Summary
We explored the relationships between lymphocyte subsets, cytokines, pulmonary inflammation index (PII) and disease evolution in patients with (corona virus disease 2019) COVID‐19. A total of ...123 patients with COVID‐19 were divided into mild and severe groups. Lymphocyte subsets and cytokines were detected on the first day of hospital admission and lung computed tomography results were quantified by PII. Difference analysis and correlation analysis were performed on the two groups. A total of 102 mild and 21 severe patients were included in the analysis. There were significant differences in cluster of differentiation 4 (CD4+ T), cluster of differentiation 8 (CD8+ T), interleukin 6 (IL‐6), interleukin 10 (IL‐10) and PII between the two groups. There were significant positive correlations between CD4+ T and CD8+ T, IL‐6 and IL‐10 in the mild group (r2 = 0·694, r 2 = 0·633, respectively; P < 0·01). After ‘five‐in‐one’ treatment, all patients were discharged with the exception of the four who died. Higher survival rates occurred in the mild group and in those with IL‐6 within normal values. CD4+ T, CD8+ T, IL‐6, IL‐10 and PII can be used as indicators of disease evolution, and the PII can be used as an independent indicator for disease progression of COVID‐19.
Bone marrow mesenchymal stem cells (BMSCs) can differentiate into Schwann cells (SCs) during peripheral nerve injury; in our previous research, we showed that SC-derived exosomes (SC-exos) played a ...direct induction role while fibroblast-derived exosomes (Fb-exos) had no obvious induction role. The induction role of neural stem cell (NSC)-derived exosomes (NSC-exos) has also been widely confirmed. However, no studies have compared the induction effects of these three types of cells at the same time. Therefore, by investigating the effect of these three cell-derived exosomes upon the induction of BMSCs to differentiate into SCs, this study explored the role of different exosomes in promoting the differentiation of stem cells into SCs cells, and conducted a comparison between the two groups by RNA sequencing to further narrow the range of target genes and related gene pathways in order to study their related mechanisms.
We extracted exosomes from SCs, fibroblasts (Fb) and neural stem cells (NSC) and then investigated the ability of these exosomes to induce differentiation into BMSCs under different culture conditions. The expression levels of key proteins and gene markers were detected in induced cells by fluorescence immunoassays, western blotting and polymerase chain reaction (PCR); then, we statistically compared the relative induction effects under different conditions. Finally, we analyzed the three types of exosomes by RNA-seq to predict target genes and related gene pathways.
BMSCs were cultured by three media: conventional (no induction), pre-induction or pre-induction + original induction medium (ODM) with exosomes of the same cell origin under different culture conditions. When adding the three different types of exosomes separately, the overall induction of BMSCs to differentiate into SCs was significantly increased (P < 0.05). The induction ability was ranked as follows: pre-induction + ODM + exosome group > pre-induction + exosome group > non-induction + exosome group. Using exosomes from different cell sources under the same culture conditions, we observed the following trends under the three culture conditions: RSC96-exos group ≥ NSC-exos group > Fb-exos group. The overall ability to induce BMSCs into SCs was significantly greater in the RSC96-exos group and the NSC-exos group. Although there was no significant difference in induction efficiency when comparing these two groups, the overall induction ability of the RSC96-exos group was slightly higher than that of the NSC-exos group. By combining the differentiation induction results with the RNA-seq data, the three types of exosomes were divided into three comparative groups: RSC vs. NSC, RSC vs. Fb and NSC vs. Fb. We identified 203 differentially expressed mRNA target genes in these three groups. Two differentially expressed genes were upregulated simultaneously, namely riboflavin kinase (RFK, ENSRNOG00000022273) and ribosomal RNA processing 36 (Rrp36, ENSRNOG00000017836). We did not identify any co-upregulated target genes for the miRNAs, but did identify one target gene of the lncRNAs, namely ENSRNOG00000065005. Analysis identified 90 GO terms related to nerves and axons in the mRNAs; in addition, KEGG enrichment and GASA analysis identified 13 common differential expression pathways in the three groups.
Our analysis found that pre-induction + ODM + RSC96/NSC-exos culture conditions were most conducive with regards to induction and differentiation. RSC96-exos and NSC-exos exhibited significantly greater differentiation efficiency of BMSCs into SCs. Although there was no statistical difference, the data indicated a trend for RSC96-exos to be advantageous We identified 203 differentially expressed mRNAs between the three groups and two differentially expressed target mRNAs were upregulated, namely riboflavin kinase (RFK, ENSRNOG00000022273) and ribosomal RNA processing 36 (Rrp36, ENSRNOG00000017836). 90 GO terms were related to nerves and axons. Finally, we identified 13 common differentially expressed pathways across our three types of exosomes. It is hoped that the efficiency of BMSCs induction differentiation into SCs can be improved, bringing hope to patients and more options for clinical treatment.
Clinical studies have shown that osteoprotegerin (OPG) is reduced in patients with nonalcoholic steatohepatitis (NASH), but the underlying mechanisms are unclear. The current study focuses on the ...role of OPG in the NASH pathogenesis. OPG knockout mice and wild-type control mice fed a methionine choline-deficient diet (MCD) for 4 weeks resulted in an animal model of NASH. Measurement of triglycerides (TG) in serum and liver to assess steatosis. Hematoxylin eosin (HE), Sirius Red and Masson staining were used to assess the liver damage. Transcriptome sequencing analysis, qPCR and western blot were to analyze changes in lipid metabolism and inflammation-related indicators in the liver. In vivo knockout of OPG resulted in a reduction of TG levels in the liver and a significant increase in serum ALT and AST. The expression of inflammatory factors and fibrosis genes was significantly upregulated in the livers of OPG knockout mice. Transcriptome sequencing analysis showed that OPG knockout significantly enhanced MCD diet-induced activation of the mitogen-activated protein kinase (MAPK) signaling pathway. Mechanistically, OPG may inhibit MAPK signaling pathway activity by upregulating the expression of dual specificity phosphatase 14 (DUSP14), thereby reducing inflammatory injury. OPG could regulate the activity of the MAPK signaling pathway via DUSP14, thus regulating the expression of some inflammatory factors in NASH, it may be a promising target for the treatment of NASH.
Objectives
This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information ...and the radiomics of deep learning algorithms.
Methods
A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (
n
= 167) and testing (
n
= 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA).
Results
The AUCs of the clinical image model in training set and testing set were 0.883 95% CI: 0.822–0.945 and 0.802 95% CI: 0.666–0.937, respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively.
Conclusions
Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.
Graphical abstract
Key points
MSI is an important biomarker for immunotherapy in gastric cancer.
Quantitative radiomics features were closely related to MSI in gastric cancer.
Combining clinical and radiomics features with deep learning could evaluate MSI noninvasively.
Aims/Introduction
As a member of the tumor necrosis factor‐α‐related protein family, complement‐1q tumor necrosis factor‐α‐related protein isoform 5 (CTRP5) has been found to be associated with ...obesity and insulin resistance (IR). Previous studies in humans and animals have reported contradictory results related to the association between CTRP5 and IR. The purpose of the present study was to explore the relationship between CTRP5 and IR through a cross‐sectional study and drug intervention study of type 2 diabetes patients.
Materials and Methods
A cross‐sectional study was carried out with 118 newly diagnosed patients with type 2 diabetes and 116 healthy adults. In an interventional study, 78 individuals with newly diagnosed type 2 diabetes received sodium–glucose cotransporter 2 inhibitor (dapagliflozin) treatment for 3 months. Circulating CTRP5 concentrations were measured by enzyme‐linked immunosorbent assay.
Results
Serum CTRP5 concentrations were markedly reduced in patients with type 2 diabetes when compared with those of healthy individuals (P < 0.01). When considering the study population as a whole, individuals with IR (homeostasis model of assessment of IR ≥2.78) had lower CTRP5 concentrations than the individuals without IR (homeostasis model of assessment of IR <2.78; P < 0.01). Serum CTRP5 negatively correlated with age, body mass index, waist‐to‐hip ratio, Systolic blood pressure, triglyceride, total cholesterol, glycated hemoglobin, fasting blood glucose, 2‐h blood glucose, fasting insulin and homeostasis model of assessment of IR. After 12 weeks of sodium–glucose cotransporter 2 inhibitor treatment, serum CTRP5 levels in type 2 diabetes patients were significantly reduced accompanied with ameliorated glycometabolism and IR compared with before treatment (P < 0.01).
Conclusions
CTRP5 is likely a marker for type 2 diabetes in humans.
The present study found that complement‐1q tumor necrosis factor‐α‐related protein isoform 5 (CTRP5) levels were reduced in type 2 diabetes patients and that plasma CTRP5 levels were related to insulin resistance. Furthermore, we found that plasma CTRP5 concentrations further decreased after dapaglifozin treatment, which showed that CTRP5 might play an important role in the development of type 2 diabetes.
Abstract
Homeobox A3 (HOXA3), one of HOX transcription factors, regulates gene expression during embryonic development. HOXA3 expression has been reported to be associated with several cancers; ...however, its role in colon cancer and underlying mechanism are still unclear. The expression of HOXA3 in 232 paired of human colon tumor and adjacent non‐tumorous tissues were measured by qPCR. The relationship between HOXA3 expression and clinical outcomes were analyzed by Kaplan‐Meier survival curves analysis. Human colon cancer cell lines HT29 and HTC116 were transfected with HOXA3 siRNA, or HOXA3 expressing vector, and then cell proliferation and apoptosis were assessed, respectively. Western blot was performed to detect the activation of EGFR/Ras/Raf/MEK/ERK signaling pathway. Moreover, HOXA3‐overexpressing and HOXA3‐suppressing HT29 cells were subcutaneous injected into nod mice to confirm the regulation of HOXA3 on EGFR/Ras/Raf/MEK/ERK signaling in regulating tumor growth. HOXA3 was upregulated in colon tumor tissues and cell lines, and upregulated expression of HOXA3 was associated with low survival rate. Knockdown of HOXA3 suppressed cell viability and clone formation, while induced cell apoptosis. HOXA3 knockdown could not induce the increase of cell apoptosis on the condition of EGFR overexpression. In vivo xenograft studies, HOXA3‐suppressing cells showed less tumorigenic. Moreover, HOXA3 knockdown suppressed the activation of EGFR/Ras/Raf/MEK/ERK signaling pathway. To conclude, this study indicated that HOXA3 might act as a promoter of human colon cancer formation by regulating EGFR/Ras/Raf/MEK/ERK signaling pathway. HOXA3 might be a potential therapeutic target for the treatment of colon cancer.
Background:
The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating ...treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer.
Methods:
Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system.
Results:
The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s.
Conclusion:
In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.
Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical prognosis. However, there are significant differences and difficulties associated with manually ...identifying tumor sprouting. This study used the Faster region convolutional neural network (RCNN) model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer.
We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019. The pathologists used LabelImg software to identify tumor buds and to count their numbers. Finally, 1,000 images were screened, and the total number of tumor buds was approximately 3,226; the images were randomly divided into a training set and a test set at a ratio of 6:4. After the images in the training set were manually identified, the identified buds in the 600 images were used to train the Faster RCNN identification model. After the establishment of the artificial intelligence identification detection platform, 400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor buds. Finally, by comparing the results of the Faster RCNN system and the identification information of pathologists, the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment. The selected performance indicators include accuracy, precision, specificity, etc. ROC (receiver operator characteristic) and AUC (area under the curve) were used to quantify the performance of the system to automatically identify tumor budding areas and numbers.
The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identification system was 0.96, the image diagnosis accuracy rate was 0.89, the precision was 0.855, the sensitivity was 0.94, the specificity was 0.83, and the negative predictive value was 0.933. After 400 test sets, pathological image verification showed that there were 356 images with the same positive budding area count, and the difference between the positive area count and the manual detection count in the remaining images was less than 3. The detection system based on tumor budding recognition in pathological sections is comparable to that of pathologists’ accuracy; however, it took significantly less time (0.03±0.01)s for the pathologist (13±5)s to diagnose the sections with the assistance of the AI model.
This system can accurately and quickly identify the tumor sprouting area in the pathological sections of colorectal cancer and count their numbers, which greatly improves the diagnostic efficacy, and effectively avoids the need for confirmation by different pathologists. The use of the AI reduces the burden of pathologists in reading sections and it has a certain clinical diagnosis and treatment value.
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