces exhaled-contaminant concentration in head regions, and saves energy as well.

Split-hand index for amyotrophic lateral sclerosis diagnosis: A frequentist and Bayesian meta-analysis.

OBJECTIVE: Preferential wasting of the thenar muscles, the split-hand sign, may be used for early diagnosis of amyotrophic lateral sclerosis (ALS). METHODS: Electronic databases were searched for studies assessing the split-hand index (SHI) and the compound muscle action potential (CMAP) amplitudes of abductor pollicis brevis (APB), first dorsal interosseous (FDI), and abductor digiti minimi (ADM). The SHI was obtained by multiplying CMAP amplitudes of APB and FDI and dividing the product by the CMAP amplitude of ADM. The Bayesian analysis was used for validation. RESULTS: In total, 17 studies and 1635 patients were included. Our meta-analysis revealed that ALS patients had significantly decreased SHI (standardized mean difference [SMD], -1.60, P < 0.001), CMAP of the APB (SMD, -1.67, P < 0.001), FDI (SMD, -1.12, P < 0.001), and ADM (SMD, -1.09, P < 0.001). The binormal receiver operating characteristic curve analysis showed a threshold of < 7.4 for SHI, and cutoff values of < 6.4 mV for APB and < 8.4 mV for FDI, respectively. The Bayesian analysis validated decreased SHI in ALS patients (posterior mean difference of - 5.91). CONCLUSIONS: An SHI of < 7.4 can be used facilitating earlier diagnosis of ALS. SIGNIFICANCE: SHI can be used as a standard neurophysiological biomarker for early diagnosis.

Diagnostic test accuracy of pretreatment collateral score in predicting stroke outcomes after intra-arterial endovascular thrombectomy: a meta-analysis in DSA and CTA.

OBJECTIVES: This study compared the diagnostic accuracy of pretreatment circulation collateral scoring (CS) system using digital subtraction angiography (DSA) and computed tomography angiography (CTA) in predicting favorable functional outcome (FFO) after intra-arterial endovascular thrombectomy (IA-EVT). Subgroup analysis characterizing scoring systems within each category was additionally conducted. MATERIALS AND METHODS: We performed a diagnostic meta-analysis to assess the sensitivity and specificity of each CS system by using DSA and CTA, respectively. The hierarchical summary receiver operating characteristic curve (HSROC) models were used to estimate the diagnostic odds ratio (DOR) and area under the curve (AUC). The Bayes theorem was employed to determine posttest probability (PTP). RESULTS: In total, 14 and 21 studies were assessed with DSA and CTA, respectively. In DSA, the pooled sensitivity and specificity were 0.72 (95% CI, 0.63-0.79) and 0.61 (0.53-0.68), respectively, and in the HSROC model, the DOR was 3.94 (2.71-5.73), and the AUC was 0.71 (90.67-0.75). CTA revealed a pooled sensitivity and specificity of 0.74 (0.64-0.82) and 0.53 (0.44-0.62), respectively, and in the HSROC model, the DOR was 3.17 (2.34-4.50), and the AUC was 0.67 (0.63-0.71). With a pretest probability of 26.3%, the CS in DSA and CTA exhibited limited increase of PTPs of 39% and 36%, respectively, in detecting the FFO on day 90. CONCLUSION: DSA and CTA have comparable accuracy and are limited in predicting the functional outcome. The collateral score systems assessed with DSA and CTA were more suitable for screening than diagnosis for patients before IA-EVT. KEY POINTS: • Our study revealed the differences of various scoring systems for assessing collateral status. • DSA and CTA have comparable accuracy, but both imaging modalities played relatively limited roles in predicting functional outcome on day 90. • The collateral score systems assessed with DSA and CTA were more suitable for screening than diagnosis for patients before IA-EVT.

Posterior circulation acute stroke prognosis early CT scores in predicting functional outcomes: A meta-analysis.

BACKGROUND AND PURPOSE: Patients with posterior circulation acute ischemic stroke exhibit varied clinical presentations and functional outcomes. Whether posterior circulation acute stroke prognosis early computed tomography scores (PC-ASPECTS) predict unfavorable functional outcomes (UFO) for patients treated with different therapeutic regimens is unclear. METHODS: According to PRISMA guidelines, we performed a systematic search of electronic databases for studies assessing the functional outcomes of posterior circulation acute ischemic stroke using baseline PC-ASPECTS. The following three scales of PC-ASPECTS were retrieved: UFO prediction by using PC-ASPECTS per score decrease, UFO prediction by using binary PC-ASPECTS with a cut-off value, and the difference in PC-ASPECTS between patients with unfavorable and favorable functional outcomes. Moreover, a subgroup analysis was conducted for patients treated with intra-arterial endovascular treatment (IA-EVT) only. Sensitivity analysis with different definition of UFO and image modalities were also conducted. RESULTS: In total, 25 studies were included. In scale 1, PC-ASPECTS significantly predicted UFO (odds ratio [OR]: 1.66 per score decrease, 95% confidence interval [CI]: 1.32-2.07). In scale 2, binary PC-ASPECTS with a cut-off value between 6 and 9 significantly predicted UFO (OR: 3.91, 95% CI: 2.54-6.01). In scale 3, patients with UFO had lower PC-ASPECTS than those with favorable outcomes (standardized mean difference [SMD]: -0.67, 95% CI: -0.8 to -0.55). For patients treated with IA-EVT only, the scales demonstrated consistent results. Sensitivity analysis showed PC-ASPECTS significantly predicted UFO in both definitions of modified Rankin Scale ≥ 3 and ≥ 4, and magnetic resonance imaging was preferred imaging modality for PC-ASPECTS evaluation. CONCLUSION: Baseline PC-ASPECTS is effective in predicting UFO for patients with posterior circulation acute ischemic stroke treated with different therapeutic regimens.

Characterizing the variation of particles in varied sizes from a container truck in a port area.

The transportation of container trucks in urban areas not only frequently causes traffic jams but also produces severe air pollution. With regard to this consideration, measurements of particle concentrations and traffic volume on different polluted days were carried out to discover the varied characteristics of particles from container truck transportation in the port area. Based on the original data, descriptive statistics were performed firstly to reveal the statistical characteristics of particle number concentrations (PNC). The Kolmogorov-Smirnov test as well as the Anderson-Darling test was adopted to identify the "best-fit" distributions on PNC data while the corresponding maximum likelihood estimation was conducted to estimate the parameters of the identified distribution. Additionally, the Pearson correlation analysis and principal component analysis were performed respectively to reveal the relationships between traffic volume and PNC. The results showed that on a hazy day, PNC levels in the morning were generally higher than those in the afternoon, while on a non-hazy day, the results were opposite. Particles in all sizes on a non-hazy day and larger than 0.5 µm on a hazy day were verified to fit the lognormal distribution. In contrast to the particles below 2 µm, the particles above 2 µm exhibited higher correlations with the traffic flow of a container truck in the morning on a hazy day. These results indicate the importance of reducing air pollution from a container truck and provide policymakers with a foundation for possible measures in a port city.

Multiscale multifractal properties between ground-level ozone and its precursors in rural area in Hong Kong.

In rural area, due to the reduction of NOx and CO emitted from vehicle exhausts, the ozone photochemical reaction exhibits relatively weak effect and ozone formation presents different pattern with its precursors in contrast to urban situation. Hence, in this study, we apply detrended cross-correlation analysis to investigate the multifractal properties between ozone and its precursors in a rural area in Hong Kong. The observed databases of ozone, NO2, NOx and CO levels during 2005-2014 are obtained from a rural monitoring station in Hong Kong. Based on the collected database, the cross-correlation analysis is carried out firstly to examine the cross-correlation patterns and the results indicate that close interactive relations exist between them. Then the detrended cross-correlation analysis is performed for further analysis. The multifractal characters occur between ozone and its precursors. The long-term cross-correlations behaviors in winter are verified to be stronger than that in other seasons. Additionally, the method is extended on daily averaged data to explore the multifractal property on various time scales. The long-term cross-correlation behavior of ozone vs NO2 and NOx on daily basis becomes weaker while that of ozone vs CO becomes stronger. The multifractal properties for all pairs in summer are found to be the strongest among the whole year. These findings successfully illustrate that the multifractal analysis is a useful tool for describing the temporal scaling behaviors of ozone trends in different time series in rural areas.

Multifractal nature of particulate matters (PMs) in Hong Kong urban air.

In this study, we investigate the persistent variation and the multifractal nature of particulate matter (PM) concentrations from vehicle emissions at a typical traffic intersection of street canyon in Hong Kong. Six size groups of PMs are measured and collected during rush hour sessions on different dates respectively. A recently developed model, namely multifractal detrended fluctuation analysis (MF-DFA), is employed to decompose and analyze the collected database. Through estimating the scaling exponent, it is found that the PM levels from vehicular emissions display long-term correlation characters. By employing MF-DFA method to calculate the generalized Hurst exponent and discuss the multifractal spectrums of all size groups, it is noticed that the fine particulate matters in grain diameter of 0.3-0.499 µm present strong multifractal nature, intensive oscillations of concentration variations, and long-term persistence. For fine particulate matters in the grain diameter ranges from 0.5 µm to 4.99 µm, their similar and weak multifractal natures reflect the self-similarity behaviors among these groups and the gradual decreases of the lasting effects. For large size particulate matters, i.e., grain diameter above 5 µm, certain mono-fractal nature and sharp decay of long-term persistence are obtained, even for intermittent effects. It can therefore be concluded that the fine particulate matter diffuses anomaly and persists for a long time.

The effects of melatonin on colonic transit time in normal controls and IBS patients.

OBJECTIVES: The role of melatonin in regulating gut motility in human subjects is not clear. The aim of this study was to investigate the effects of exogenous melatonin on colonic transit time (CTT) in healthy subjects and in patients with irritable bowel syndrome (IBS). METHODS: Colonic transit time was measured in 17 healthy controls using the radio-opaque, blue dye, and Bristol stool form score method before and after 30 days of melatonin treatment 3 mg daily. A double blind cross-over study aimed at measuring CTT was also performed in 17 matched IBS patients using the blue dye and Bristol stool form score methods. The patients were randomized and received either melatonin 3 mg or placebo daily for 8 weeks, followed by a 4-week washout, and then placebo or melatonin in the reverse order for a second 8-week period. RESULTS: The melatonin treatment of the control subjects caused an increase in CTT (mean+/-SD) from 27.4+/-10.5 to 37.4+/-23.8 h (P=0.04). Compared with the CTT of the controls (25.2+/-7.7), that of the constipation-predominant IBS patients appeared prolonged-65.2+/-33.3 h (P<0.01). The CTT did not change significantly in IBS patients after melatonin treatment. CONCLUSION: Melatonin may be a promising candidate for the future research of agents that can modulate bowel motility.

Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme.

For ground-level ozone (O(3)) prediction, a predictive model, with reliable performance not only on non-polluted days but, more importantly, on polluted days, is favored by public authorities to issue alerts, so that concerned citizens and industrial organizations could take precautions to avoid exposure and reduce harmful emissions. However, the class imbalance problem, i.e., in some collected field data, number of O(3) polluted days are much smaller than that of non-polluted days, will deteriorate the model performance on minority class-O(3) polluted days. Despite support vector machine (SVM) obtaining promising results in air quality prediction, in this study, a cost-sensitive classification scheme is proposed for the standard support vector classification model (S-SVC) in order to investigate whether the class imbalance plagues S-SVC. The S-SVC with such scheme is named as CS-SVC. Experiments on imbalanced data sets collected from two air quality monitoring sites in Hong Kong show that 1) S-SVC is still sensitive to class imbalance problem; 2) compared with S-SVC, CS-SVC effectively avoids class imbalance problem with lower percentage of false negative on O(3) polluted days but with higher percentage of false positive on non-polluted days; 3) compared with both S-SVC and CS-SVC, support vector regression model (SVR), after converting its output to binary one, only has similar performance with S-SVC, which indicates class imbalance problem also impairs the regressor model. From point of protecting public health, CS-SVC, which less likely misses to forecast O(3) polluted days, is recommended here.

Investigation of respirable suspended particulate trend and relevant environmental factors in Hong Kong downtown areas.

As the addressing of high demand of good air quality in urban area, a study on air pollutant dispersion and distribution resulting from vehicular exhaust emission is strongly required. In particular, vehicular exhaust emission has become a major air pollution source in metropolitan city like Hong Kong, which is characterized with the heavy, dense traffic flow and the limited land resources. Respirable suspended particulate (RSP) is one of main pollutants resulted from vehicular exhaust emission in urban area. Hence, in this study, we focus on analyzing the variation of RSP levels including diurnal, monthly and annual patterns at selected roadsides in Hong Kong during the period of 1998--2005. Furthermore, the relationships between RSP level and the relevant meteorological factors such as temperature, rainfall and wind conditions in Hong Kong territory have been discussed as well.

Evolving trend and self-similarity of ozone pollution in central Hong Kong ambient during 1984-2002.

The evolving pattern of ozone level in Hong Kong urban air has undergone various changes and corresponds to the regional urban and economic development. We assess such changes by reviewing and analyzing the original ozone pollutant database monitored in central Hong Kong downtown area during the period of 1984-2002. Both fractal analysis and traditional statistical methods are adopted to estimate the ozone evolving characteristics during the studied period. It is found that the ozone evolving pattern has strong self-similarity and the ozone pollution presents increasing trend in Hong Kong region in recent years based on the analysis. The typical fractal dimensions for total time series are D = 0.894 for available data set (N = 5760) and D = 0.859 for complete data set with interpolation (N = 6935), respectively. The fractal analysis can be used to assess the pollution trend in urban environment and may provide an alternative method for environmental study.

Seasonal variation of air pollution index: Hong Kong case study.

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.

Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model.

In this work, we focus on simulating the ground-level ozone (O3) time series and its daily maximum concentration in Hong Kong urban air by employing the multilayer perceptron (MLP) model combined with the automatic relevance determination (ARD) method (for simplicity, we name it as MLP-ARD model). Two air quality monitoring sites in Hong Kong, i.e., Tsuen Wan and Tung Chung, are selected for the numerical experiments. The MLP-ARD model based on Bayesian evidence framework can provide reliable interval estimation of real observation as well as offering efficient strategy to avoid over-fitting. The performance comparisons between MLP-ARD model and traditional artificial neural network (ANN) model based on maximum likelihood indicate that MLP-ARD model is more powerful to capture the wild fluctuation of O3 level especially during O3 episodes than the traditional model. Furthermore, it can assess and rank the input variables for the prediction according to their relative importance to the output variable, i.e., the daily maximum O3 concentration in this study. The preliminary experimental results indicate that nitric oxide (NO) and solar radiation are the most important input variables for O3 prediction at both selected sites. In addition, the previous daily maximum O3 level is also important for Tung Chung site. In this regard, MLP-ARD model is a feasible tool to interpret the real physical and chemical process of urban O3 variation.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.