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BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

To further enhance the mechanical properties and service temperature of silicone resin coatings， the effects of the silane coupling agent KH-560 and low melting point glass powder on the thermal protection performance of silicone resin coatings were investigated.The elastic modulus， bonding strength， thermal expansion coefficient， ablation， thermal insulation and thermal shock properties of the coating were tested using a high-temperature solid material dynamic elastic property tester， a universal electronic tensile machine， an expansion meter and a gas thermal shock experimental system； The composition， surface micromorphology and elemental content of the coating were analyzed using infrared spectroscopy， emission scanning electron microscopy， and its attached energy spectrum analysis.Results showed that with increasing KH-560 content (0～3%)， the elastic modulus and adhesion strength of the silicone resin coatings were initially increased and then decreased.When 2.0%KH-560 was added，the maximum values for the elastic modulus and adhesion strength of the coatings were achieved at room temperature， reaching 24.10 GPa and 4.40 MPa， respectively.This was mainly attributed to the enhancement of bonding between the silicone resin and the metal substrate， as well as between the filler and the silicone resin facilitated by KH-560.The addition of 110%glass powder was found to effectively improve the thermal stability and thermal shock resistance of the silicone resin coatings at 800 ℃， due to the melting of glass powder，which filled the voids generated by the decomposition of silicone resin， thereby preventing further oxidation decomposition of the silicone resin.Additionally， silicone resin coatings with a glass powder content of 110%were observed to exhibit good thermal insulation and ablative properties.

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Aims: Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD. Methods and results: Twelve patients with CAD without HF (CAD‐non HF), 12 patients with CAD complicated with HF (CAD‐HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD‐HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme‐linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL‐23, and FGF‐21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme‐linked immunosorbent assay. Conclusions: The combination of VEGFR2, FLRG, IL‐23, and FGF‐21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD. [ABSTRACT FROM AUTHOR]

As a new imaging inspection method with characteristics of a wide view field and non-contact, spatial frequency domain imaging (SFDI) is very suitable to evaluate the optical properties of agricultural products to ensure the sustainable development of agriculture. However, due to the unique forward scattering characteristics of fruit skin, only a few photons can return to the skin surface after interacting with the flesh, thus affecting the detection accuracy of the flesh layer. This study aims to propose a more accurate and wider applicable method to extract the optical properties of two-layer tissue from SFDI measurements. Firstly, a two-layer model was proposed by optimizing the reflectivity of the flesh layer through the optical properties and thickness of the skin layer. Secondly, the influence of the optical properties and thickness of different skin layers on the reflectivity optimization of the flesh layer was investigated by a Monte Carlo simulation, and then, the accuracy and effectiveness of the proposed model was evaluated for practical inspection by phantom experiments. Finally, this model was used to obtain the optical properties, layer by layer, of four thin-skinned fruits (pear, apple, peach and muskmelon) to verify its universality. The results showed that, for the skin layer, the average errors of the absorption coefficient (μa1) and the reduced scattering coefficient (μ′s1) were 10.87% and 7.91%, respectively, and for the flesh layer, the average errors of the absorption coefficient (μa2) and the reduced scattering coefficient (μ′s2) were 16.76% and 8.64%, respectively. This study provides the basis for the SFDI detection of optical properties of two-layer tissue such as thin-skinned fruits, which can be further used for nondestructive fruit quality evaluations. [ABSTRACT FROM AUTHOR]

COVID‐19 has infected 272 million patients and caused 5.33 million deaths around the world, and it remains the main global threat. Previous studies revealed that Chinese traditional medicine is an effective treatment for COVID‐19 infection. This study aims to reveal the pharmacological effects of kaempferol, which is the active component of Radix Bupleuri and Tripterygii Radix, and potential mechanisms for the treatment of COVID‐19. Here, we employed the bioinformatics methods to filter the anti‐COVID‐19 candidate genes of kaempferol, which mainly enriched in inflammation (TNF, JUN, etc.) and virus infection (AKT1, JNK, etc.). The Transcription levels of AKT1, JNK and JUN were significantly reduced by kaempferol treatment in the LPS‐activated macrophages. In addition, kaempferol reduced the secretion of inflammatory factors by LPS‐stimulated macrophages, inhibited MAPK/NF‐κB signaling and regulated macrophage polarization to M2 type in vitro, and suppressed endotoxin‐induced cytokine storm and improved survival in mice. Molecular docking analysis demonstrated that kaempferol was probable to bind the COVID‐19 protein 5R84 and formatted hydrogen bond with the residues, the free binding energy of which was lower than the original ligand. In summary, our current work indicates that kaempferol has anti‐COVID‐19 potential through the reduction of COVID‐19‐induced body dysfunction and molecule–protein interaction, and bioinformatics results clarify that some of these key target genes might serve as potential molecular markers for detecting COVID‐19. [ABSTRACT FROM AUTHOR]

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of 'crown' pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of 'crown' pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment. [ABSTRACT FROM AUTHOR]

Background: Losing one's only child is a major traumatic life event that may lead to post-traumatic stress disorder (PTSD); however, the underlying mechanisms of its psychological consequences remain poorly understood. Here, we investigated subregional hippocampal functional connectivity (FC) networks based on resting-state functional magnetic resonance imaging and the deoxyribonucleic acid methylation of the human glucocorticoid receptor gene (NR3C1) in adults who had lost their only child. Methods: A total of 144 Han Chinese adults who had lost their only child (51 adults with PTSD and 93 non-PTSD adults [trauma-exposed controls]) and 50 controls without trauma exposure were included in this fMRI study (age: 40–67 years). FCs between hippocampal subdivisions (four regions in each hemisphere: cornu ammonis1 [CA1], CA2, CA3, and dentate gyrus [DG]) and methylation levels of the NR3C1 gene were compared among the three groups. Results: Trauma-exposed adults, regardless of PTSD diagnosis, had weaker positive FC between the left hippocampal CA1, left DG, and the posterior cingulate cortex, and weaker negative FC between the right CA1, right DG, and several frontal gyri, relative to healthy controls. Compared to non-PTSD adults, PTSD adults showed decreased negative FC between the right CA1 region and the right middle/inferior frontal gyri (MFG/IFG), and decreased negative FC between the right DG and the right superior frontal gyrus and left MFG. Both trauma-exposed groups showed lower methylation levels of the NR3C1 gene. Conclusions: Adults who had lost their only child may experience disrupted hippocampal network connectivity and NR3C1 methylation status, regardless of whether they have developed PTSD. [ABSTRACT FROM AUTHOR]

Background : Losing one's only child may lead to post-traumatic stress disorder (PTSD), of which re-experiencing is the core symptom. However, neuroimaging studies of sex differences in re-experiencing in the context of the trauma of losing one's only child and PTSD are scarce; comparisons of the functional networks from the hippocampal subfields to the thalamus might clarify the neural basis. Methods : Thirty couples without any psychiatric disorder who lost their only child (non-PTSD group), 55 patients with PTSD, and 50 normal controls underwent resting-state functional magnetic resonance imaging. The functional connectivity (FC) from the hippocampal subregions to the thalamus and the correlations of FC with re-experiencing symptoms were analyzed within and between the sexes. Results : Compared with husbands without PTSD, wives without PTSD had higher re-experiencing symptoms and weaker FC between the right hippocampal cornu ammonis 3 (RCA3) and the right thalamus (RT; RCA3-RT). Moreover, only the correlation between the RCA3-RT FC and re-experiencing in wives without PTSD was significant. Among the three groups, only the RCA3-RT FC in female subjects was markedly different. Additionally, the RCA3-RT FC in wives without PTSD was remarkably lower relative to female patients with PTSD. Conclusion : Wives without PTSD who lost their only child had worse re-experiencing symptoms relative to their husbands, which was associated with the FC alteration between the hippocampal subregions and the thalamus. Importantly, the low level of the RCA3-RT FC may play a potentially protective role against the development of PTSD in wives who have lost their only child. [ABSTRACT FROM AUTHOR]

Background. The incidence of sepsis has been increasing in recent years. The molecular mechanism of different pathogenic sepsis remains elusive, and biomarkers of sepsis against different pathogens are still lacking. Methods. The microarray data of bacterial sepsis, fungal sepsis, and mock-treated samples were applied to perform differentially expressed gene (DEG) analysis to identify a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set. Functional enrichment analysis was used to explore the body's response to bacterial sepsis and fungal sepsis. Gene set variation analysis (GSVA) was used to score individual samples against the two pathogen-specific gene sets, and each sample gets a GSVA index. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of sepsis. An independent data set was used to validate the bacterial sepsis-specific GSVA index. Results. The genes differentially expressed only in bacterial sepsis and the genes differentially expressed only in fungal sepsis were significantly involved in different biological processes (BPs) and pathways. This indicated that the body's responses to fungal sepsis and bacterial sepsis are varied. Twenty-two genes were identified as bacterial sepsis-specific genes and upregulated in bacterial sepsis, and 23 genes were identified as fungal sepsis-specific genes and upregulated in fungal sepsis. ROC curve analysis showed that both of the two pathogen sepsis-specific GSVA indexes may be a reliable biomarker for corresponding pathogen-induced sepsis (AUC = 1.000), while the mRNA of CALCA (also known as PCT) have a poor diagnostic value with AUC = 0.512 in bacterial sepsis and AUC = 0.705 in fungi sepsis. In addition, the AUC of the bacterial sepsis-specific GSVA index in the independent data set was 0.762. Conclusion. We proposed a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set; the bacterial sepsis GSVA index may be a reliable biomarker for bacterial sepsis. [ABSTRACT FROM AUTHOR]

Background: In recent years, sepsis-induced acute respiratory distress syndrome (ARDS) has remained a major clinical challenge for patients in intensive care units. While some progress has been reported over the years, the pathogenesis of ARDS still needs to be further expounded. Methods: In the present study, gene set enrichment analysis, differentially expressed genes analysis, short time-series expression miner, protein–protein interaction (PPI) networks, module analysis, hypergeometric test, and functional enrichment analysis were performed in whole blood gene expression profiles of sepsis and induced-sepsis ARDS to explore the molecular mechanism of sepsis-induced ARDS. Results: Further dysregulated genes in the process evolving from healthy control through sepsis to sepsis-induced ARDS were identified and organized into 10 functional modules based on their PPI networks. These functional modules were significantly involved in cell cycle, ubiquitin mediated proteolysis, spliceosome, and other pathways. MYC, STAT3, LEF1, and BRCA1 were potential transcription factors (TFs) regulating these modules. A TF-module-pathway global regulation network was constructed. In particular, our findings suggest that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. Receiver operating characteristic curve analysis showed the core genes in the global regulation network may be biomarkers for sepsis or sepsis-induced ARDS. Conclusions: We found that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. The reviews of this paper are available via the supplementary material section. [ABSTRACT FROM AUTHOR]