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  1. Liu H, Liu Y, Dong X, Liu H, Han B
    Front Psychol, 2021;12:755635.
    PMID: 34925159 DOI: 10.3389/fpsyg.2021.755635
    Studies investigating age-related positivity effects during facial emotion processing have yielded contradictory results. The present study aimed to elucidate the mechanisms of cognitive control during attentional processing of emotional faces among older adults. We used go/no-go detection tasks combined with event-related potentials and source localization to examine the effects of response inhibition on age-related positivity effects. Data were obtained from 23 older and 23 younger healthy participants. Behavioral results showed that the discriminability index (d') of older adults on fear trials was significantly greater than that of younger adults [t(44)=2.37, p=0.024, Cohen's d=0.70], whereas an opposite pattern was found in happy trials [t(44)=2.56, p=0.014, Cohen's d=0.75]. The electroencephalography results on the amplitude of the N170 at the left electrode positions showed that the fear-neutral face pairs were larger than the happy-neutral ones for the younger adults [t(22)=2.32, p=0.030, Cohen's d=0.48]; the older group's right hemisphere presented similar tendency, although the results were not statistically significant [t(22)=1.97, p=0.061, Cohen's d=0.41]. Further, the brain activity of the two hemispheres in older adults showed asymmetrical decrement. Our study demonstrated that the age-related "positivity effect" was not observed owing to the depletion of available cognitive resources at the early attentional stage. Moreover, bilateral activation of the two hemispheres may be important signals of normal aging.
  2. He L, Soh KL, Yu J, Chen A, Dong X
    Front Psychiatry, 2023;14:1094360.
    PMID: 37324817 DOI: 10.3389/fpsyt.2023.1094360
    OBJECTIVE: This study aimed to evaluate and conclude the quality of critically systematic reviews (SRs) of the efficacy of family-centered interventions on perinatal depression.

    METHODS: SRs of the efficacy of family-centered interventions on perinatal depression were systematically searched in nine databases. The retrieval period was from the inception of the database to December 31, 2022. In addition, two reviewers conducted an independent evaluation of the quality of reporting, bias risk, methodologies, and evidence using ROBIS (an instrument for evaluating the bias risk of SRs), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), AMSTAR 2 (an assessment tool for SRs), and grading of recommendations, assessment, development and evaluations (GRADE).

    RESULTS: A total of eight papers satisfied the inclusion criteria. In particular, AMSTAR 2 rated five SRs as extremely low quality and three SRs as low quality. ROBIS graded four out of eight SRs as "low risk." Regarding PRISMA, four of the eight SRs were rated over 50%. Based on the GRADE tool, two out of six SRs rated maternal depressive symptoms as "moderate;" one out of five SRs rated paternal depressive symptoms as "moderate;" one out of six SRs estimated family functioning as "moderate," and the other evidence was rated as "very low" or "low." Of the eight SRs, six (75%) reported that maternal depressive symptoms were significantly reduced, and two SRs (25%) were not reported.

    CONCLUSION: Family-centered interventions may improve maternal depressive symptoms and family function, but not paternal depressive symptoms. However, the quality of methodologies, evidence, reporting, and bias of risk in the included SRs of family-centered interventions for perinatal depression was not satisfactory. The above-mentioned demerits may negatively affect SRs and then cause inconsistent outcomes. Therefore, SRs with a low risk of bias, high-quality evidence, standard reporting, and strict methodology are necessary to provide evidence of the efficacy of family-centered interventions for perinatal depression.

  3. Dong X, Xu S, Liu Y, Wang A, Saripan MI, Li L, et al.
    Cancer Imaging, 2020 Aug 01;20(1):53.
    PMID: 32738913 DOI: 10.1186/s40644-020-00331-0
    BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge.

    METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.

    RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.

    CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.

  4. Wei Y, Wang D, Li G, Yu H, Dong X, Jiang H
    Water Sci Technol, 2023 Nov;88(10):2566-2580.
    PMID: 38017678 DOI: 10.2166/wst.2023.365
    In recent years, chemical water treatment equipment has gained significant attention due to its environmental-friendly features, multifunctionality, and broad applicability. Recognizing the limitations of existing chemical treatment equipment, such as challenges in scale removal and the high water content in scale deposits, we propose a novel drum design for both anode and cathode, enabling simultaneous scale suction and dehydration. We constructed a small experimental platform to validate the equipment's performance based on our model. Notably, under the optimal operating parameters, the hardness removal rate for circulating water falls within the range of 19.6-24.46%. Moreover, the scale accumulation rate per unit area and unit time reaches 13.7 g h-1 m-2. Additionally, the energy consumption per unit weight of the scale remains impressively low at 0.16 kWh g-1. Furthermore, the chemical oxygen demand (COD) concentration decreased from an initial 106.0 mg L-1 to a mere 18.8 mg L-1, resulting in a remarkable total removal rate of 82.26%. In conclusion, our innovative electrochemical water treatment equipment demonstrates exceptional performance in scale removal, organic matter degradation, and water resource conservation, offering valuable insights for future research and development in chemical treatment equipment and electrochemical theory.
  5. Chen BJ, Liu Y, Yang K, Li X, Dong X, Guan Y, et al.
    Food Chem X, 2023 Dec 30;20:100913.
    PMID: 38144747 DOI: 10.1016/j.fochx.2023.100913
    This study aimed to evaluate the efficacy of amylase in hydrolyzing complex carbohydrates of different parts of Ganoderma spp. The aqueous extracts of the Ganoderma samples were analyzed for their selected nutritional composition and physicochemical properties. The purified extracts were also structurally characterized. The aqueous canopy extracts of red-purple Ganoderma had a notably higher total sugar and saponin content than their stalks, but not for the black-type Ganoderma. The enzymatic extraction effectively improved the extraction yields, whereas the amounts of sugars and saponins in some extracts were increased after the enzymatic treatment. The results also showed that only those enzyme-treated cultivated black Ganoderma canopy had increased total sugar and total saponin content. The antioxidant activities of all stalk extracts were higher than the canopy extracts. Their emulsifying properties were comparable with lecithin due to their high saponin content. Therefore, these extracts are new natural emulsifiers.
  6. Wen D, Cheng Z, Li J, Zheng X, Yao W, Dong X, et al.
    J Neurosci Methods, 2021 Nov 01;363:109353.
    PMID: 34492241 DOI: 10.1016/j.jneumeth.2021.109353
    BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM).

    METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.

    RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.

    CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.

  7. Wen D, Li R, Jiang M, Li J, Liu Y, Dong X, et al.
    Neural Netw, 2021 Dec 25;148:23-36.
    PMID: 35051867 DOI: 10.1016/j.neunet.2021.12.010
    This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.
  8. Zhang X, Dong X, Saripan MIB, Du D, Wu Y, Wang Z, et al.
    Thorac Cancer, 2023 Jul;14(19):1802-1811.
    PMID: 37183577 DOI: 10.1111/1759-7714.14924
    BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.

    METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.

    RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.

    CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.

  9. Zhang R, Wang S, Huang X, Yang Y, Fan H, Yang F, et al.
    Anal Chim Acta, 2020 Jan 15;1094:142-150.
    PMID: 31761041 DOI: 10.1016/j.aca.2019.10.012
    α-synuclein is a predominantly expressing neuronal protein for understanding the neurodegenerative disorders. A diagnosing system with aggregated α-synuclein encoded by SNCA gene is necessary to make the precautionary treatment against Parkinson's disease (PD). Herein, gold-nanourchin conjugated anti-α-synuclein antibody was desired as the probe and seeded on single-walled carbon nanotube (SWCN) integrated interdigitated electrode (IDE). The surface morphology of SWCN-modified IDE and gold urchin-antibody conjugates were observed under FESEM, FETEM and AFM, the existing elements were confirmed. Voltammetry analysis revealed that the limit of fibril-formed α-synuclein detection was improved by 1000 folds (1 fM) with gold-nanourchin-antibody modified surface, compared to the surface with only antibody (1 pM). Validating the interaction of α-synuclein by Enzyme-linked Immunosorbent Assay was displayed the detection limit as 10 pM. IDE has a good reproducibility and a higher selectivity on α-synuclein as evidenced by the interactive analysis with the control proteins, PARK1 and DJ-1.
  10. Yang C, Li Q, Wang X, Cui A, Chen J, Liu H, et al.
    Research (Wash D C), 2023;6:0226.
    PMID: 37746659 DOI: 10.34133/research.0226
    Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas (PAs) and threatened species. Since the 21st century, both the highlands and lowlands of Asia have been experiencing the dramatic human expansion. However, the threat degree of human expansion to biodiversity is poorly understood. Here, the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental (Asia), national (48 Asian countries), and hotspot (6,502 Asian terrestrial PAs established before 2000) scales is investigated by integrating multiple large-scale data. The results show that human expansion poses widespread threat to biodiversity in Asia, especially in Southeast Asia, with Malaysia, Cambodia, and Vietnam having the largest threat degrees (∼1.5 to 1.7 times of the Asian average level). Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries (most Southeast Asian countries). The regions with threats to biodiversity are present in ∼75% terrestrial PAs (including 4,866 PAs in 26 countries), and human expansion in PAs triggers higher threat degrees to biodiversity than that in non-PAs. Our findings provide novel insight for the Sustainable Development Goal 15 (SDG-15 Life on Land) and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of SDG-15. To reduce the threat degree, Asian developing countries should accelerate economic transformation, and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries (i.e., trade leading to the loss of wildlife habitats) to alleviate human expansion, especially in PAs and highlands.
  11. Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.
    Autophagy, 2021 Jan;17(1):1-382.
    PMID: 33634751 DOI: 10.1080/15548627.2020.1797280
    In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
  12. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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