Reliability and validity evaluation of the chinese revision of the attitude towards adult vaccination scale.

BACKGROUND: Although vaccination is one of the critical interventions to address global health issues, inadequate vaccination rates has become an international challenge. Vaccine hesitancy is the key to affecting inadequate vaccination rates. According to the WHO SAGE working group's definition, vaccine hesitancy refers to delaying or refusing vaccination and has been ranked as one of the top 10 health threats. There has yet to be a scale that evaluates vaccination attitudes among Chinese adults. However, an attitude quantity, the adult vaccination attitude scale, has been developed to assess adult vaccination attitudes and reasons for vaccine hesitancy. OBJECTIVE: The Adult Attitudes to Vaccination Scale (ATAVAC) was initially developed by Professor Zoi Tsimtsiou et al. This study aimed to analyze the structure of the Chinese version of the ATAVAC and explore the relationship between adult vaccination attitudes, e-health literacy, and medical distrust. METHODS: After obtaining author permission for the initial scales, the study was translated using the Brislin back-translation method. 693 adults were enrolled to the study. To validate this hypothesis, participants finished the socio-demographic questionnaire, the Chinese version of the ATAVAC, the electronic Health Literacy Scale (e-HEALS) and the Medical Mistrust Index (MMI). The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to examine the underlying structure of the factors of the Chinese version of the Adult Vaccination Attitude Scale and to measure its reliability and validity. RESULTS: The Cronbach's alpha coefficient for the Chinese version of the ATAVAC was 0.885, with Cronbach's alpha coefficients ranging from 0.850 to 0.958 for each dimension. The content validity index was 0.90, and the retest reliability was 0.943. The exploratory factor analysis (EFA) supported the 3-factor structure of the translation instrument, and the scale had good discriminant validity. The confirmatory factor analysis (CFA) revealed a degree of freedom of 1.219, a model fit index (GFI) of 0.979, a normative fit index (NFI) of 0.991, a Tucker-Lewis index (TLI) of 0.998, a comparability index (CFI) of 0.998 and a root mean square error of approximation (RMSEA) of 0.026. CONCLUSION: The results show that the Chinese version of the ATAVAC has demonstrated good reliability and validity. Hence, it can be used as an effective tool to assess vaccination attitudes among Chinese adults.

Circular RNA_0001495 increases Robo1 expression by sponging microRNA-527 to promote the proliferation, migration and invasion of bladder cancer cells.

Bladder cancer (BCa) is a heterogeneous disease that poses great threats on public health. Increasing studies have identified the vital functions of circular RNAs (circRNAs) in BCa treatment. Hence, this current study set out to explore the modulatory role of circ_0001495 in BCa development. First, the expression of circ_0001495 was determined by reverse transcription quantitative polymerase chain reaction. Cell biological processes were then analyzed after altering the circ_0001495 expression in T24 cells. Next, interactions among circ_0001495, microRNA-527 (miR-527) and roundabout guidance receptor 1 (Robo1) were investigated by dual luciferase reporter gene assay, RNA pull down assay and FISH assay. Lastly, xenograft tumors in nude mice were established to explore the effect of circ_0001495 in vivo. It was found that circ_0001495 was highly expressed in BCa tissues and cells, and was further correlated with poor prognosis in BCa patients. In addition, circ_0001495 inhibited the activity of miR-527 by acting as a sponge to sponge miR-527, which further elevated the Robo1 expression. Lastly, circ_0001495 was found to promote the proliferation, migration and invasion of BCa cells in vitro through the miR-527/Robo1 axis and promote the growth and metastasis of BCa tumors in vivo. Altogether, findings in our study highlight the promoting role of circ_0001495 in the progression of BCa by increasing Robo1 via sponging miR-527, representing a promising target for BCa management.

Simple and efficient preparation of high-purity trehalulose from the waste syrup of isomaltulose production using solid-phase extraction followed by hydrophilic interaction chromatography.

A simple and efficient method was developed for the preparation of high-purity trehalulose from the waste syrup of isomaltulose production. The waste syrup was pre-treated with C18 solid-phase extraction, where 98% decolorization and 97% reducing sugar recovery were obtained, followed by hydrophilic interaction liquid chromatography separation on a cysteine-bonded zwitterionic column. Under optimized conditions, trehalulose was separated from isomaltulose isomer and prepared on a semi-preparative scale with >99% purity. The structure of the prepared trehalulose was subsequently confirmed by nuclear magnetic resonance, and three tautomers of trehalulose (α-D-glucosylpyranosyl-1,1-ß-D-fructopyranose, α-D-glucosylpyranosyl-1,1-ß-D-fructofuranose, and α-D-glucosylpyranosyl-1,1-α-D-fructofuranose) were detected and completely characterized by 13 C NMR spectroscopy for the first time in this study. The tautomerization of α-D and ß-D type transition was observed by hydrophilic interaction liquid chromatography on an AdvanceBio Glycan Mapping column, with smaller particle size (2.7 µm). Furthermore, the prepared trehalulose was applied as a standard for trehalulose quantification during the sucrose conversion by Klebsiella sp. LX3. The combination of solid-phase extraction and hydrophilic interaction liquid chromatography offers a new avenue for the preparation of sugar isomers from complex natural or fermentation products.

Learning Region-Based Attention Network for Traffic Sign Recognition.

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

Carrier screening for spinal muscular atrophy with a simple test based on melting analysis.

Carrier screening of spinal muscular atrophy (SMA) can provide reproductive options for carriers and prevent the birth defects. Here, we developed a simple screening test based on melting analysis. The test comprises a duplex PCR with two primer pairs and three probes to simultaneous amplify SMN1, SMN2, and CFTR. By analyzing the melting profiles, we were able to determine the SMN1/SMN2 ratio and SMN1 + SMN2 copy number to subsequently determine the copy number of SMN1. Samples with one copy of SMN1 were considered as "high risk for carrier," while samples with ≥2 copies of SMN1 were considered as "low risk for carrier." We evaluated the clinical performance of this test using 215 clinical samples with various genotypes that had been previously confirmed by multiplex ligation-dependent probe amplification (MLPA). The test showed high sensitivity (100%) and specificity (97.1%) as well as high positive (97.3%) and negative (100%) predictive value, and was in perfect agreement with the gold standard test, MLPA (k = 0.97). Moreover, it is rapid, inexpensive, and easy to perform and automate, with high reproducibility and capacity. Therefore, we expect this test will advance carrier screening for SMA.

Preparation of glutathione-functionalized zwitterionic silica material for efficient enrichment of sialylated N-glycopeptides.

A glutathione (GSH)-functionalized silica material was prepared using divinyl sulfone activation chemistry (named SiO2-DVS-GSH). The successful synthesis of the SiO2-DVS-GSH material was confirmed by FT-IR, elemental analysis, and zeta potential analysis. The effects of water content, pH value, and salt concentration in the mobile phase on the model compound (uracil, uridine, cytosine, cytidine, guanosine, xanthosine, orotic acid) retention was studied, and a hydrophilic interaction liquid chromatography (HILIC) retention feature together with electrostatic interaction of the SiO2-DVS-GSH material was observed. The prepared stationary phase was further applied for the separation of oligosaccharide. In addition, the SiO2-DVS-GSH material displayed remarkable selectivity and specificity for the sialylated N-glycopeptides' enrichment from bovine fetuin tryptic digests, even at a mass ratio of 1:1000 (w/w) to bovine serum albumin (BSA, non-glycosylated protein), showing superior performance compared to commercial ZIC-HILIC material. Moreover, the SiO2-DVS-GSH material behaved well in the N-glycopeptides' enrichment from human serum, demonstrating its promising potential for glycoproteomics of complex biological samples. Graphical Abstract A glutathione (GSH)-functionalized silica material was prepared using divinyl sulfone activation chemistry, and it shows remarkable selectivity and specificity for the sialylated N-glycopeptides' enrichment.

RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory.

With the development of low-cost RGB-D (Red Green Blue-Depth) sensors, RGB-D object recognition has attracted more and more researchers' attention in recent years. The deep learning technique has become popular in the field of image analysis and has achieved competitive results. To make full use of the effective identification information in the RGB and depth images, we propose a multi-modal deep neural network and a DS (Dempster Shafer) evidence theory based RGB-D object recognition method. First, the RGB and depth images are preprocessed and two convolutional neural networks are trained, respectively. Next, we perform multi-modal feature learning using the proposed quadruplet samples based objective function to fine-tune the network parameters. Then, two probability classification results are obtained using two sigmoid SVMs (Support Vector Machines) with the learned RGB and depth features. Finally, the DS evidence theory based decision fusion method is used for integrating the two classification results. Compared with other RGB-D object recognition methods, our proposed method adopts two fusion strategies: Multi-modal feature learning and DS decision fusion. Both the discriminative information of each modality and the correlation information between the two modalities are exploited. Extensive experimental results have validated the effectiveness of the proposed method.

Classification of ADHD with bi-objective optimization.

Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classification scheme based on L1-norm support vector machine (SVM). In our classification model, two objectives, namely, the margin of separation and the empirical error are considered at the same time. Then the normal boundary intersection (NBI) method of Das and Dennis is used to solve the bi-objective optimization problem. A representative nondominated set which reflects the entire trade-off information between the two objectives is obtained. Each representative nondominated point in the set corresponds to an efficient classifier. Finally a decision maker can choose a final efficient classifier from the set according to the performance of each classifier. Our scheme avoids the trial and error process for regularization hyper-parameter selection. Experimental results show that our bi-objective optimization classification scheme for ADHD diagnosis performs considerably better than some traditional classification methods.

Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

BACKGROUND: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. METHODS: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. RESULTS: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. CONCLUSIONS: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

[Extracting target from blurred midwave infrared image based on immune template clustering].

Extracting targets from a blurred midwave infrared image is a challenging task due to the fuzziness of the image. Inspired by the coordination mechanism between biological innate immunity and adaptive immunity, an immune template clustering targets extraction method is proposed, which based on imaging mechanism and template statistical property of midwave image. Firstly, by learning from the recognition function of innate immunity and maximizing the between-cluster variance, a midwave blurred infrared image is segmented into a target pixel set, a background pixel set and a blurred pixel set. Secondly, according to the presentation function of innate immunity, the frequency domain template features of pixels in midwave blurred infrared image are extracted. Finally, adaptive immune clustering is completed for the blurred pixel set based on frequency domain template feature, in order to divide each blurred pixel into target pixel or background pixel. Experimental results show that the proposed algorithm can extract targets from a midwave blurred infrared image efficiently. Compared with classical edge template and conventional region template methods, the immune template clustering method has better extraction efficiency, absolute error rate and coincidence degree with ground truth.

Concentration-dependent dual roles of proanthocyanidins on oxidative stress and docosahexaenoic acid production in Schizochytrium sp. ATCC 20888.

Antioxidant addition is an effective strategy to achieve docosahexaenoic acid (DHA) overproduction in oleaginous microorganisms. Nevertheless, antioxidants like phenolic compounds sometimes exert pro-oxidant activity. In this work, effects of proanthocyanidins (PAs) on fermentation performance and oxidative stress in Schizochytrium sp. were investigated. Low PAs addition (5 mg/L) reduced reactive oxygen species and enhanced lipogenic enzymes activities and NADPH, resulting in significant increase in lipid (20.3 g/L) by 33.6 % and DHA yield (9.8 g/L) by 53.4 %. In contrast, high PAs addition (500 mg/L) exerted pro-oxidant effects, aggravated oxidative damage and lipid peroxidation, leading to sharp decrease in biomass (21.3 g/L) by 35.1 %, lipid (8.2 g/L) by 46.0 %, and DHA (2.9 g/L) by 54.8 %. Therefore, the antioxidant concentration is especially crucial in DHA production. This study is the first to report concentration-dependant dual roles of PAs in oxidative stress and DHA production in Schizochytrium sp., providing new insights into microbial DHA production.

Contrastive Masked Autoencoders are Stronger Vision Learners.

Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty to go for making a stronger vision learner. Towards this goal, we propose Contrastive Masked Autoencoders (CMAE), a new self-supervised pre-training method for learning more comprehensive and capable vision representations. By elaboratively unifying contrastive learning (CL) and masked image model (MIM) through novel designs, CMAE leverages their respective advantages and learns representations with both strong instance discriminability and local perceptibility. Specifically, CMAE consists of two branches where the online branch is an asymmetric encoder-decoder and the momentum branch is a momentum updated encoder. During training, the online encoder reconstructs original images from latent representations of masked images to learn holistic features. The momentum encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart. To make CL compatible with MIM, CMAE introduces two new components, i.e., pixel shifting for generating plausible positive views and feature decoder for complementing features of contrastive pairs. Thanks to these novel designs, CMAE effectively improves the representation quality and transfer performance over its MIM counterpart. CMAE achieves the state-of-the-art performance on highly competitive benchmarks of image classification, semantic segmentation and object detection. Notably, CMAE-Base achieves 85.3% top-1 accuracy on ImageNet and 52.5% mIoU on ADE20k, surpassing previous best results by 0.7% and 1.8% respectively.

N-desmethyldauricine from Menispermum dauricum DC suppresses triple-negative breast cancer growth in 2D and 3D models by downregulating the NF-κB signaling pathway.

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, for which targeted therapy regimens are lacking. The traditional Chinese medicine Menispermum dauricum DC (M. dauricum) and its compounds have been reported to have antitumor activity against various cancers; however, their anti-TNBC activity is unknown. In this work, dauricine and N-desmethyldauricine from M. dauricum were separated and identified to have anti-TNBC via a multi-component bioactivity and structure-guided method. The cell counting kit 8 assay showed that dauricine and N-desmethyldauricine inhibited the proliferation of four tested TNBC cell lines, with half maximal inhibitory concentration values ranging from 5.01 µM to 13.16 µM. Further research suggested that N-desmethyldauricine induced cell apoptosis, arrested cell cycle progression in the G0/G1 phase, and inhibited cell migration. Western blot analysis revealed that the proapoptotic protein cleaved-poly-ADP-ribose polymerase 1 was upregulated, and the G0/G1 phase-related proteins cyclin-dependent kinase 2 and cyclin D1 and the migration-related protein matrix metallopeptidase 9 were downregulated. Furthermore, N-desmethyldauricine decreased the protein expression of p65, an important subunit of nuclear factor kappa-beta (NF-κB). Moreover, an antiproliferation assay of three-dimensional (3D) tumor spheroids showed that N-desmethyldauricine diminished cell‒cell adhesion and suppressed the growth of TNBC 3D spheroids. Taken together, these findings indicate that N-desmethyldauricine inhibited the proliferation of TNBC cells and decreased the expression of p65 in the NF-κB pathway.

Learning Degradation-Robust Spatiotemporal Frequency-Transformer for Video Super-Resolution.

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges remain to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. This work proposes a novel degradation-robust Frequency-Transformer (FTVSR++) for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a "divided attention" which conducts joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR++ outperforms state-of-the-art methods on different low-quality videos with clear visual margins.

Offline two-dimensional normal-phase × reversed-phase liquid chromatography coupled with high-resolution mass spectrometry for comprehensive analysis of chemical constituents in Euphorbia kansui.

Euphorbia kansui is the dried root of Euphorbia kansui T. N. Liou ex T.P. Wang. Its main chemical components are diterpenoids, triterpenes, and volatile oil. In this study, an offline two-dimensional (2D) normal-phase × reversed-phase liquid chromatography method coupled with quadrupole time-of-flight mass spectrometry was established to comprehensively analyze the chemical constituents in E. kansui. A total of 240 compounds were identified from the E. kansui extract, including 218 diterpenoids (77 known, 141 new), 16 known volatile oils, and six known triterpenes. The relationship between the structural characteristics and tandem mass spectroscopy fragments of diterpenoids was further analyzed. Based on the characteristic fragment ions, 141 new diterpenoids were determined as 118 ingenane diterpenoids and 23 jatrophane diterpenoids. The newly identified diterpenoids may provide lead compounds for drug discovery, improving the medicinal value of E. kansui.

Real-Time Micromotor Probe for Immune Neutrophil Activation State.

Neutrophil activation is a hallmark of the immune response. Approaches to identify neutrophil activation in real time are necessary but are still lacking. In this study, magnetic Spirulina micromotors are used as label-free probes that exhibit differences in motility under different neutrophil activation states. This is correlated with different secretions into the extracellular environment by activated/non-activated cells and local environmental viscoelasticity. The micromotor platform can bypass non-activated immune cells while being stopped by activated cells. Thus, the micromotors can serve as label-free biomechanical probes of the immune cell state. They can detect the activation state of target immune cells in real time and with single-cell precision, which provides new ideas for the diagnosis and treatment of diseases while deepening understanding of the biomechanics of activated immune cells.

Adaptive particle patterning in the presence of active synthetic nanomotors.

For the maintenance of a biological system, spatial organization of material condensates within the cell through the dissipation of energy is crucial. Besides directed transport via microtubules, material arrangement can be achieved via motor protein facilitated adaptive active diffusiophoresis. For example, the distribution of membrane proteins during the cell division of Escherichia coli is affected by the MinD system. Synthetic active motors exhibit the ability to simulate natural motors. Here we propose an active Au-Zn nanomotor driven by water and discovered an interesting adaptive interaction mode of the diffusiophoretic Au-Zn nanomotors with passive condensate particles in different environments. It is found that the attraction/repulsion between the nanomotor and passive particles is adaptive, while an interesting hollow pattern is formed with a negatively charged substrate and a cluster pattern is favored with a positively charged substrate.

Mobile mechanical signal generator for macrophage polarization.

The importance of mechanical signals in regulating the fate of macrophages is gaining increased attention recently. However, the recently used mechanical signals normally rely on the physical characteristics of matrix with non-specificity and instability or mechanical loading devices with uncontrollability and complexity. Herein, we demonstrate the successful fabrication of self-assembled microrobots (SMRs) based on magnetic nanoparticles as local mechanical signal generators for precise macrophage polarization. Under a rotating magnetic field (RMF), the propulsion of SMRs occurs due to the elastic deformation via magnetic force and hydrodynamics. SMRs perform wireless navigation toward the targeted macrophage in a controllable manner and subsequently rotate around the cell for mechanical signal generation. Macrophages are eventually polarized from M0 to anti-inflammatory related M2 phenotypes by blocking the Piezo1-activating protein-1 (AP-1）-CCL2 signaling pathway. The as-developed microrobot system provides a new platform of mechanical signal loading for macrophage polarization, which holds great potential for precise regulation of cell fate.

Clinical study of the combined use of dexmedetomidine and remifentanil in patients with coronary heart disease undergoing 3D laparoscopic surgery with EEG bispectral index monitoring.

OBJECTIVE: This study sought to investigate the safety and clinical outcomes associated with the combined administration of dexmedetomidine (Dex) and remifentanil (Rem) in patients with coronary heart disease undergoing three-dimensional (3D) laparoscopic surgery, with concurrent monitoring of the electroencephalography (EEG) bispectral index. METHODS: This study is of a retrospective nature and involved a total of 60 patients with coronary heart disease who underwent 3D laparoscopic surgery at our hospital between June 2020 and September 2021. In a double-blind manner, these patients were randomly assigned to two groups: the control group (Group I), which consisted of 30 patients, and the treatment group (Group II) receiving a combination of Dex and Rem, also comprising 30 patients. The study's primary objective was to compare and assess the treatment outcomes in these two patient groups. RESULTS: Patients in Group II who developed postoperative coronary heart disease experienced a significant reduction in blood pressure, heart rate, and electrocardiogram values (P<0.05). Additionally, Group II exhibited lower bispectral index (BIS) and visual analog scale (VAS) values (P<0.05). CONCLUSION: In patients with coronary heart disease undergoing 3D laparoscopic surgery, the intraoperative use of Dex combined with Rem anesthesia offers several advantages. It helps stabilize hemodynamics, reducing the risk of myocardial ischemia, and significantly alleviates postoperative pain, all without increasing the likelihood of adverse postoperative reactions. Furthermore, this approach effectively dampens the intraoperative and postoperative stress response, facilitating enhanced recovery after surgery (ERAS). Overall, the clinical impact is positive, safe, and reliable.

Hemp Seed Fermented by Aspergillus oryzae Attenuates Lipopolysaccharide-Stimulated Inflammatory Responses in N9 Microglial Cells.

The objective of our present work was to explore the possible enhanced anti-neuroinflammatory ability of Aspergillus oryzae fermented hemp seed in lipopolysaccharide (LPS)-stimulated N9 microglial cells and elucidate its underlying mechanism. The water extract of hemp seed was fermented by Aspergillus oryzae. LPS-stimulated N9 microglial cells were employed for the inflammatory cell model. The release of nitric oxide (NO) was determined by Griess assay. The cytokines and inflammatory mediator expression were measured by qPCR and ELISA. The phosphorylated key signaling proteins, including nuclear factor-κB (NF-κB), mitogen-activated protein kinases (MAPKs), and phosphatidylinositol 3-kinase (PI3K/Akt), were quantified by western blot analysis. The production of intracellular reactive oxygen species (ROS) was measured by DCFH oxidation. Fermented hemp seed (FHS) reduced NO production by downregulating inducible nitric oxide synthase (iNOS) expression in LPS-stimulated N9 microglial cells. FHS treatment decreased LPS-stimulated expression of inflammatory cytokines either on mRNA or protein levels. Moreover, FHS inhibited LPS-stimulated phosphorylation of NF-κB, MAPKs, and PI3K/Akt signaling pathways. Furthermore, FHS significantly reduced the ROS production in the cells. It was concluded that FHS exerted its anti-neuroinflammatory activities by suppressing ROS production, thus inhibiting NF-κB, MAPKs, and PI3K/Akt activation, consequently decreasing the expression levels of inflammatory mediators and cytokines.