Perovskite photodetector-based laser absorption spectroscopy for gas detection.

A gas detection method based on CH3NH3PbI3 (MAPbI3) and poly (3,4-ethylenedioxythiophene): poly (4-styrene sulfonate) (PEDOT:PSS) composite photodetectors (PDs) is proposed. The operation of the PD primarily relies on the photoelectric effect within the visible light band. Our study involves constructing a gas detection system based on tunable diode laser spectroscopy (TDLAS) and MAPbI3/PEDOT:PSS PD, and O2 was selected as the target analyte. The system has achieved a minimum detection limit (MDL) of 0.12% and a normalized noise equivalent absorption coefficient (NNEA) of 8.83 × 10-11 cm-1⋅W⋅Hz-1/2. Furthermore, the Allan deviation analysis results indicate that the system can obtain sensitivity levels as low as 0.058% over an averaging time of 328 seconds. This marks the first use of MAPbI3/PEDOT:PSS PD in gas detection based on TDLAS. Despite the detector's performance leaves much to be desired, this innovation offers a new approach to developing spectral based gas detection system.

Theabrownins in dark tea form complexes with tea polysaccharide conjugates.

BACKGROUND: Theabrownins (TBs) are one of most important quality components in dark tea, but have not been produced industrially. In this study, the aqueous extract was obtained from Pu-erh ripe tea, one kind of dark tea. Caffeine, theaflavin, catechin and saponin were removed by trichloromethane, ethyl acetate and n-butanol in turn to obtain a TB isolate. The TB isolate was subjected to column chromatography using a macroporous resin HPD-750 and eluted with a gradient of 0-700 g kg-1 ethanol aqueous solution. Four fractions were obtained, and named as TBs-FC1, TBs-FC2, TBs-FC3 and TBs-FC4. RESULTS: These four fractions contained polysaccharides and no small molecules such as catechins, caffeine and theaflavins as well as average molecular weights of 123.000 kDa, 23.380 kDa, 89.870 kDa and 106.600 kDa. It was revealed that they were complexes of TBs and tea polysaccharide conjugates (TPCs). Ultraviolet-visible (UV-visible) and infrared (IR) spectra showed the properties of TBs and TPCs. Their zeta potentials ranged from -13.40 mV to -38.80 mV in aqueous solutions at pH 3.0-9.0. CONCLUSION: This study reveals that TBs do not exist in free state but in combined state in dark tea, which provide the theoretical basis for the industrialization of TBs. © 2024 Society of Chemical Industry.

Quartz tuning fork-based high sensitive photodetector by co-coupling photoelectric and the thermoelastic effect of perovskite.

This paper reports a new strategy for enhancing the photoresponse of a quartz tuning fork (QTF). A deposited light absorbing layer on the surface of QTF could improve the performance only to a certain extent. Herein, a novel strategy is proposed to construct a Schottky junction on the QTF. The Schottky junction presented here consists of a silver-perovskite, which has extremely high light absorption coefficient and dramatically high power conversion efficiency. The co-coupling of the perovskite's photoelectric effect and its related QTF thermoelastic effect leads to a dramatic improvement in the radiation detection performance. Experimental results indicate that the CH3NH3PbI3-QTF obtains two orders of magnitude enhancement in sensitivity and SNR, and the 1σ detection limit was calculated to be 1.9 µW. It was the first time that the QTF resonance detection and perovskite Schottky junction was combined for optical detection. The presented design could be used in photoacoustic spectroscopy and thermoelastic spectroscopy for trace gas sensing.

A highly efficient Mn4+ activated Nb-based oxyfluoride red fluorescent material with excellent water stability: preparation and performance analysis.

In this study, an efficient non-rare earth Mn4+-doped K3(NbOF5)(HF2) red fluorescent material was synthesized by using the coprecipitation method. Replacing KF with K2CO3 effectively solved the problem that KF was difficult to stir due to its strong water absorption. The sample was composed of rods. The excitation spectra consisted of two strong excitation peaks at 366 nm and 468 nm. The emission spectra consisted of a series of narrow-band emissions between 580 nm and 680 nm. Besides, the luminescence quantum efficiency (QE) reached 84.3% under the excitation of 468 nm. The fluorescent lifetime of K3(NbOF5)(HF2):Mn4+ was less than 4 ms, which can achieve fast response display in backlight display applications. The WLED was fabricated with K3(NbOF5)(HF2):Mn4+ and commercial YAG:Ce3+ and the commercial InGaN blue chip. At a 30 mA drive current, the WLED device exhibited excellent luminescence properties. The correlated color temperature (CCT) was 3853 K, the Ra was 90.1 and the luminous efficiency was 310.432 lm W-1. Therefore, K3(NbOF5)(HF2):Mn4+ has very broad prospects in WLED lighting and backlight display applications.

Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions.

BACKGROUND: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. METHODS: To solve the aforementioned problems, nonlinear feature Lempel-Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. RESULTS: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. CONCLUSIONS: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.

[Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal].

An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8-13 Hz), beta frequency band (14-30 Hz) and gamma frequency band (31-60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.

Ultra-broadband optical detection from the visible to the terahertz range using a miniature quartz tuning fork.

We report and experimentally demonstrate a novel, to the best of our knowledge, sensitive and wideband optical detection strategy based on the light-induced thermoelastic effect in a miniature quartz tuning fork (mQTF) with low stiffness prongs. Compared with a traditional QTF, the soft prongs of the mQTF result in improved sensitivity. Experimental results demonstrate that the mQTF exhibits ∼54-fold superior sensitivity compared to a QTF, and the mQTF sensor has an ultra-broadband optical response, ranging from visible light to terahertz wavelengths. Its response time reaches 11.7 ms, and the minimum noise equivalent power (NEP) is measured to be 2.2 × 10-9 W Hz-1/2 at room temperature. The mQTF exhibits advantages in its cost-effectiveness, sensitivity, and ultra-broadband response, and provides a promising approach for the detection of low-dose optical and terahertz-wave radiation.

[ST segment morphological classification based on support vector machine multi feature fusion].

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.

[Follow control of upper limb rehabilitation training based on Kinect and NAO robot].

Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.

Polymer-coated quartz tuning fork for enhancing the sensitivity of laser-induced thermoelastic spectroscopy.

A novel laser-induced thermoelastic spectroscopy (LITES) sensor based on a polymer-coated quartz tuning fork (QTF) is reported. Two types of polymer films with different thicknesses are deposited on commercially available QTF to improve the conversion efficiency of laser energy deposition into vibration. CO2 was selected as the target analyte for validation measurements. The experimental results indicate that by introducing a polymer coating, a maximum gain factor of 3.46 and 3.21 is attained for the signal amplitude and signal-to-noise ratio (SNR), respectively, when compared to traditional LITES that using only a bare QTF. A minimum detectable concentration of 0.181% can be obtained, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 1.74×10-11 cm-1·W·Hz-1/2, and the measurement precision is approximately 0.06% with an averaging time of 200 s. Here, we show what we believe is the first demonstration of polymer coated QTF for LITES sensing, compared with custom QTF, the design has the virtues of lower cost, simple and easy-to-operate, is a promising new strategy for sensitive trace gas analysis.

Graphene oxide and polydimethylsiloxane coated quartz tuning fork for improved sensitive near- and mid-infrared detection.

Sensitive and broadband infrared sensors are required for security and medical applications, as few can rapidly and sensitively detect infrared without uncooled devices. Here, we report a wideband optical-detection strategy based on the thermoelastic effect of a coating-enhanced quartz tuning fork (QTF) and study the feasibility of using an atomic force probe operating in contact mode to monitor the vibration. Graphene oxide (GO) and polydimethylsiloxane (PDMS) coating were applied on the QTF's surface to improve the light absorption and the thermal-mechanical conversion efficiency. Experimental results showed that the bi-layer coatings yielded a maximum gain factor of 8 in response amplitude and signal-to-noise ratio (SNR) than that of a bare QTF, respectively. Lasers with wavelengths of 1512 nm and 10.6 µm were used as the typical representative light source to test the photoresponse of the QTF detector. The device displays a broadband photoresponse covering the near-infrared to mid-infrared range at room temperature, high performance with the maximum photoresponsivity of 85.76 V·mW-1, and 1σ detection limit of 0.056 µW; the lowest noise equivalent power (NEP) of 1.35 nW·Hz-1/2 and 43.9 ms response speed is also achieved. The preparation process of detector is simple and easy to implement; the resulting device exhibits high responsivity and wide wavelength response ranging at least from 1512 to 10600 nm, compared with custom QTF; and the surface coating strategy potentially enables the construction of a new class of low-cost photodetection sensors operated at room temperature.

Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study.

BACKGROUND: The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. METHODS: We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. RESULTS: The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14-30 Hz) had higher information flow in FCMC than the gamma band (31-45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. CONCLUSIONS: The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

[An improved maximal information coefficient algorithm applied in the analysis of functional corticomuscular coupling for stroke patients].

The functional coupling between motor cortex and effector muscles during autonomic movement can be quantified by calculating the coupling between electroencephalogram (EEG) signal and surface electromyography (sEMG) signal. The maximal information coefficient (MIC) algorithm has been proved to be effective in quantifying the coupling relationship between neural signals, but it also has the problem of time-consuming calculations in actual use. To solve this problem, an improved MIC algorithm was proposed based on the efficient clustering characteristics of K-means ++ algorithm to accurately detect the coupling strength between nonlinear time series. Simulation results showed that the improved MIC algorithm proposed in this paper can capture the coupling relationship between nonlinear time series quickly and accurately under different noise levels. The results of right dorsiflexion experiments in stroke patients showed that the improved method could accurately capture the coupling strength of EEG signal and sEMG signal in the specific frequency band. Compared with the healthy controls, the functional corticomuscular coupling (FCMC) in beta (14~30 Hz) and gamma band (31~45 Hz) were significantly weaker in stroke patients, and the beta-band MIC values were positively correlated with the Fugl-Meyers assessment (FMA) scale scores. The method proposed in this study is hopeful to be a new method for quantitative assessment of motor function for stroke patients.

[Detection of inferior myocardial infarction based on morphological characteristics].

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.

Comparison of the effectiveness between power toothbrushes and manual toothbrushes for oral health: a systematic review and meta-analysis.

Objective: Power toothbrushes is considered an effective tool for maintaining oral health; however, its efficacy as compared to manual toothbrushes is still not completely clarified. This article aims to evaluate the efficacy of power toothbrushes compared with the manual toothbrushes in terms of plaque, gingivitis and bleeding reduction.Methods: An electronic search was performed on PUBMED, Web of Science, Wiley and Research Gate. Studies comparing the effectiveness of plaque, gingivitis and bleeding reduction between power and manual toothbrushes were included. Results and effect sizes analysis are presented as standard mean difference (SMD), and subgroup analysis stratified by mode of action of the power toothbrush was performed. Study quality and risk of bias were assessed using the Cochrane assessment tool.Results: A total of 21 randomized clinical studies were included. Power toothbrushes were significantly more effective in reducing plaque index (26 trials: SMD = 0.86, 95% CI: 0.58 to 1.14, I2 = 91.5%, p < .0001), gingival index (14 trials: SMD = 0.47, 95% CI: 0.12 to 0.82, I2 = 88.7%, p < .0001), and bleeding index (11 trials: SMD = 0.92, 95% CI: 0.43 to 1.40, I2 = 91.8%, p < .0001) compared with the manual toothbrushes, except that there was no significant differences between the oscillating-rotating toothbrushes and manual toothbrushes regarding gingivitis reduction (7 trials: SMD = 0.07, 95% CI: -0.20 to 0.33, I2 = 57.2%, p = .03).Conclusions: Power toothbrushes is more effective in reducing dental plaque, gingivitis and bleeding compared with the manual toothbrush.

[Detection algorithm of paroxysmal atrial fibrillation with sparse coding based on Riemannian manifold].

In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.

[An automatic pulmonary nodules detection algorithm with multi-scale information fusion].

Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.

LncRNA SNHG1 functions as a ceRNA to antagonize the effect of miR-145a-5p on the down-regulation of NUAK1 in nasopharyngeal carcinoma cell.

How lncRNA SNHG1 influences the aggressiveness of nasopharyngeal carcinoma cells as well as the underlying mechanism was studied. The lncRNA differences were analysed by GSE12452 gene microarray. The expression of SNHG1, MiR-145-5p and NUAK1 was identified by qRT-PCR and western blot. Transfection was conducted to construct nasopharyngeal carcinoma cells with different expressions of SNHG1, miR-145-5p and NUAK1. Dual-luciferase reporter assay was performed to explore the relationship between SNHG1, miR-145-5p and NUAK1. Wound-healing assay and transwell invasion experiments were employed to study changes in cell migration capacity and cell invasion, respectively. Tumour xenografts were performed to observe lung metastasis of nude mice inoculated with transfected CNE cells. SNHG1 is highly expressed in nasopharyngeal carcinoma tissues and in cell lines. Down-regulation of SNHG1 facilitated the expression of miR-145-5p and further suppressed the level of NAUK1 in CNE and HNE-1 cells. Silencing of SNHG1, up-regulation of miR-145-5p and inhibition of NAUK1 by relative transfection all attenuated the aggressiveness of CNE and HNE-1 cells both in vivo and in vitro. Moreover, the impaired cell migration and invasion by SNHG1 siRNA could be rescued by cotransfection of miR-145-5p in CNE and HNE-1 cells. LncRNA SNHG1 promoted the expression of NUAK1 by down-regulating miR-145-5p and thus promoted the aggressiveness of nasopharyngeal carcinoma cells through AKT signalling pathway and induced epithelial-mesenchymal transition (EMT).

Polycyclic aromatic hydrocarbons exposure and hematotoxicity in occupational population: A two-year follow-up study.

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental and occupational pollutants. To date, the effect and mechanism by which PAHs exposure impaired hematopoietic system remains unclear. METHODS: We examined the capability of PAHs to disrupt hematopoiesis in a study of 639 male participants in China by measuring complete blood counts (CBC) in 2013 and 2014. Gas chromatography-mass spectrometry (GC/MS) method was used to measure airborne levels of PAHs and benzene. We measured 1-hydroxypyrene (1-OHP), S-phenylmercapturic acid (SPMA) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) in urinary by ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS/MS) method. RESULTS: We found decreased dose-response of white blood cells, eosinophils, monocytes and lymphocytes with increased PAHs exposure in two consecutive years. We did not find association between benzene with CBC in our study. After stratification analysis by smoking status, the findings were highly consistent. White blood cells, monocytes and red blood cell counts were decreased in high urinary 8-OHdG group. CONCLUSIONS: Our study showed that PAHs could impair the hematopoietic system independently, and oxidative stress might play an important role in potential hematotoxicity.