Plexin domain-containing 1 may be a biomarker of poor prognosis in hepatocellular carcinoma patients, may mediate immune evasion.

BACKGROUND: For the first time, we investigated the oncological role of plexin domain-containing 1 (PLXDC1), also known as tumor endothelial marker 7 (TEM7), in hepatocellular carcinoma (HCC). AIM: To investigate the oncological profile of PLXDC1 in HCC. METHODS: Based on The Cancer Genome Atlas database, we analyzed the expression of PLXDC1 in HCC. Using immunohistochemistry, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blotting, we validated our results. The prognostic value of PLXDC1 in HCC was analyzed by assessing its correlation with clinicopathological features, such as patient survival, methylation level, tumor immune microenvironment features, and immune cell surface checkpoint expression. Finally, to assess the immune evasion potential of PLXDC1 in HCC, we used the tumor immune dysfunction and exclusion (TIDE) website and immunohistochemical staining assays. RESULTS: Based on immunohistochemistry, qRT-PCR, and Western blot assays, overexpression of PLXDC1 in HCC was associated with poor prognosis. Univariate and multivariate Cox analyses indicated that PLXDC1 might be an independent prognostic factor. In HCC patients with high methylation levels, the prognosis was worse than in patients with low methylation levels. Pathway enrichment analysis of HCC tissues indicated that genes upregulated in the high-PLXDC1 subgroup were enriched in mesenchymal and immune activation signaling, and TIDE assessment showed that the risk of immune evasion was significantly higher in the high-PLXDC1 subgroup compared to the low-PLXDC1 subgroup. The high-risk group had a significantly lower immune evasion rate as well as a poor prognosis, and PLXDC1-related risk scores were also associated with a poor prognosis. CONCLUSION: As a result of this study analyzing PLXDC1 from multiple biological perspectives, it was revealed that it is a biomarker of poor prognosis for HCC patients, and that it plays a role in determining immune evasion status.

Visual Interpretation of Radiomics Features in Filtered Computed Tomography Images during the Portal Phase of Acute Pancreatitis.

BACKGROUND: Current research on radiomics for diagnosing and prognosing acute pancreatitis predominantly revolves around model development and testing. However, there is a notable absence of ongoing interpretation and analysis regarding the physical significance of these models and features. Additionally, there is a lack of extensive exploration of visual information within the images. This limitation hinders the broad applicability of radiomics findings. This study aims to address this gap by specifically analyzing filtered Computed Tomography (CT) image features of acute pancreatitis to identify meaningful visual markers in the pancreas and peripancreatic area. METHODS: Numerous filtered CT images were obtained through pyradiomics. The window width and window level were fine-tuned to emphasize the pancreas and peripancreatic regions. Subsequently, the LightGBM algorithm was employed to conduct an embedded feature screening, followed by statistical analysis to identify features with statistical significance (p-value < 0.01). Within the purview of the study, for each filtering method, features of high importance to the preceding prediction model were incorporated into the analysis. The image visual markers were then systematically sought in reverse, and their medical interpretation was undertaken to a certain extent. RESULTS: In Laplacian of Gaussian filtered images within the pancreatic region, severe acute pancreatitis (SAP) exhibited fewer small areas with repetitive greyscale patterns. Conversely, in the peripancreatic region, SAP displayed greater irregularity in both area size and the distribution of greyscale levels. In logarithmic images, SAP demonstrated reduced low greyscale connectivity in the pancreatic region, while showcasing a higher average variation in greyscale between two adjacent pixels in the peripancreatic region. Moreover, in gradient images, SAP presented with decreased repetition of two adjacent pixel greyscales within the pancreatic region, juxtaposed with an increased inhomogeneity in the size of the same greyscale region within the δ range in the peripancreatic region. CONCLUSIONS: Various filtered images convey distinct physical significance and properties. The selection of the appropriate filtered image, contingent upon the characteristics of the Region of Interest (ROI), enables a more comprehensive capture of the heterogeneity of the disease.

Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline's F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.

Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study.

OBJECTIVES: This study aims to develop a model based on intratumoral and peritumoral radiomics from fat-suppressed T2-weighted(FS-T2WI) images to predict the histopathological grade of soft tissue sarcoma (STS). METHODS: This retrospective study included 160 patients with STS from two centers, of which 82 were low-grade and 78were high-grade. Radiomics features were extracted and selected from the region of tumor mass volume (TMV) and peritumoral tumor volume (PTV) respectively. The TMV, PTV, and combined(TM-PTV) radiomics models were established in the training cohort (n = 111)for the prediction of histopathological grade. Finally, a radiomics nomogram was constructed by combining the TM-PTV radiomics signature (Rad-score) and the selected clinical-MRI predictor. The ROC and calibration curves were used to determine the performance of the TMV, PTV, and TM-PTV models in the training and validation cohort (n = 49). The decision curve analysis (DCA) and calibration curves were used to investigate the clinical usefulness and calibration of the nomogram, respectively. RESULTS: The TMV model, PTV model, and TM-PTV model had AUCs of 0.835, 0.879, and 0.917 in the training cohort and 0.811, 0.756, 0.896 in the validation cohort. The nomogram, including the TM-PTV signatures and peritumoral hyperintensity, achieved good calibration and discrimination with a C-index of 0.948 (95% CI, 0.906 to 0.990) in the training cohort and 0.921 (95% CI, 0.840 to 0.995) in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION: The proposed model based on intratumoral and peritumoral radiomics showed good performance in distinguishing low-grade from high-grade STSs.

Morphological and transcriptional analysis of sexual differentiation and gonadal development in a burrowing fish, the four-eyed sleeper (Bostrychus sinensis).

Four-eyed sleeper (Bostrychus sinensis) is a commercially important sea water fish, and the male individuals exhibit significant advantages in somatic growth and stress resistance, so developing sex control strategy to create all-male progeny will produce higher economic value. However, little is known about the genetic background associated with sex differentiation in this species. In this study, we investigated gonadal development and uncovered critical window stages of sexual differentiation (about 2 mph), transition from proliferation to differentiation in female germ stem cells (GSCs) (2-3 mph) and male GSCs (3-4 mph). De novo transcriptome analysis revealed candidate genes and signaling pathways associated with sexual differentiation and gonadal development in four-eyed sleeper. The results showed that sox9 and zglp1 were the earliest sex-biased transcription factors during sex differentiation. Down-regulation of chemokine, cytokines-cytokine receptors and up-regulation of cellular senescence pathway might be involved in GSC differentiation. Weighted gene correlation network analysis showed that metabolic pathway and occludin were the hub signaling and gene in ovarian development, meanwhile the MAPK signaling pathways, cellular senescence pathway and ash1l (histone H3-lysine4 N-trimethyltransferase) were the hub pathways and gene in testicular development. The present work elucidated the developmental processes of sexual differentiation and gonadal development and revealed their associated revealed genes and signaling pathways in four-eyed sleeper, providing theoretical basis for developing sex-control techniques.

VEGF Mediates Tumor Growth and Metastasis by Affecting the Expression of E-Cadherin and N-Cadherin Promoting Epithelial to Mesenchymal Transition in Gastric Cancer.

Background: Gastric cancer (GC) is the fifth leading cancer in the world, and there is a high mortality rate in China. Exploring the relationship between the prognosis of GC and the expression of related genes is helpful to further understand the common characteristics of the occurrence and development of GC and provide a new method for the identification of early GC, so as to provide the best therapeutic targets. Methods: Vascular endothelial growth factor (VEGF) and markers of epithelial-mesenchymal transition (EMT) were investigated immunohistochemically using tumor samples obtained from 196 GC tissues and adjacent tumor tissues. The correlation of the expression level with histopathologic features and survival was investigated. Results: Here, we show that VEGF and EMT markers expression were significantly correlated with depth of tumor invasion and GC stage (P < .05), degree of differentiation and lymph node metastasis (P < .001). We found that the rate of VEGF positivity in GC tissues was 52.05%, which was significantly higher than that in adjacent cancer tissues (16.84%). In GC, the association between VEGF and E-cadherin was negative (r = -0.188, P < .05), whereas VEGF and N-cadherin were positively correlated (r = 0.214, P < .05). Furthermore, the Kaplan-Meier analysis and a Cox regression model were used to analyze the effect of VEGF and EMT marker expression on the survival of the patients. We found that the overall survival of GC patients was correlated with VEGF (P < .001), N-cadherin (P < .001), E-cadherin (P = .002) expression, and some histopathologic features. Conclusions: Vascular endothelial growth factor and EMT markers exist side by side and play a part together in the development of GC, which provides new ideas for evaluating the prognosis of GC and researching targeted drugs.

Exploring Prognosis, Tumor Microenvironment and Tumor Immune Infiltration in Hepatocellular Carcinoma Based on ATF/CREB Transcription Factor Family Gene-Related Model.

Introduction: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer. It is the fourth leading cause of cancer-related death worldwide. Deregulation of the ATF/CREB family is associated with the progression of metabolic homeostasis and cancer. Because the liver plays a central role in metabolic homeostasis, it is critical to assess the predictive value of the ATF/CREB family in the diagnosis and prognosis of HCC. Methods: Using data from The Cancer Genome Atlas (TCGA), this research evaluated the expression, copy number variations, and frequency of somatic mutations of 21 genes in the ATF/CREB family in HCC. A prognostic model based on the ATF/CREB gene family was developed via Lasso and Cox regression analyses, with the TCGA cohort serving as the training dataset and the International Cancer Genome Consortium (ICGC) cohort serving as the validation set. Kaplan-Meier and receiver operating characteristic analyses verified the accuracy of the prognostic model. Furthermore, the association among the prognostic model, immune checkpoints, and immune cells was examined. Results: High-risk patients exhibited an unfavorable outcome as opposed to those in the low-risk category. Multivariate Cox analysis revealed that the risk score calculated based on the prognostic model was an independent prognostic factor for HCC. Analysis of immune mechanisms revealed that the risk score had a positive link to the expression of immune checkpoints, particularly CD274, PDCD1, LAG3, and CTLA4. Differences in immune cells and immune-associated roles were found between the high- and low-risk patients, as determined by single-sample gene set enrichment analysis. The core genes ATF1, CREB1, and CREB3 in the prognostic model were shown to be upregulated in HCC tissues as opposed to adjoining normal tissues, and the 10-year overall survival (OS) rate was worse among patients with elevated expression levels of ATF1, CREB1, and CREB3. Elevated expression levels of ATF1, CREB1, and CREB3 in HCC tissues were confirmed by qRT-PCR and immunohistochemistry studies. Conclusion: According to the results of our training set and test set, the risk model based on the six ATF/CREB gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of HCC patients. This study provides novel insights into the individualized treatment of patients with HCC.

Coronary CT angiography for preoperative evaluation of non-cardiac surgery in patients with thoracic tumors: preliminary exploratory analysis in a retrospective cohort.

PURPOSE: Noninvasive coronary CT angiography (CCTA) was used to retrospectively analyze the characteristics of coronary artery disease (CAD) in patients with thoracic tumors and the impact of the results on clinical surgery decision-making, thus increasing the understanding of perioperative cardiac risk evaluation. METHOD: A total of 779 patients (age 68.6 ± 6.6 years) with thoracic tumor (lung, esophageal, and mediastinal tumor) scheduled for non-cardiac surgery were retrospectively enrolled. Patients were divided into two groups: accepted or canceled surgery. Clinical data and CCTA results were compared between the two groups, and multivariate logistic regression analysis was performed to determine predictors of the events of cancellations of scheduled surgeries. RESULTS: 634 patients (81.4%) had non-significant CAD and 145 patients (18.6%) had significant CAD. Single­, 2­, and 3­ vessel disease was found in 173 (22.2%), 93 (11.9%) and 50 (6.4%) patients, respectively. 500 (64.2%), 96 (12.3%), 96 (12.3%), 56 (7.2%) and 31 (4.0%) patients were rated as CACS 0, 1-99, 100-399, 400-999 and > 1000, respectively. Cancellations of scheduled procedures continue to increase based on the severity of the stenosis and the number of major coronary artery stenosis. The degree of stenosis and the number of vascular stenosis were independent predictors of cancelling scheduled surgery. CONCLUSIONS: For patients with thoracic tumors scheduled for non-cardiac surgery, the results suggested by CCTA significantly influenced surgery planning and facilitated to reduce perioperative cardiovascular events.

C5aR1 shapes a non-inflammatory tumor microenvironment and mediates immune evasion in gastric cancer.

C5a receptor 1 (C5aR1) is associated with various inflammatory processes, the pathogenesis of immune diseases, and tumor growth. However, its role in the tumor microenvironment of gastric cancer (GC) remains unclear. In this study, the expression of C5aR1 in GC and normal gastric mucosa tissues was compared using data retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and the results were validated by in vitro qRT-PCR and immunohistochemical analyses. The relationship between C5aR1 expression and the overall survival of patients with GC was analyzed using the Kaplan-Meier method. Subsequently, enrichment analysis was performed, and the signaling pathways were screened. C5aR1 expression was also correlated with genes related to the immune checkpoint and immune cell infiltration. The results revealed that C5aR1 expression was enhanced in GC tissues compared to normal gastric tissues, and that patients with high expression of C5aR1 had a worse 10-year overall survival compared to those showing low expression of C5aR1. Functional analysis revealed that C5aR1 is a gene related to theimmune system and may play a crucial role in inflammatory and tumor immune responses. Additionally, C5aR1 showed a positive correlation with most immune checkpoint-related genes and a negative correlation with natural killer cells, dendritic cells, and CD8+ T cells. Immune evasion risk was observed to be significantly greater in patients with higher expression of C5aR1 than in those with lower expression. The results of this study reveal that C5aR1 shapes a non-inflammatory tumor microenvironment in GC and mediates immune evasion.

SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder.

Chronic kidney disease (CKD) is a life-threatening and prevalent disease. CKD patients, especially endstage kidney disease (ESKD) patients on hemodialysis, suffer from kidney failures and are unable to remove excessive fluid, causing fluid overload and multiple morbidities including death. Current solutions for fluid overtake monitoring such as ultrasonography and biomarkers assessment are cumbersome, discontinuous, and can only be performed in the clinic. In this paper, we propose SRDA, a latent graph learning powered fluid overload detection system based on Sensor Relation Dual Autoencoder to detect excessive fluid consumption of EKSD patients based on passively collected bio-behavioral data from smartwatch sensors. Experiments using real-world mobile sensing data indicate that SRDA outperforms the state-of-the-art baselines in both F1 score and recall, and demonstrate the potential of ubiquitous sensing for ESKD fluid intake management.

Mobile Sensing in the COVID-19 Era: A Review.

Background: During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilities in monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual, neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals' infectious status, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses and subspecies. Methods: We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoring COVID-19. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobile sensing studies. Results: We found that existing literature can be naturally grouped in four clusters, including remote detection, long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative works with regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed the implications and future directions of mobile sensing in communicable diseases from the perspectives of technology and applications. Conclusion: Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of time and populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed to improve healthcare and social outcomes.

PLXDC1 Can Be a Biomarker for Poor Prognosis and Immune Evasion in Gastric Cancer.

Background: Research has revealed that Plexin domain containing 1 (PLXDC1) is correlated with the prognosis of a variety of tumors, but its role in the tumor microenvironment (TME) of gastric cancer has not been reported. Methods: In this study, we analyzed PLXDC1 expression in gastric cancer using the Oncomine and the Cancer Genome Atlas (TCGA) databases and immunohistochemical staining experiments, and performed prognostic assessment with data from the TCGA and Kaplan-Meier Plotter databases. The immunomodulatory role of PLXDC1 in the gastric cancer TME was analyzed by signaling pathway enrichment, immune cell correlation analysis, immunomodulator risk model construction and immunohistochemical staining experiments of immune cells. Results: The results indicated that PLXDC1 was overexpressed in gastric cancer and that its overexpression was associated with poor prognosis. Multivariate Cox analysis revealed that PLXDC1 could be an independent biomarker of the risk of gastric cancer. Signaling pathway enrichment revealed that high PLXDC1 expression was involved in signaling pathways related to immune activation and stromal activation, and Tumor Immune Dysfunction and Exclusion (TIDE) assessment indicated that high PLXDC1 expression was associated with a significantly higher risk of immune evasion than low PLXDC1 expression. A Cox risk model based on PLXDC1-associated immunomodulators also presented poor prognosis, and immune evasion was significantly higher in the high-risk group than in the low-risk group. In addition, immunohistochemical staining of CD8/CD3/CD4+ T cells in the high and low PLXDC1 expression groups also observed immune cell distribution characteristics of immune evasion. Conclusion: This study analyzed PLXDC1 from multiple biological perspectives and revealed that PLXDC1 can be a biomarker for poor prognosis and immune evasion in gastric cancer.

Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI.

Background: Alzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability. Methods: We proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, and NCs) process using MRI. And the accuracy, recall, specificity, area under the curve of receiver operating characteristic curve (AUC), F1 and Matthew's correlation coefficient (MCC) scores to assess accuracy of auxiliary clinical diagnoses. Results: Compared to other combinations of algorithms, our model obtained remarkable overall stratification accuracies in all different classification sets. In terms of AD vs. MCI, the mean training AUC of the 3 runs were 85.1% in 95% confidence intervals (CIs). In terms of AD vs. NC, the mean training AUC of the 3 runs was 90.6% in 95% CIs. In terms of the 3 stratifications of AD, MCI, and NC, relative precision, recall, and specificity for each category in the training test (TS) were all near 89%, while the F1 and MCC scores of both sets were 59.9% and 59.5%, respectively. Conclusions: Using a deep CNN and iterated RF architecture, we showed that brain image stratification is a promising means for evaluating AD, and examining the underlying etiology of the disease, by applying computer and medical images to achieve the early auxiliary diagnosis of AD. However, we still have a long way to go from the discovery of image markers to clinical application.

The upregulation of stromal antigen 3 expression suppresses the phenotypic hallmarks of hepatocellular carcinoma through the Smad3-CDK4/CDK6-cyclin D1 and CXCR4/RhoA pathways.

BACKGROUND: The stromal antigen 3 (STAG3) gene encodes an adhesion complex subunit that can regulate sister chromatid cohesion during cell division. Chromosome instability caused by STAG3 gene mutation may potentially promote tumor progression, but the effect of STAG3 on hepatocellular carcinoma (HCC) and the related molecular mechanism are not reported in the literature. The mechanism of the occurrence and development of HCC is not adequately understood. Therefore, the biological role of STAG3 in HCC remains to be studied, and whether STAG3 might be a sensitive therapeutic target in HCC remains to be determined. METHODS: The expression and clinical significance of STAG3 in HCC tissues and cell lines were determined by RT-qPCR and immunohistochemistry analyses. The biological functions of STAG3 in HCC were determined through in vitro and in vivo cell function tests. The molecular mechanism of STAG3 in HCC cells was then investigated by western blot assay. RESULTS: The mRNA expression of STAG3 was lower in most HCC cells than in normal cells. Subsequently, an immunohistochemical analysis of STAG3 was performed with 126 samples, and lower STAG3 expression was associated with worse overall survival in HCC patients. Moreover, cytofunctional tests revealed that the lentivirus-mediated overexpression of STAG3 in HCC cells inhibited cell proliferation, migration, and invasion; promoted apoptosis; induced G1/S phase arrest in vitro; and inhibited tumor growth in vivo. Furthermore, studies of the molecular mechanism suggested that the overexpression of STAG3 increased Smad3 expression and decreased CDK4, CDK6, cyclin D1, CXCR4 and RhoA expression. CONCLUSION: STAG3 exhibits anticancer effects against HCC, and these effects involve the Smad3-CDK4/CDK6-cyclin D1 and CXCR4/RhoA pathways. STAG3 is a tumor-suppressor gene that may serve as a potential target for molecular therapy, which provides a new idea for the treatment of HCC.

CHSY3 can be a Poor Prognostic Biomarker and Mediates Immune Evasion in Stomach Adenocarcinoma.

Background: Chondroitin sulphate synthase 3 (CHSY3) is an important enzyme that regulates glycosylation, but it has not been reported in tumours. This study explored for the first time the oncological features of CHSY3 in stomach adenocarcinoma (STAD). Methods: We analysed CHSY3 expression in STAD through the Cancer Genome Atlas (TCGA) database and verified our findings by immunohistochemical staining and Western blot experiments. The prognostic value of CHSY3 in STAD was analysed through the biological aspects of CHSY3 in STAD, such as communal clinical follow-up survival data, methylation sites, tumour immune microenvironment (TIME) and immune cell surface checkpoints. Finally, the immune-evasion potential of CHSY3 in STAD was assessed on the Tumor Immune Dysfunction and Exclusion (TIDE) website and immunohistochemical staining experiment. Results: CHSY3 overexpression in STAD was associated with a poor prognosis based on immunohistochemical staining and Western blot experiments. Multivariate Cox analysis suggested that CHSY3 could be an independent prognostic risk factor. Pathway enrichment and TIME analysis demonstrated that CHSY3 up-regulated mesenchymal activation and immune activation signals in STAD, while TIDE assessment revealed that the risk of immune evasion was significantly higher in the high CHSY3 expression group than in the low CHSY3 expression group. Risk model scores based on CHSY3-associated immune cell surface checkpoints also presented poor prognosis, and immune evasion was significantly higher in the high-risk group than in the low-risk group. Conclusions: This study analysed CHSY3 from multiple biological perspectives and revealed that CHSY3 can be a biomarker of poor prognosis and mediates the TIME immune-evasion status in STAD.

N2-Polarized Neutrophils Guide Bone Mesenchymal Stem Cell Recruitment and Initiate Bone Regeneration: A Missing Piece of the Bone Regeneration Puzzle.

The role of neutrophils in bone regeneration remains elusive. In this study, it is shown that intramuscular implantation of interleukin-8 (IL-8) (commonly recognized as a chemotactic cytokine for neutrophils) at different levels lead to outcomes resembling those of fracture hematoma at various stages. Ectopic endochondral ossification is induced by certain levels of IL-8, during which neutrophils are recruited to the implanted site and are N2-polarized, which then secrete stromal cell-derived factor-1α (SDF-1α) for bone mesenchymal stem cell (BMSC) chemotaxis via the SDF-1/CXCR4 (C-X-C motif chemokine receptor 4) axis and its downstream phosphatidylinositol 3'-kinase (PI3K)/Akt pathway and ß-catenin-mediated migration. Neutrophils are pivotal for recruiting and orchestrating innate and adaptive immunocytes, as well as BMSCs at the initial stage of bone healing and regeneration. The results in this study delineate the mechanism of neutrophil-initiated bone regeneration and interaction between neutrophils and BMSCs, and innate and adaptive immunities. This work lays the foundation for research in the fields of bone regenerative therapy and biomaterial development, and might inspire further research into novel therapeutic options.

Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks.

BACKGROUND: Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients. METHODS: To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences. This method uses an enhanced mask region-based convolutional neural network (Mask R-CNN) with graph-cut segmentation. Specifically, the k-nearest neighbor (k-NN) algorithm is employed to cluster the target liver pixels in order to get an appropriate aspect ratio. Then, anchors are adapted to the liver size using the ratio information. Thus, high-accuracy liver localization can be achieved using the anchors and rotation-invariant object recognition. Next, a fully convolutional network (FCN) is used to segment the foreground objects, and local fine-grained liver detection is realized by pixel prediction. Finally, a whole liver mask is obtained by Mask R-CNN proposed in this paper. RESULTS: We proposed a Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN algorithms in term of the dice similarity coefficient (DSC), and the Medical Image Computing and Computer-Assisted Intervention (MICCAI) metrics. CONCLUSIONS: Our experimental results demonstrate that the improved Mask R-CNN architecture has good performance, accuracy, and robustness for liver segmentation in CT image sequences.