Analysis of the clinical diagnosis and treatment of fetal meconium peritonitis.

BACKGROUND: The purpose of this study was to improve diagnostic and therapeutic standards by examining the clinical features, treatment, and prognosis of fetal meconium peritonitis (FMP), as well as the diagnostic efficacy of ultrasound for FMP. METHODS: The clinical data of 41 infants and pregnant women diagnosed with meconium peritonitis (MP) and treated at the Fujian Maternal and Child Health Hospital from January 2013 to January 2020 were analyzed retrospectively. Clinical data, imaging data, complications, treatment strategies, pregnancy outcomes, neonatal prognoses, and follow-up outcomes were all analyzed. RESULTS: The MP prenatal diagnosis rate was 56.1% (23/41), the neonatal surgery rate was 53.7% (22/41), and the survival rate was 85.4% (35/41). Intraperitoneal calcification (23 pregnant women, 56.1%), intestinal dilatation (13 pregnant women, 31.7%), peritoneal effusion (22 pregnant women, 53.7%), intraperitoneal pseudocyst (7 pregnant women, 17.1%), and polyhydramnios were diagnosed via prenatal ultrasound (18 pregnant women, 43.9%). Twenty-two pregnant women were assigned to the surgical treatment (operation) group, while 18 were assigned to the conservative treatment group. In the operation group, there were 9 cases of ileal atresia (40.9%), 7 cases of jejunal atresia (31.8%), 2 cases of atresia at the jejunum-ileum junction (9.1%), 2 cases of ileal perforation (9.1%), 1 case of ileal necrosis (4.5%), and 1 case of adhesive obstruction (4.5%). There was no statistically significant difference (p > .05) in the occurrence of various prenatal ultrasound findings by etiology. CONCLUSION: Multiple prenatal ultrasound markers have been identified for MP. To improve the efficacy of newborn treatment for FMP and reduce neonatal mortality, dynamic monitoring of ultrasound image alterations and strengthened integrated perinatal management are necessary.

Nonintrusive wind blade fault detection using a deep learning approach by exploring acoustic information.

Various physical characteristics, including ultrasonic waves, active acoustic emissions, vibrations, and thermal imaging, have been used for blade fault detection. In this work, we propose using the sound produced by spinning wind blades to identify faults. To the best of our knowledge, passive acoustic information has not yet been explored for this task. In particular, we develop three networks targeting different scenarios. The main contributions of this work are threefold. First, when normal and aberrant data are available for supervised learning, an attention-convolutional recurrent neural network is designed to show the feasibility of using passive sound information to conduct fault detection. Second, in the absence of abnormal training data, we build a normal-encoder network to learn the distributions of normal data through semisupervised learning, which avoids the requirement of abnormal training data. Third, when multiple devices are used to collect the data, due to different properties of devices, there is a domain mismatch issue. To overcome this, we create an adversarial domain adaptive network to close the gap between the source and target domains. Acoustic signal datasets of actual wind turbine operations are collected to evaluate our fault detection systems. The findings demonstrate that the proposed systems offer high classification accuracy and indicate the feasibility of passive acoustic signal-based wind turbine blade fault detection with one step close to automatic detection.

Fatty Acid Synthase Mutations Predict Favorable Immune Checkpoint Inhibitor Outcome and Response in Melanoma and Non-Small Cell Lung Cancer Patients.

Fatty acid synthase (FASN) acts as the central member in fatty acid synthesis and metabolism processes, which regulate oncogenic signals and tumor immunogenicity. To date, no studies have reported the connection of FASN mutations with ICI efficacy. In this study, from 631 melanoma and 109 NSCLC patients who received ICI treatments, we retrospectively curated multiomics profiles and ICI treatment data. We also explored the potential molecular biological mechanisms behind FASN alterations. In melanoma patients, FASN mutations were observed to associate with a preferable immunotherapeutic prognosis and response rate (both p < 0.01). These connections were further corroborated by the NSCLC patients (both p < 0.01). Further analyses showed that a favorable tumor immunogenicity and immune microenvironment were involved in FASN mutations. This work confirms the clinical immunotherapy implications of FASN mutation-mediated fatty acid metabolism and provides a possible indicator for immunotherapy prognosis prediction.

Chemical Exchange Saturation Transfer (CEST) Signal at -1.6 ppm and Its Application for Imaging a C6 Glioma Model.

The chemical exchange saturation transfer (CEST) signal at -1.6 ppm is attributed to the choline methyl on phosphatidylcholines and results from the relayed nuclear Overhauser effect (rNOE), that is, rNOE(-1.6). The formation of rNOE(-1.6) involving the cholesterol hydroxyl is shown in liposome models. We aimed to confirm the correlation between cholesterol content and rNOE(-1.6) in cell cultures, tissues, and animals. C57BL/6 mice (N = 9) bearing the C6 glioma tumor were imaged in a 7 T MRI scanner, and their rNOE(-1.6) images were cross-validated through cholesterol staining with filipin. Cholesterol quantification was obtained using an 18.8-T NMR spectrometer from the lipid extracts of the brain tissues from another group of mice (N = 3). The cholesterol content in the cultured cells was manipulated using methyl-ß-cyclodextrin and a complex of cholesterol and methyl-ß-cyclodextrin. The rNOE(-1.6) of the cell homogenates and their cholesterol levels were measured using a 9.4-T NMR spectrometer. The rNOE(-1.6) signal is hypointense in the C6 tumors of mice, which matches the filipin staining results, suggesting that their tumor region is cholesterol deficient. The tissue extracts also indicate less cholesterol and phosphatidylcholine contents in tumors than in normal brain tissues. The amplitude of rNOE(-1.6) is positively correlated with the cholesterol concentration in the cholesterol-manipulated cell cultures. Our results indicate that the cholesterol dependence of rNOE(-1.6) occurs in cell cultures and solid tumors of C6 glioma. Furthermore, when the concentration of phosphatidylcholine is carefully considered, rNOE(-1.6) can be developed as a cholesterol-weighted imaging technique.

MIAOME: Human microbiome affect the host epigenome.

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/.

Change of nutrients, microorganisms, and physical properties of exposed extensive green roof substrate.

Green roofs (GR) can be used as a nature-based solution to tackle eco-environmental problems caused by climate change and rapid urbanization. The substrate in the GRs is the growing medium for vegetation, and its properties directly affect the ecosystem services of GRs. To investigate the characteristic changes of an exposed substrate after the removal of vegetation, a one-year field experiment was conducted. Substrate properties were comprehensively compared for areas in GR that were planted with Sedum lineare and those with bare substrate. Results show that vegetation cover not only prevented substrate loss by 5.14% (p < 0.05) but also protected the chemical, microbial, and physical properties of the substrate. Moreover, the structure of the substrate changed, as evidenced by a significant increase in fine sand (p < 0.05). The results highlight that attention should be paid to maintaining vegetation cover during GR management. In addition, extensive GRs may not be suitable for fallowing. Once a GR has been established, it needs regular maintenance. Otherwise, the ecological and economic benefits of the GR may be reduced. The findings of the present study can be used to determine the life-cycle costs. Further research should focus on differences in the substrate loss rates, runoff, and temperatures of the substrates under exposure and vegetation cover. The microbial changes after revegetation should also be studied to clarify the role of vegetation in GR ecosystems. The present study provides a reference for improving GR management and ensuring their sustainability.

Integrative analysis of the metabolome and transcriptome reveal the phosphate deficiency response pathways of alfalfa.

Understanding the mechanisms underlying the responses to inorganic phosphate (Pi) deficiency in alfalfa will help enhance Pi acquisition efficiency and the sustainable use of phosphorous resources. Integrated global metabolomic and transcriptomic analyses of mid-vegetative alfalfa seedlings under 12-day Pi deficiency were conducted. Limited seedling growth were found, including 13.24%, 16.85% and 33.36% decreases in height, root length and photosynthesis, and a 24.10% increase in root-to-shoot ratio on day 12. A total of 322 and 448 differentially abundant metabolites and 1199 and 1061 differentially expressed genes were identified in roots and shoots. Increased (>3.68-fold) inorganic phosphate transporter 1;4 and SPX proteins levels in the roots (>2.15-fold) and shoots (>2.50-fold) were related to Pi absorption and translocation. The levels of phospholipids and Pi-binding carbohydrates and nucleosides were decreased, while those of phosphatases and pyrophosphatases in whole seedlings were induced under reduced Pi. In addition, nitrogen assimilation was affected by inhibiting high-affinity nitrate transporters (NRT2.1 and NRT3.1), and nitrate reductase. Increased delphinidin-3-glucoside might contribute to the gray-green leaves induced by Pi limitation. Stress-induced MYB, WRKY and ERF transcription factors were identified. The responses of alfalfa to Pi deficiency were summarized as local systemic signaling pathways, including root growth, stress-related responses consisting of enzymatic and nonenzymatic systems, and hormone signaling and systemic signaling pathways including Pi recycling and Pi sensing in the whole plant, as well as Pi recovery, and nitrate and metal absorption in the roots. This study provides important information on the molecular mechanism of the response to Pi deficiency in alfalfa.

A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement.

The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility.

pH Mapping of Skeletal Muscle by Chemical Exchange Saturation Transfer (CEST) Imaging.

Magnetic resonance imaging (MRI) is extensively used in clinical and basic biomedical research. However, MRI detection of pH changes still poses a technical challenge. Chemical exchange saturation transfer (CEST) imaging is a possible solution to this problem. Using saturation transfer, alterations in the exchange rates between the solute and water protons because of small pH changes can be detected with greater sensitivity. In this study, we examined a fatigued skeletal muscle model in electrically stimulated mice. The measured CEST signal ratio was between 1.96 ppm and 2.6 ppm in the z-spectrum, and this was associated with pH values based on the ratio between the creatine (Cr) and the phosphocreatine (PCr). The CEST results demonstrated a significant contrast change at the electrical stimulation site. Moreover, the pH value was observed to decrease from 7.23 to 7.15 within 20 h after electrical stimulation. This pH decrease was verified by 31P magnetic resonance spectroscopy and behavioral tests, which showed a consistent variation over time.

A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor.

The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.

D-serine Ameliorates Motor and Cognitive Impairments in ß-amyloid 1-42 Injected Mice by Inhibiting JNK Signaling Pathway.

The senile plaque formed by ß-amyloid (Aß) deposition in the brain is one of the main pathological features of Alzheimer's disease (AD), and the c-Jun N-terminal kinase (JNK) signaling pathway plays an important role in the pathogenesis of AD. This study aimed to investigate that D-serine may ameliorate motor and cognitive impairment in Aß injected mice by inhibiting JNK signaling pathway. Firstly, Kunming mice were injected intrahippocampally with Aß1-42 to build AD model. The mice were injected intraperitoneally with saline, D-serine, D-amino acid oxidase (DAAO), and Sodium benzoate (BE) for 10 consecutive days, respectively. Subsequently, the motor and cognitive functions of mice were detected by behavioral tests. The silver staining and immunohistochemical methods were used to detect the distributions of Aß in the hippocampus of mice. 18F-2-Fluro-D-deoxy-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans were performed to detected glucose metabolism of Aß1-42 induced lesions. The expressions of relative JNK factors were detected by immunohistochemistry and Western blot methods. These results showed that Aß severely impaired the motor and memory abilities of mice. The expressions of glial fibrillary acidic protein (GFAP), tumor necrosis factor (TNF-α), N-methyl-D-aspartate receptor 1 (NMDAR1), phospho-JNK (p-JNK), p-c-Jun and activating transcription factor 2 (ATF2) increased significantly. After D-serine treatment, the abilities of movement and memory of mice were improved, and the clearance rate of Aß was accelerated. The expressions of GFAP, TNF-α, NMDAR1, p-JNK, p-c-Jun and ATF2 decreased significantly. DAAO and BE were administered to further validate these results. Therefore, this study showed that D-serine could alleviate the cognitive impairment of Aß1-42 injected mice by inhibiting JNK signaling pathway. These results provide more evidences for the effect of D-serine on AD and relevant mechanism to treat AD.

Role of the cholesterol hydroxyl group in the chemical exchange saturation transfer signal at -1.6 ppm.

Chemical exchange saturation transfer (CEST) can provide metabolite-weighted images in the clinical setting; therefore, understanding the origin of each CEST signal is essential to revealing the changes in diseases at the molecular level, which would provide further insight for diagnoses and treatments. The CEST signal at -1.6 ppm is attributed to the choline methyl group of phosphatidylcholines. The methyl groups have no exchangeable protons, so the corresponding CEST signals must result from the relayed nuclear Overhauser effect (rNOE); however, the detailed mechanism remains unclear. Cholesterol is a major component of biological membranes, and its content is closely related to the dynamics and phases of these lipids. However, cholesterol has a hydroxyl group, which could participate in proton exchange to complete the rNOE process. In this study, we used liposomes containing cholesterol and its analogs (5α-cholestane and progesterone), which presumably have similar capabilities of influencing lipid bilayers, and found that the steroid hydroxyl group is the key to inducing the rNOE at -1.6 ppm. Our results suggest that the origin of the rNOE at -1.6 ppm likely requires an intermolecular NOE between the proton of the choline methyl group and that of the cholesterol hydroxyl group, and a chemical exchange between the cholesterol hydroxyl group and bulk water. However, the phenomenon in which the rNOE at -1.6 ppm appears when the cholesterol concentration is high seems to contradict the in vivo results, suggesting a more complicated mechanism associated with the rNOE at -1.6 ppm in biological membranes.

CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization.

Compressed sensing (CS) significantly accelerates magnetic resonance imaging (MRI) by allowing the exact reconstruction of image from highly undersampling k-space data. In this process, the high sparsity obtained by the learned dictionary and exploitation of correlation among patches are essential to the reconstructed image quality. In this paper, by a use of these two aspects, we propose a novel CS-MRI model based on analysis dictionary learning and manifold structure regularization (ADMS). Furthermore, a proper tight frame constraint is used to obtain an effective overcomplete analysis dictionary with a high sparsifying capacity. The constructed manifold structure regularization nonuniformly enforces the correlation of each group formed by similar patches, which is more consistent with the diverse nonlocal similarity in realistic images. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM), in which the fast algorithm for each sub-problem is separately developed. The experimental results demonstrate that main components in the proposed method contribute to the final reconstruction performance and the effectiveness of the proposed model.

Volatile Oil from Acorus gramineus Ameliorates the Injury Neurons in the Hippocampus of Amyloid Beta 1-42 Injected Mice.

In recent years, the extraction fraction of volatile oil from Acorus gramineus has significant effects on anti-dementia and improving the learning and memory of animals. To date, limited studies have determined whether volatile oil from A. gramineus has the protective effect on neuronal damage. The aim of this study was to investigate the protective effects of volatile oil from A. gramineus on Alzheimer's disease (AD) mice, by means of behavior test, immunohistochemistry and western blot methods. In this study, mice were injected with Aß1-42 in the bilateral hippocampus to establish the AD model. On the seventh day after modeling, the mice with cognitive dysfunction were selected by the novel object recognition task. Subsequently, the volatile oil treatment groups underwent intragastric administration for per 10 g body weight 2.5 or 5 µL volatile oil from A. gramineus for 3 weeks. The control group and the AD group were given the same amount of saline. Our results showed that after treatment of volatile oil from A. gramineus, the number of Doublecortin and Nestin positive cells increased significantly, suggesting that the volatile oil from A. gramineus may induce the regeneration of hippocampal neurons in mice, and promote the growth of hippocampal neurons by upregulation of brain-derived neurotrophic factor, tyrosine protein kinase B, and neurotrophin-3 expression. These results might provide more experimental evidences for underlying mechanism about the neuroprotective effects of volatile oil from A. gramineus against AD relevant symptoms. Anat Rec, 302:2261-2270, 2019. © 2019 American Association for Anatomy.

Pretreatment of Grape Seed Proanthocyanidin Extract Exerts Neuroprotective Effect in Murine Model of Neonatal Hypoxic-ischemic Brain Injury by Its Antiapoptotic Property.

Grape seed proanthocyanidin extract (GSPE), an active component extracted from the grape, has been reported to demonstrate antioxidant, anti-inflammatory, anticancer, and antiapoptosis effects. However, little is known about the role of GSPE on neonatal hypoxic-ischemic (HI) brain injury. The aim of this study was to evaluate the neuroprotective effect of GSPE pretreatment on neonatal HI brain injury in mice. A modified Rice-Vannucci method was performed to induce neonatal HI brain injury in the 7-day-old mouse pups pretreated with GSPE or vehicle. The infarct volumes were determined by TTC staining. TUNEL staining was used to detect cells apoptosis, and the expressions of apoptosis-related proteins: bax, bcl2, and cleaved caspase-3 were assayed by Western blot. Behavioral tests were also conducted to assess the functional recovery after injury. We showed that the brain damage and neurobehavioral outcomes improvement was observed in GSPE pretreated group. GSPE was proved to suppress apoptosis through inhibition of bax and cleaved caspase-3 expression. It demonstrates that GSPE could alleviate brain damage maybe through its antiapoptotic activity in a neonatal HI brain injury model, and GSPE has the potential to be a new drug for effective prevention of this disorder.

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification.

In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods.

An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm.

The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target's scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.

Reconstruction of Single Image from Multiple Blurry Measured Images.

The problem of blind image recovery using multiple blurry images of the same scene is addressed in this paper. To perform blind deconvolution, which is also called blind image recovery, the blur kernel and image are represented by groups of sparse domains to exploit the local and nonlocal information such that a novel joint deblurring approach is conceived. In the proposed approach, the group sparse regularization on both the blur kernel and image is provided, where the sparse solution is promoted by -norm. In addition, the reweighted data fidelity is developed to further improve the recovery performance, where the weight is determined by the estimation error. Moreover, to reduce the undesirable noise effects in group sparse representation, distance measures are studied in the block matching process to find similar patches. In such a joint deblurring approach, a more sophisticated two-step interactive process is needed in which each step is solved by means of the well-known split Bregman iteration algorithm, which is generally used to efficiently solve the proposed joint deblurring problem. Finally, numerical studies, including synthetic and real images, demonstrate that the performance of this joint estimation algorithm is superior to the previous state-of-the-art algorithms in terms of both objective and subjective evaluation standards. The recovery results of real captured images using unmanned aerial vehicles are also provided to further validate the effectiveness of the proposed method.

Extract of Ginkgo biloba promotes neuronal regeneration in the hippocampus after exposure to acrylamide.

Previous studies have demonstrated a neuroprotective effect of extract of Ginkgo biloba against neuronal damage, but have mainly focused on antioxidation of extract of Ginkgo biloba. To date, limited studies have determined whether extrasct of Ginkgo biloba has a protective effect on neuronal damage. In the present study, acrylamide and 30, 60, and 120 mg/kg extract of Ginkgo biloba were administered for 4 weeks by gavage to establish mouse models. Our results showed that 30, 60, and 120 mg/kg extract of Ginkgo biloba effectively alleviated the abnormal gait of poisoned mice, and up-regulated protein expression levels of doublecortin (DCX), brain-derived neurotrophic factor, and growth associated protein-43 (GAP-43) in the hippocampus. Simultaneously, DCX- and GAP-43-immunoreactive cells increased. These findings suggest that extract of Ginkgo biloba can mitigate neurotoxicity induced by acrylamide, and thereby promote neuronal regeneration in the hippocampus of acrylamide-treated mice.

[Prevalence of bacterial vaginosis in married women of reproductive age in the rural area of Shandong province and its risk factors].

OBJECTIVE: To explore the prevalence of bacterial vaginosis of married women of reproductive age in the rural area of Shandong province and its affecting factors, with an aim to provide references for setting up preventing measures for this disease. METHODS: A stratified, population-based, cluster sampling method was employed. The epidemiological investigation on bacterial vaginosis of married women of reproductive age was carried out at four sampling fields. RESULTS: The prevalence of bacterial vaginosis was 6.64%. The main risk factors included: lacked of personal bathtub and towel; did not use sterilized paper on menstrual phase; had no knowledge that gonorrhea, syphilis, AIDS, and trichomonal vaginitis can be transmitted through sexual intercourse. CONCLUSION: The prevalence of bacterial vaginosis in married women of reproductive age in Shandong rural area is high, which correlates with the lower education level, limited health knowledge, and unhealthy behavior.