Bioinformatics analysis revealed the potential crosstalk genes and molecular mechanisms between intracranial aneurysms and periodontitis.

OBJECTIVES: The risk of intracranial aneurysms (IAs) development and rupture is significantly higher in patients with periodontitis (PD), suggesting an association between the two. However, the specific mechanisms of association between these two diseases have not been fully investigated. MATERIALS AND METHODS: In this study, we downloaded IAs and PD data from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. The protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) was performed to identified key modules and key crosstalk genes. In addition, the immune cell landscape was assessed and the correlation of key crosstalk genes with each immune cell was calculated. Finally, transcription factors (TFs) regulating key crosstalk genes were explored. RESULTS: 127 overlapping DEGs were identified and functional enrichment analysis highlighted the important role of immune reflection in the pathogenesis of IAs and PD. We identified ITGAX and COL4A2 as key crosstalk genes. In addition, the expression of multiple immune cells was significantly elevated in PDs and IAs compared to controls, and both key crosstalk genes were significantly negatively associated with Macrophages M2. Finally, GATA2 was identified as a potential key transcription factor (TF), which regulates two key crosstalk gene. CONCLUSIONS: The present study identifies key crosstalk genes and TF in PD and IAs, providing new insights for further study of the co-pathogenesis of PD and IAs from an immune and inflammatory perspective. Also, this is the first study to report the above findings.

The potential crosstalk genes and molecular mechanisms between glioblastoma and periodontitis.

Despite clinical and epidemiological evidence suggestive of a link between glioblastoma (GBM) and periodontitis (PD), the shared mechanisms of gene regulation remain elusive. In this study, we identify differentially expressed genes (DEGs) that overlap between the GEO datasets GSE4290 [GBM] and GSE10334 [PD]. Functional enrichment analysis was conducted, and key modules were identified using protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA). The expression levels of CXCR4, LY96, and C3 were found to be significantly elevated in both the test dataset and external validation dataset, making them key crosstalk genes. Additionally, immune cell landscape analysis revealed elevated expression levels of multiple immune cells in GBM and PD compared to controls, with the key crosstalk genes negatively associated with Macrophages M2. FLI1 was identified as a potential key transcription factor (TF) regulating the three key crosstalk genes, with increased expression in the full dataset. These findings contribute to our understanding of the immune and inflammatory aspects of the comorbidity mechanism between GBM and PD.

MCNMF-Unet: a mixture Conv-MLP network with multi-scale features fusion Unet for medical image segmentation.

Recently, the medical image segmentation scheme combining Vision Transformer (ViT) and multilayer perceptron (MLP) has been widely used. However, one of its disadvantages is that the feature fusion ability of different levels is weak and lacks flexible localization information. To reduce the semantic gap between the encoding and decoding stages, we propose a mixture conv-MLP network with multi-scale features fusion Unet (MCNMF-Unet) for medical image segmentation. MCNMF-Unet is a U-shaped network based on convolution and MLP, which not only inherits the advantages of convolutional in extracting underlying features and visual structures, but also utilizes MLP to fuse local and global information of each layer of the network. MCNMF-Unet performs multi-layer fusion and multi-scale feature map skip connections in each network stage so that all the feature information can be fully utilized and the gradient disappearance problem can be alleviated. Additionally, MCNMF-Unet incorporates a multi-axis and multi-windows MLP module. This module is fully end-to-end and eliminates the need to consider the negative impact of image cropping. It not only fuses information from multiple dimensions and receptive fields but also reduces the number of parameters and computational complexity. We evaluated the proposed model on BUSI, ISIC2018 and CVC-ClinicDB datasets. The experimental results show that the performance of our proposed model is superior to most existing networks, with an IoU of 84.04% and a F1-score of 91.18%.

The rumen microbiota contributed to the development of mastitis induced by subclinical ketosis.

BACKGROUND: Mastitis is a serious disease which affects animal husbandry, particularly in cow breeding. The etiology of mastitis is complex and its pathological mechanism is not yet fully understood. Our previous research in clinical investigation has revealed that subclinical ketosis can increase the number of somatic cell counts (SCC) in milk, although the underlying mechanism remains unclear. Recent studies have further confirmed the significant role of mastitis. RESULTS: In this study, we aimed to examine the SCC, rumen microbiota, and metabolites in the milkmen of cows with subclinical ketosis. Additionally, we conducted a rumen microbiota transplant into mice to investigate the potential association between rumen microbiota disturbance and mastitis induced by subclinical ketosis in dairy cows. The study has found that cows with subclinical ketosis have a higher SCC in their milk compared to healthy cows. Additionally, there were significant differences in the rumen microbiota and the level of volatile fatty acid (VFA) between cows with subclinical ketosis and healthy cows. Moreover, transplanting the rumen microbiota from subclinical ketosis and mastitis cows into mice can induce mammary inflammation and liver function damage than transplanting the rumen flora from healthy dairy cows. CONCLUSIONS: In addition to the infection of mammary gland by pathogenic microorganisms, there is also an endogenous therapeutic pathway mediated by rumen microbiota. Targeted rumen microbiota modulation may be an effective way to prevent and control mastitis in dairy cows.

[Diagnostic and Prognostic Value of 18F-FDG PET/CT in Bone Marrow Infiltration of Newly Diagnosed Diffuse Large B-Cell Lymphoma].

OBJECTIVE: To explore the diagnostic value of 18F-FDG PET/CT in bone marrow infiltration (BMI) of newly diagnosed diffuse large B-cell lymphoma (DLBCL), compared with the results of bone marrow biopsy (BMB) and investigate whether the BMI diagnosed by 18F-FDG PET/CT and other factors have independent prognostic values. METHODS: Ninety-four newly diagnosed DLBCL patients who underwent PET/CT in Clinical Medical College of Shanghai General Hospital of Nanjing Medical University were included. BMB was performed within 2 weeks before or after PET/CT, and standardized treatment was performed after PET/CT. The manifestations of bone marrow (BM) FDG uptake were recorded. The diagnostic criteria of BMI were BMB positive or focal BM FDG uptake confirmed by imaging follow-up. The relationship between clinical features and BM FDG uptake and the values of PET/CT and BMB in the diagnosis of BMI was analyzed. The progression-free survival (PFS) was analyzed by Kaplan-Meier survival curves, log-rank test was used to compare PFS rate, and Cox regression model was used to analyze the independent risk factors affecting PFS. RESULTS: Among 94 DLBCL patients, 34 patients showed focal BM uptake (fPET), 7 patients showed super BM uptake (sBMU), 11 patients showed diffuse homogenous uptake higher than liver (dPET), and the other 42 patients had normal BM uptake (nPET) (lower than liver). BMB positive was found in all sBMU patients, in 20.6%(7/34) of fPET patients, and in 27.3% (3/11) of dPET patients. All nPET patients had negative BMB results. dPET patients were associated with lower hemoglobin level and leukocyte count compared with nPET group (P < 0.001, P =0.026). Compared with fPET patients, sBMU patients were more likely to have B symptoms and elevated lactate dehydrogenase (LDH). A total of 44 patients were diagnosed BMI, including 17 cases with BMB+. The sensitivity and specificity of BMB in the diagnosis of BMI was 38.6% (17/44) and 100% (50/50), respectively. Using fPET and sBMU as criteria of PET BMI, the diagnostic sensitivity and specificity of PET/CT was 93.2% (41/44) and 100% (50/50), respectively. Kaplan-Meier analysis showed that there was no significant difference in 2-year PFS rate between nPET and dPET patients (P >0.05), while sBMU patients had lower 2-year PFS rate compared with fPET patients (P < 0.001). Multivariate analysis showed that higher Ann Arbor stage (HR=9.010, P =0.04) and sBMU (HR=3.964, P =0.002) were independent risk factors affecting PFS. CONCLUSIONS: Increased BM FDG uptake of DLBCL can be manifested as dPET, fPET and sBMU. fPET and sBMU can replace BMB to diagnose BMI. Although dPET cannot completely exclude the possibility of BMI, it does not affect the prognosis, so it can be diagnosed as PET BMI negative. sBMU is an independent prognostic risk factor.

FM-Unet: Biomedical image segmentation based on feedback mechanism Unet.

With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.

Surface-Framework structure: A neural network structure for weakening gridding effect in PCB mark-point semantic segmentation.

Image transfer plays a significant role in the manufacture of PCB; it affects the production speed and quality of the manufacturing process. This study proposes a surface-framework structure, which divides the network into two parts: surface and framework. The surface part does not include subsampling to extract the detailed features of the image, thereby improving the segmentation effect when the computing power requirement is not large. Meanwhile, a semantic segmentation method based on Unet and surface-framework structure, called pure efficient Unet (PE Unet), is proposed. A comparative experiment is conducted on our mark-point dataset (MPRS). The proposed model achieved good results in various metrics. The proposed network's IoU attained 84.74%, which is 3.15% higher than Unet. The GFLOPs is 34.0 which shows that the network model balances performance and speed. Furthermore, comparative experiments on MPRS, CHASE_DB1, TCGA-LGG datasets for Surface-Framework structure are introduced, the IoU promotion clipped means on these datasets are 2.38%, 4.35% and 0.78% respectively. The Surface-Framework structure can weaken the gridding effect and improve the performance of semantic segmentation network.

Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

In†Silico Analysis Identifies the Anti-Liver Injury Targets of Diammonium Glycyrrhizinate: Validated in Perfluorooctanoic Acid-Lesioned Mouse Model.

Liver injury refers to a pathological condition that causes dysfunction to hepatic parenchymal cells. And diammonium glycyrrhizinate (DG) is clinically prescribed for hepatoprotection. To date, detailed information regarding DG against liver injury in molecular mechanisms remains unrevealed totally. In the present study, we applied network pharmacology and molecular docking to decipher substantial genes, biological functions of DG for treating liver injury. Furthermore, preclinical experiments using perfluorooctanoic acid (PFOA)-induced liver injury in mice were used to validate the bioinformatic findings. Our results showed that the target network of DG and liver injury predominantly shared 90 genes. Eleven core genes of DG treating liver injury including ALB, TP53, TNF, CASP3, PTGS2, JUN, TLR4, IL10, STAT3, NOS3, FOS. The gene ontology and KEGG enrichment further highlighted their importance in regulation of cell proliferation, regulation of transcription, inflammatory response, regulation of NF-kappaB import into nucleus, regulation of apoptotic process, T cell receptor signaling pathway, and Toll-like receptor signaling pathway. Moreover, DG treatment was found to rescue the PFOA-induced liver injury through the modulation of identified genes including TNF, CASP3, PTGS2, and ALB. Current integrated data from bioinformatics method and experimental validation uncovered that DG exerts potent actions to treat liver injury through regulating core targets associated with inflammation and immunomodulation.

Deletion of platelet CLEC-2 decreases GPIbα-mediated integrin αIIbß3 activation and decreases thrombosis in TTP.

Microvascular thrombosis in patients with thrombotic thrombocytopenic purpura (TTP) is initiated by GPIbα-mediated platelet binding to von Willebrand factor (VWF). Binding of VWF to GPIbα causes activation of the platelet surface integrin αIIbß3. However, the mechanism of GPIbα-initiated activation of αIIbß3 and its clinical importance for microvascular thrombosis remain elusive. Deletion of platelet C-type lectin-like receptor 2 (CLEC-2) did not prevent VWF binding to platelets but specifically inhibited platelet aggregation induced by VWF binding in mice. Deletion of platelet CLEC-2 also inhibited αIIbß3 activation induced by the binding of VWF to GPIbα. Using a mouse model of TTP, which was created by infusion of anti-mouse ADAMTS13 monoclonal antibodies followed by infusion of VWF, we found that deletion of platelet CLEC-2 decreased pulmonary arterial thrombosis and the severity of thrombocytopenia. Importantly, prophylactic oral administration of aspirin, an inhibitor of platelet activation, and therapeutic treatment of the TTP mice with eptifibatide, an integrin αIIbß3 antagonist, reduced pulmonary arterial thrombosis in the TTP mouse model. Our observations demonstrate that GPIbα-mediated activation of integrin αIIbß3 plays an important role in the formation of thrombosis in TTP. These observations suggest that prevention of platelet activation with aspirin may reduce the risk for thrombosis in patients with TTP.

Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field.

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint.

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

Role of SNPs in the Biogenesis of Mature miRNAs.

Single nucleotide polymorphisms (SNPs) play a significant role in microRNA (miRNA) generation, processing, and function and contribute to multiple phenotypes and diseases. Therefore, whole-genome analysis of how SNPs affect miRNA maturation mechanisms is important for precision medicine. The present study established an SNP-associated pre-miRNA (SNP-pre-miRNA) database, named miRSNPBase, and constructed SNP-pre-miRNA sequences. We also identified phenotypes and disease biomarker-associated isoform miRNA (isomiR) based on miRFind, which was developed in our previous study. We identified functional SNPs and isomiRs. We analyzed the biological characteristics of functional SNPs and isomiRs and studied their distribution in different ethnic groups using whole-genome analysis. Notably, we used individuals from Great Britain (GBR) as examples and identified isomiRs and isomiR-associated SNPs (iso-SNPs). We performed sequence alignments of isomiRs and miRNA sequencing data to verify the identified isomiRs and further revealed GBR ethnographic epigenetic dominant biomarkers. The SNP-pre-miRNA database consisted of 886 pre-miRNAs and 2640 SNPs. We analyzed the effects of SNP type, SNP location, and SNP-mediated free energy change during mature miRNA biogenesis and found that these factors were closely associated to mature miRNA biogenesis. Remarkably, 158 isomiRs were verified in the miRNA sequencing data for the 18 GBR samples. Our results indicated that SNPs affected the mature miRNA processing mechanism and contributed to the production of isomiRs. This mechanism may have important significance for epigenetic changes and diseases.

Function-Oriented Graphene Quantum Dots Probe for Single Cell in situ Sorting of Active Microorganisms in Environmental Samples.

Functional microorganisms play a vital role in removing environmental pollutants because of their diverse metabolic capability. Herein, a function-oriented fluorescence resonance energy transfer (FRET)-based graphene quantum dots (GQDs-M) probe was developed for the specific identification and accurate sorting of azo-degrading functional bacteria in the original location of environmental samples for large-scale culturing. First, nitrogen-doped GQDs (GQDs-N) were synthesized using a bottom-up strategy. Then, a GQDs-M probe was synthesized based on bonding FRET-based GQDs-N to an azo dye, methyl red, and the quenched fluorescence was recovered upon cleavage of the azo bond. Bioimaging confirmed the specific recognition capability of GQDs-M upon incubation with the target bacteria or environmental samples. It is suggested that the estimation of environmental functional microbial populations based on bioimaging will be a new method for rapid preliminary assessment of environmental pollution levels. In combination with a visual single-cell sorter, the target bacteria in the environmental samples could be intuitively screened at the single-cell level in 17 bacterial strains, including the positive control Shewanella decolorationis S12, and were isolated from environmental samples. All of these showed an azo degradation function, indicating the high accuracy of the single-cell sorting strategy using the GQDs-M. Furthermore, among the bacteria isolated, two strains of Bacillus pacificus and Bacillus wiedmannii showed double and triple degradation efficiency for methyl red compared to the positive control (strain S12). This strategy will have good application prospects for finding new species or high-activity species of specific functional bacteria.

Visualizing and Isolating Iron-Reducing Microorganisms at the Single-Cell Level.

Iron-reducing microorganisms (FeRM) play key roles in many natural and engineering processes. Visualizing and isolating FeRM from multispecies samples are essential to understand the in situ location and geochemical role of FeRM. Here, we visualized FeRM by a "turn-on" Fe2+-specific fluorescent chemodosimeter (FSFC) with high sensitivity, selectivity, and stability. This FSFC could selectively identify and locate active FeRM from either pure culture, coculture of different bacteria, or sediment-containing samples. Fluorescent intensity of the FSFC could be used as an indicator of Fe2+ concentration in bacterial cultures. By combining the use of the FSFC with that of a single-cell sorter, we obtained three FSFC-labeled cells from an enriched consortium, and all of them were subsequently shown to be capable of iron reduction; two unlabeled cells were shown to have no iron-reducing capability, further confirming the feasibility of the FSFC.IMPORTANCE Visualization and isolation of FeRM from samples containing multiple species are commonly needed by researchers from different disciplines, such as environmental microbiology, environmental sciences, and geochemistry. However, no available method has been reported. In this study, we provide a method to visualize FeRM and evaluate their activity even at the single-cell level. When this approach is combined with use of a single-cell sorter, FeRM can also be isolated from samples containing multiple species. This method can be used as a powerful tool to uncover the in situ or ex situ role of FeRM and their interactions with ambient microbes or chemicals.

Integrated expression analysis revealed RUNX2 upregulation in lung squamous cell carcinoma tissues.

This study aimed to investigate the clinicopathological significance and prospective molecular mechanism of RUNX family transcription factor 2 (RUNX2) in lung squamous cell carcinoma (LUSC). The authors used immunohistochemistry (IHC), RNA-seq, and microarray data from multi-platforms to conduct a comprehensive analysis of the clinicopathological significance and molecular mechanism of RUNX2 in the occurrence and development of LUSC. RUNX2 expression was significantly higher in 16 LUSC tissues than in paired non-cancerous tissues detected by IHC (P < 0.05). RNA-seq data from the combination of TCGA and genotype-tissue expression (GTEx) revealed significantly higher expression of RUNX2 in 502 LUSC samples than in 476 non-cancer samples. The expression of RUNX2 protein was also significantly higher in pathologic T3-T4 than in T1-T2 samples (P = 0.031). The pooled standardised mean difference (SMD) for RUNX2 was 0.87 (95% CI, 0.58-1.16), including 29 microarrays from GEO and one from ArrayExpress. The co-expression network of RUNX2 revealed complicated connections between RUNX2 and 45 co-expressed genes, which were significantly clustered in pathways including ECM-receptor interaction, focal adhesion, protein digestion and absorption, human papillomavirus infection and PI3K-Akt signalling pathway. Overexpression of RUNX2 plays an essential role in the clinical progression of LUSC.

Clinical significance of transcription factor RUNX2 in lung adenocarcinoma and its latent transcriptional regulating mechanism.

RUNX family transcription factor 2 (RUNX2) overexpression has been found in various human malignancies. However, the expression levels of RUNX2 mRNA and protein in lung adenocarcinoma (LUAD) were not investigated. This study aims to thoroughly analysis the expression level and potential mechanisms of RUNX2 mRNA in LUAD. We applied in-house immunohistochemistry, high-throughput RNA-sequencing, and gene microarrays to comprehensively investigate the expression level of RUNX2 in LUAD. A pool standard mean difference (SMD) and summary receiver operating characteristic curves (SROC) were calculated to assess the integrated expression value of RUNX2 in LUAD. The hazard ratios (HRs) were integrated to evaluate the overall prognostic effect of RUNX2 on the LUAD patients. The differentially expressed genes (DEGs) of LUAD, the potential target genes of RUNX2, and its co-expressed genes were overlapped to obtain a set of specific genes for GO and KEGG enrichment analyses. RUNX2 overexpression in LUAD was validated using a large number of cases (2 418 LUAD and 1 574 non-tumor lung samples). The pooled SMD was 0.85 (95 % CI: 0.64-1.05) and the area under the curve (AUC) of the SROC was 0.86 (95 %CI: 0.83-0.89). The integrated HR was 1.20 [1.04-1.38], indicating that increased expression of RUNX2 was an independent risk factor for the poor survival of the LUAD patients. RUNX2 and its transcriptionally regulates potential target genes may promote cell proliferation and drug resistance of LUAD by modulating the cell cycle and MAPK signaling pathways. RUNX2 can provide new research directions for targeted drug therapy and drug resistance for LUAD treatment.

Monocyte upregulation of podoplanin during early sepsis induces complement inhibitor release to protect liver function.

Multiple organ failure in sepsis is a progressive failure of several interdependent organ systems. Liver dysfunction occurs early during sepsis and is directly associated with patient death; however, the underlying mechanism of liver dysfunction is unclear. Platelet transfusion benefits patients with sepsis, and inhibition of complement activation protects liver function in septic animals. Herein, we explored the potential link between platelets, complement activation, and liver dysfunction in sepsis. We found that deletion of platelet C-type lectin-like receptor 2 (CLEC-2) exacerbated liver dysfunction in early sepsis. Platelet CLEC-2-deficient mice exhibited higher complement activation, more severe complement attack in the liver, and lower plasma levels of complement inhibitors at early time points after E. coli infection. Circulating monocytes expressed the CLEC-2 ligand podoplanin in early sepsis, and podoplanin binding induced release of complement inhibitors from platelets. Injection of complement inhibitors released from platelets reduced complement attack and attenuated liver dysfunction in septic mice. These findings indicate a new function of platelets in the regulation of complement activation during sepsis.

Brain Tissue Segmentation and Bias Field Correction of MR Image Based on Spatially Coherent FCM with Nonlocal Constraints.

Influenced by poor radio frequency field uniformity and gradient-driven eddy currents, intensity inhomogeneity (or bias field) and noise appear in brain magnetic resonance (MR) image. However, some traditional fuzzy c-means clustering algorithms with local spatial constraints often cannot obtain satisfactory segmentation performance. Therefore, an objective function based on spatial coherence for brain MR image segmentation and intensity inhomogeneity correction simultaneously is constructed in this paper. First, a novel similarity measure including local neighboring information is designed to improve the separability of MR data in Gaussian kernel mapping space without image smoothing, and the similarity measure incorporates the spatial distance and grayscale difference between cluster centroid and its neighborhood pixels. Second, the objective function with an adaptive nonlocal spatial regularization term is drawn upon to compensate the drawback of the local spatial information. Meanwhile, bias field information is also embedded into the similarity measure of clustering algorithm. From the comparison between the proposed algorithm and the state-of-the-art methods, our model is more robust to noise in the brain magnetic resonance image, and the bias field is also effectively estimated.

Site-1 protease deficiency causes human skeletal dysplasia due to defective inter-organelle protein trafficking.

Site-1 protease (S1P), encoded by MBTPS1, is a serine protease in the Golgi. S1P regulates lipogenesis, endoplasmic reticulum (ER) function, and lysosome biogenesis in mice and in cultured cells. However, how S1P differentially regulates these diverse functions in humans has been unclear. In addition, no human disease with S1P deficiency has been identified. Here, we report a pediatric patient with an amorphic and a severely hypomorphic mutation in MBTPS1. The unique combination of these mutations results in a frequency of functional MBTPS1 transcripts of approximately 1%, a finding that is associated with skeletal dysplasia and elevated blood lysosomal enzymes. We found that the residually expressed S1P is sufficient for lipid homeostasis but not for ER and lysosomal functions, especially in chondrocytes. The defective S1P function specifically impairs activation of the ER stress transducer BBF2H7, leading to ER retention of collagen in chondrocytes. S1P deficiency also causes abnormal secretion of lysosomal enzymes due to partial impairment of mannose-6-phosphate-dependent delivery to lysosomes. Collectively, these abnormalities lead to apoptosis of chondrocytes and lysosomal enzyme-mediated degradation of the bone matrix. Correction of an MBTPS1 variant or reduction of ER stress mitigated collagen-trafficking defects. These results define a new congenital human skeletal disorder and, more importantly, reveal that S1P is particularly required for skeletal development in humans. Our findings may also lead to new therapies for other genetic skeletal diseases, as ER dysfunction is common in these disorders.