Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images.

Background: Liver tumor segmentation based on medical imaging is playing an increasingly important role in liver tumor research and individualized therapeutic decision-making. However, it remains a challenging in terms of the accuracy of automatic segmentation of liver tumors. Therefore, we aimed to develop a novel deep neural network for improving the results from the automatic segmentation of liver tumors. Methods: This paper proposes the attention-guided context asymmetric fusion network (AGCAF-Net), combining attention guidance and fusion context modules on the basis of a residual neural network for the automatic segmentation of liver tumors. According to the attention-guided context block (AGCB), the feature map is first divided into multiple small blocks, the local correlation between features is calculated, and then the global nonlocal fusion module (GNFM) is used to obtain the global information between pixels. Additionally, the context pyramid module (CPM) and asymmetric semantic fusion module (AFM) are used to obtain multiscale features and resolve the feature mismatch during feature fusion, respectively. Finally, we used the liver tumor segmentation benchmark (LiTS) dataset to verify the efficiency of our designed network. Results: Our results showed that AGCAF-Net with AFM and CPM is effective in improving the accuracy of liver tumor segmentation, with the Dice coefficient increasing from 82.5% to 84.1%. The segmentation results of liver tumors by AGCAF-Net were superior to those of several state-of-the-art U-net methods, with a Dice coefficient of 84.1%, a sensitivity of 91.7%, and an average symmetric surface distance of 3.52. Conclusions: AGCAF-Net can obtain better matched and accurate segmentation in liver tumor segmentation, thus effectively improving the accuracy of liver tumor segmentation.

Ellagic acid promotes osteoblasts differentiation via activating SMAD2/3 pathway and alleviates bone mass loss in OVX mice.

Characterized by bone mass loss, osteoporosis is an orthopedic disease typically found in postmenopausal women and aging individuals. Consistent with its pathogenesis summarized as an imbalance in bone formation/resorption, current pharmacologically therapeutic strategies for osteoporosis mainly aim to promote bone formation or/and inhibit bone resorption. However, few effective drugs with mild clinical side effects have been developed, making it a well-concerned issue to seek appropriate drugs for osteoporosis. In this study, we investigated the effect of ellagic acid (EA) on osteogenesis in vitro and in vivo and searched for its molecular mechanism. Here, we showed that EA promoted osteogenic differentiation of MSCs, increased mRNA and protein expression levels of osteoblast marker genes Runt-related transcription factor2, Osterix, Alkaline phosphatase, Collagen type I alpha 1, Osteopontin and Osteocalcin. Furthermore, ovariectomized mice with orally administered EA (10 mg/kg, 50 mg/kg) had significantly higher bone mass than those in controls. And experiments such as fluorescence double-labeling and enzyme-linked immunosorbent assay also demonstrated that EA could promote osteogenesis in vivo. To probe the molecular mechanism of EA, we performed RNA sequencing analysis using EA-treated BMSCs. Significant up-regulation of SMAD2/3 transcription factors was identified by RNA-seq, and it was confirmed in vitro that EA promoted bone formation by activating the SMAD2/3 signaling pathway. Evidence from our present experiments indicates that EA may be a promising candidate for clinical treatment for osteoporosis in future.

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study.

PURPOSE: To establish and validate a multiparameter prediction model for early recurrence after radical resection in patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC). MATERIALS AND METHODS: This study reviewed the clinical characteristics and preoperative CT images of 143 cHCC-CC patients who underwent radical resection from three institutions. A total of 110 patients from institution 1 were randomly divided into training set (n = 78) and testing set (n = 32) in the ratio of 7-3. Univariate and multivariate logistic regression analysis were used to construct a nomogram prediction model in the training set, which was internally and externally validated in the testing set and the validation set (n = 33) from institutions 2 and 3. The area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration analysis were used to evaluate the model's performance. RESULTS: The combined model demonstrated superior predictive performance compared to the clinical model, the CT model, the pathological model and the clinic-CT model in predicting the early postoperative recurrence. The nomogram based on the combined model included AST, ALP, tumor size, tumor margin, arterial phase peritumoral enhancement, and MVI (Microvascular invasion). The model had AUCs of 0.89 (95% CI 0.81-0.96), 0.85 (95% CI 0.70-0.99), and 0.86 (95% CI 0.72-1.00) in the training, testing, and validation sets, respectively, indicating high predictive power. DCA showed that the combined model had good clinical value and correction effect. CONCLUSION: A nomogram incorporating clinical characteristics and preoperative CT features can be utilized to effectively predict the early postoperative recurrence in patients with cHCC-CC.

TARSL: Triple-attention cross-network representation learning to predict synthetic lethality for anti-cancer drug discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since traditional methods for SL prediction suffer from high-cost, time-consuming and off-targets effects, computational approaches have been efficient complementary to these methods. Most of existing approaches treat SL associations as independent of other biological interaction networks, and fail to consider other information from various biological networks. Despite some approaches have integrated different networks to capture multi-modal features of genes for SL prediction, these methods implicitly assume that all sources and levels of information contribute equally to the SL associations. As such, a comprehensive and flexible framework for learning gene cross-network representations for SL prediction is still lacking. In this work, we present a novel Triple-Attention cross-network Representation learning for SL prediction (TARSL) by capturing molecular features from heterogeneous sources. We employ three-level attention modules to consider the different contribution of multi-level information. In particular, feature-level attention can capture the correlations between molecular feature and network link, node-level attention can differentiate the importance of various neighbors, and network-level attention can concentrate on important network and reduce the effects of irrelated networks. We perform comprehensive experiments on human SL datasets and these results have proven that our model is consistently superior to baseline methods and predicted SL associations could aid in designing anti-cancer drugs.

CD73 inhibits titanium particle-associated aseptic loosening by alternating activation of macrophages.

Aseptic inflammation is a major cause of late failure in total joint arthroplasty, and the primary factor contributing to the development and perpetuation of aseptic inflammation is classical macrophage activation (M1 phenotype polarization) induced by wear particles. CD73 (ecto-5'-nucleotidase) is an immunosuppressive factor that establishes an adenosine-induced anti-inflammatory environment. Although CD73 has been shown to suppress inflammation by promoting alternate macrophage activation (M2 phenotype polarization), its role in wear particle-induced aseptic inflammation is currently unknown. Our experiments were based on metabolomic assay results in a mouse model of aseptic loosening, and studied the function of CD73 in vivo and in vitro using a mouse aseptic loosening model and a mouse bone marrow derived macrophage (BMDM) inflammation model. Results show that aseptic loosening (AL) reduces the purine metabolic pathway and decreases the native expression of the metabolite adenosine. In vivo, CD73 expression was low in the bone tissue surrounding the titanium nail and synovial-like interface tissue, while in vitro experiments demonstrated that CD73 knockdown promoted titanium particles-induced aseptic inflammation. CD73 overexpression mitigated the titanium particle-mediated enhancement of LPS-induced M1 polarization while promoting the titanium particle-mediated attenuation of IL-4-induced M2 polarization. In BMDM exposed to titanium particles, CD73 promotes M2 polarization via the p38 pathway. Meanwhile, local injection of recombinant mouse CD73 protein slightly alleviated the progression of AL. Collectively, our data suggest that CD73 alleviates the process of AL, and this function is achieved by promoting alternate activation of macrophages.

Helicobacter pylori-induced aberrant demethylation and expression of GNB4 promotes gastric carcinogenesis via the Hippo-YAP1 pathway.

BACKGROUND: Helicobacter pylori (H. pylori) infection causes aberrant DNA methylation and contributes to the risk of gastric cancer (GC). Guanine nucleotide-binding protein subunit beta-4 (GNB4) is involved in various tumorigenic processes. We found an aberrant methylation level of GNB4 in H. pylori-induced GC in our previous bioinformatic analysis; however, its expression and underlying molecular mechanisms are poorly understood. METHODS: The expression, underlying signaling pathways, and clinical significance of GNB4 were analyzed in a local cohort of 107 patients with GC and several public databases. H. pylori infection was induced in in vitro and in vivo models. Methylation-specific PCR, pyrosequencing, and mass spectrometry analysis were used to detect changes in methylation levels. GNB4, TET1, and YAP1 were overexpressed or knocked down in GC cell lines. We performed gain- and loss-of-function experiments, including CCK-8, EdU, colony formation, transwell migration, and invasion assays. Nude mice were injected with genetically manipulated GC cells, and the growth of xenograft tumors and metastases was measured. Real-time quantitative PCR, western blotting, immunofluorescence, immunohistochemistry, chromatin immunoprecipitation, and co-immunoprecipitation experiments were performed to elucidate the underlying molecular mechanisms. RESULTS: GNB4 expression was significantly upregulated in GC and correlated with aggressive clinical characteristics and poor prognosis. Increased levels of GNB4 were associated with shorter survival times. Infection with H. pylori strains 26695 and SS1 induced GNB4 mRNA and protein expression in GC cell lines and mice. Additionally, silencing of GNB4 blocked the pro-proliferative, metastatic, and invasive ability of H. pylori in GC cells. H. pylori infection remarkably decreased the methylation level of the GNB4 promoter region, particularly at the CpG#5 site (chr3:179451746-179451745). H. pylori infection upregulated TET1 expression via activation of the NF-κB. TET binds to the GNB4 promoter region which undergoes demethylation modification. Functionally, we identified that GNB4 induced oncogenic behaviors of tumors via the Hippo-YAP1 pathway in both in vitro and in vivo models. CONCLUSIONS: Our findings demonstrate that H. pylori infection activates the NF-κB-TET1-GNB4 demethylation-YAP1 axis, which may be a potential therapeutic target for GC.

Higenamine Promotes Osteogenesis Via IQGAP1/SMAD4 Signaling Pathway and Prevents Age- and Estrogen-Dependent Bone Loss in Mice.

Osteoporosis is a common bone disease caused by an imbalance of bone resorption and formation that results in a loss of total bone density. SMAD2/3 signal transduction is known to play a crucial role in osteogenic differentiation through transforming growth factor-beta (TGF-ß). By screening a library of small-molecule compounds, the current study identifies higenamine (HG) as an active osteogenic agent that could be a therapeutic candidate for osteoporosis. In vitro data demonstrated that HG effectively induced expressions of osteogenic markers in mouse bone marrow stromal cell (BMSCs) and preosteoblastic cell cultures. Further, HG treatment resulted in enhanced bone formation and prevented accelerated bone loss on two animal models that mimic spontaneous senile osteoporosis and postmenopausal osteoporosis. IQ motif-containing GTPase-activating protein 1 (IQGAP1) was confirmed as a novel target of HG, where HG appears to bind to the Glu-1019 site of IQGAP1 to exert its osteogenic effects. Data subsequently suggested that HG promoted phosphorylation of SMAD2/3 and regulated the SMAD2/3 pathway by inhibiting SMAD4 ubiquitination. Overall, the findings highlight HG as a new small-molecule drug to promote bone formation through SMAD2/3 pathway in osteoporosis. © 2023 American Society for Bone and Mineral Research (ASBMR).

Periplocin targets low density lipoprotein receptor-related protein 4 to attenuate osteoclastogenesis and protect against osteoporosis.

Osteoporosis is a common inflammaging-related condition, where long-term accumulation of pro-inflammatory cytokines causes massive bone loss. Periplocin, a cardiotonic steroid isolated from Periploca forrestii, has been proved to reduce inflammation in several inflammatory diseases, such as rheumatoid arthritis. However, its effect and mechanism of inflammation in osteoporosis, in which pro-inflammatory factors accelerate bone loss, has not been well demonstrated. In this study, periplocin attenuated receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation of bone marrow-derived macrophages (BMMs) and RAW264.7 cells in vitro. It reduced osteoclast numbers and bone resorption in a concentration- and time-dependent manner. Further, periplocin treatment resulted in reduced bone loss on mice with ovariectomy-induced osteoporosis in vivo. By transcriptome sequencing, periplocin was indicated to function through inhibition of the mitogen-activated protein kinase (MAPK) and nuclear factor-κB (NF-κB) signaling pathways and attenuating interactions between NF-κB and nuclear factor of activated T-cells 1 (NFATc1). It was further detected to bind low density lipoprotein receptor-related protein 4 (LRP4) in osteoclasts to exert anti-inflammatory and anti-osteoclastic effects. Overall, the findings have highlighted a better understanding for the anti-inflammatory and anti-osteoclastic role of periplocin in osteoporosis and its mechanism, bringing new possibilities for osteoporosis treatment.

Prediction of miRNA-disease associations by neural network-based deep matrix factorization.

MicroRNA(miRNA) is a class of short non-coding RNAs with a length of about 22 nucleotides, which participates in various biological processes of cells. A number of studies have shown that miRNAs are closely related to the occurrence of cancer and various human diseases. Therefore, studying miRNA-disease associations is helpful to understand the pathogenesis of diseases as well as the prevention, diagnosis, treatment and prognosis of diseases. Traditional biological experimental methods for studying miRNA-disease associations have disadvantages such as expensive equipment, time-consuming and labor-intensive. With the rapid development of bioinformatics, more and more researchers are committed to developing effective computational methods to predict miRNA-disease associations in roder to reduce the time and money cost of experiments. In this study, we proposed a neural network-based deep matrix factorization method named NNDMF to predict miRNA-disease associations. To address the problem that traditional matrix factorization methods can only extract linear features, NNDMF used neural network to perform deep matrix factorization to extract nonlinear features, which makes up for the shortcomings of traditional matrix factorization methods. We compared NNDMF with four previous classical prediction models (IMCMDA, GRMDA, SACMDA and ICFMDA) in global LOOCV and local LOOCV, respectively. The AUCs achieved by NNDMF in two cross-validation methods were 0.9340 and 0.8763, respectively. Furthermore, we conducted case studies on three important human diseases (lymphoma, colorectal cancer and lung cancer) to validate the effectiveness of NNDMF. In conclusion, NNDMF could effectively predict the potential miRNA-disease associations.

DEPDC1 as a crucial factor in the progression of human osteosarcoma.

OBJECTIVE: Novel therapeutic strategies are emerging with the increased understanding of the underlying mechanisms of human osteosarcoma. This current study tends to decipher the potentially critical role of DEP domain-containing 1 (DEPDC1), a tumor-related gene, during the progression of osteosarcoma. METHODS: Bioinformatics analysis of 25,035 genes from the National Center for Biotechnology Information (NCBI) databases was performed to screen differentially expressed genes between osteosarcoma and normal control groups, complemented by the examination of 85 clinical osteosarcoma specimens. Furthermore, the manipulation of DEPDC1 expression levels by using silencing RNA (siRNA) or lentiviral vector intervention on human osteosarcoma cells was performed to reveal its role and interactions in in vitro and in vivo settings. RESULTS: Gene expression profile analysis and immunohistochemical (IHC) examination suggested that DEPDC1 is highly expressed in human osteosarcoma cells and tumor tissue. The silencing of DEPDC1 arrested osteosarcoma cell proliferation, promoted apoptosis, and ceased tumor metastasis. Studies involving clinical human osteosarcoma cases exhibited a strong correlation of DEPDC1 over-expressed osteosarcoma specimens with a reduced patient survival rate. CONCLUSIONS: Collectively, this study demonstrated that DEPDC1 is a critical driver in the promotion of osteosarcoma progression and results in poor patient prognosis. Genetically targeting or pharmacologically inhibiting DEPDC1 may serve as a promising strategy for treating human osteosarcoma.

Down-regulating Circular RNA Prkcsh suppresses the inflammatory response after spinal cord injury.

Circular RNAs (circRNAs) are a class of conserved, endogenous non-coding RNAs that are involved in transcriptional and post-transcriptional gene regulation and are highly enriched in the nervous system. They participate in the survival and differentiation of multiple nerve cells, and may even promote the recovery of neurological function after stroke. However, their role in the inflammatory response after spinal cord injury remains unclear. In the present study, we established a mouse model of T9 spinal cord injury using the modified Allen's impact method, and identified 16,013 circRNAs and 960 miRNAs that were differentially expressed after spinal cord injury. Of these, the expression levels of circPrkcsh were significantly different between injured and sham-treated mice. We then treated astrocytes with tumor necrosis factor-α in vitro to simulate the inflammatory response after spinal cord injury. Our results revealed an elevated expression of circPrkcsh with a concurrent decrease in miR-488 expression in injured cells. We also found that circPrkcsh regulated the expression of the inflammation-related gene Ccl2. Furthermore, in tumor necrosis factor-α-treated astrocytes, circPrkcsh knockdown decreased the expression of Ccl2 by upregulating miR-488 expression, and reduced the secretion of inflammatory cytokines in vitro. These findings suggest that differentially expressed circRNAs participate in the inflammatory response after spinal cord injury and act as the regulators of certain microRNAs. Furthermore, circPrkcsh may be used as an miR-488 sponge to regulate Ccl2 expression, which might provide a new potential therapy for SCI. The study was approved by the Animal Ethics Committee of Shandong University of China (approval No. KYLL-20170303) on March 3, 2017.

Follow-up study of depressive state on patients with atrial fibrillation 1 year after radio-frequency ablation.

Objective: To analyze the effect of depression on the recurrence of atrial fibrillation (AF) 1 year after radio-frequency ablation. Methods: A total of 91 patients with AF admitted to our hospital from January 2020 to July 2021 were studied. All patients were followed up 1 year after radio-frequency ablation. A total of 91 subjects were divided into recurrence group (n = 30) and no recurrence group (n = 61) according to the recurrence situation 1 year after radio-frequency ablation. Age, disease course, body mass index (BMI), gender, echocardiography (left atrial diameter), blood inflammatory indicators (neutrophil count, lymphocyte count, and monocyte count), and Self-rating Depression Scale (SDS) scores were compared between the two groups. Logistic multivariate regression analysis was used to analyze the effect of SDS score and other indexes on the recurrence of AF 1 year after radio-frequency ablation. Results: The age of patients in relapse group was higher than that in no relapse group (P < 0.05) and the course of disease was longer than that of the no recurrence group (P < 0.05). The BMI was higher than that of the no recurrence group (P < 0.05) and the left atrial diameter was greater than that of the no recurrence group (P < 0.05). Neutrophil count and monocyte count were significantly higher than those in no recurrence group (P < 0.05) and the lymphocyte count was significantly lower than that in the no recurrence group (P < 0.05). There were significant differences in SDS score composition between the two groups (P < 0.05) and the composition ratio of patients with moderate and major depression in the relapsing group was significantly higher than that in the non-relapsing group. The composition ratio of patients without depression in the relapsing group was significantly lower than that in the non-relapsing group. Multivariate analysis showed that age, disease course, BMI, left atrial diameter, neutrophil count, lymphocyte count, monocyte count, and SDS score were all independent factors affecting the recurrence of AF patients 1 year after radio frequency ablation (P < 0.05). Compared with patients without depression, patients with mild, moderate and major depression had an increased risk of recurrence by 74.0, 98.2, and 151.2% 1 year after radio-frequency ablation, respectively (OR = 1.740, 1.982, and 2.512). Conclusion: There is a high rate of depression in patients with AF. Depression is an important factor affecting the early recurrence of patients with AF after radio-frequency ablation.

An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria.

Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been some computational methods to study tumor heterogeneity from the level of genome, transcriptome, and histology, but these methods still have certain limitations. In this study, we proposed an epistasis and heterogeneity analysis method based on genomic single nucleotide polymorphism (SNP) data. First of all, a maximum correlation and maximum consistence criteria was designed based on Bayesian network score K2 and information entropy for evaluating genomic epistasis. As the number of SNPs increases, the epistasis combination space increases sharply, resulting in a combination explosion phenomenon. Therefore, we next use an improved genetic algorithm to search the SNP epistatic combination space for identifying potential feasible epistasis solutions. Multiple epistasis solutions represent different pathogenic gene combinations, which may lead to different tumor subtypes, that is, heterogeneity. Finally, the XGBoost classifier is trained with feature SNPs selected that constitute multiple sets of epistatic solutions to verify that considering tumor heterogeneity is beneficial to improve the accuracy of tumor subtype prediction. In order to demonstrate the effectiveness of our method, the power of multiple epistatic recognition and the accuracy of tumor subtype classification measures are evaluated. Extensive simulation results show that our method has better power and prediction accuracy than previous methods.

CircPrkcsh, a circular RNA, contributes to the polarization of microglia towards the M1 phenotype induced by spinal cord injury and acts via the JNK/p38 MAPK pathway.

Spinal cord injury (SCI) is a complex pathological change that includes primary SCI and gradually evolves into secondary SCI. Accumulating evidence demonstrates that circular RNAs (circRNAs) are involved in the pathology of a variety of neurological diseases and injuries. However, the characteristics and function of circRNAs in SCI have yet to be elucidated. Although previous research demonstrated that circPrkcsh induces astrocytes to produce inflammatory factors and chemokines, the precise function and mechanism of circPrkcsh in microglia after SCI remains unknown. In this study, we constructed a mouse model of SCI by applying a SCI impactor. Quantitative Real-time PCR and Fluorescence in situ hybridization analysis revealed that circPrkcsh was upregulated in the microglia of SCI mice when compared to sham-operated mice. Gain- or loss-of-function experiments and in vivo assays further indicated that circPrkcsh promotes microglia M1 polarization both in vivo and in vitro. Furthermore, bioinformatics analysis, dual-luciferase assays, and RNA immunoprecipitation assays, confirmed that circPrkcsh serves as a competing endogenous RNA (ceRNA) to promote the expression of MEKK1 mRNA by sponging miR-488. Double knockout rescue experiments further showed that circPrkcsh regulates the MEKK1/JNK/p38 MAPK pathway via miR-488. Our research provides a better understanding of the mechanism of circPrkcsh in SCI and demonstrates that the circPrkcsh/miR-488/Mekk1 axis is a promising regulatory method for the treatment of SCI.

Peritumoral edema in preoperative magnetic resonance imaging is an independent prognostic factor for hepatocellular carcinoma.

BACKGROUND: Peritumoral edema is an independent prognostic risk factor for malignant tumors. Therefore, assessment of peritumoral edema in preoperative magnetic resonance imaging (MRI) may provide better prognostic information in patients with hepatocellular carcinoma (HCC). AIM: To determine whether peritumoral edema in preoperative MRI is a prognostic factor for HCC. METHODS: A retrospective analysis of 90 patients with HCC confirmed by surgical pathology was performed. All patients' peritumoral edema in preoperative MRI was reviewed by two radiologists. The association of disease recurrence with peritumoral edema and clinicopathological features was assessed using the Cox proportional hazards model. Interobserver agreement for evaluating peritumoral edema was determined using Cohen's κ coefficient. RESULTS: Recurrence and non-recurrence after an average 20.8 month follow-up was 25.6% (23/90) and 74.4% (67/90), respectively. The ratio of peritumoral edema of 90 patients with HCC in preoperative MRI was 35.6% (32/90). In univariate Cox regression analysis, peritumoral edema [hazard ratio (HR) 11.08, P < 0.001], tumor diameter (HR 4.12, P = 0.001), microvascular invasion (HR 2.78, P = 0.020), gender (HR 0.29, P = 0.006), cirrhosis (HR 2.45, P = 0.049), ascites syndrome (HR 2.83, P = 0.022), aspartate aminotransferase(AST)/alanine aminotransferase(ALT) (HR 5.07, P = 0.003) were indicators for HCC recurrence. In multivariate Cox regression analysis, the tumor diameter (HR 2.53, P = 0.032) and peritumoral edema (HR 8.71, P < 0.001) were independent prognostic factors of HCC. The sensitivity, specificity, positive predictive value and negative predictive value of peritumoral edema and tumor diameter were 82.6%&60.9%, 80.6%&77.6%, 59.4%&48.3%, and 93.1%&85.3%, respectively. CONCLUSION: Peritumoral edema in preoperative MRI may be considered as a biomarker of prognostic information for patients with HCC.

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION: Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.

Inconsistency of Bone Mineral Density Between Femoral Head and Proximal Femur After Femoral Neck Fracture Surgery Indicates Great Possibility of Femoral Head Necrosis.

On clinical observation, it was found that the bone mineral density (BMD) of the femoral head and proximal femur was not consistent in some patients with femoral neck fracture after surgery. The current study was performed to explore whether this phenomenon was associated with femoral head necrosis after surgery for femoral neck fracture. Bone mineral density inconsistency is when the difference of the sum of pixel values on both sides of the fracture line has exceeded 30%. Statistical analysis was performed on the clinical characteristics of 271 patients who had received the operation for femoral neck fracture. Chi-square test, Spearman rank correlation, independent sample t test, Kaplan-Meier method, and log-rank test, as well as univariate Cox regression and multivariate Cox regression, were used to analyze the potential relationship among related factors. It was revealed that the incidence of inconsistency in BMD between the femoral head and proximal femur was significantly increased in patients with femoral head necrosis after surgery for femoral neck fracture, and that the consistency was considerably high between BMD inconsistency and femoral head necrosis. The inconsistent BMD occurred 11.1 months earlier than the necrosis of the femoral head. Cox multivariate regression analysis indicated that the inconsistency in BMD between the femoral head and proximal femur after surgery for femoral neck fracture was an independent prognostic factor affecting femoral head necrosis. The inconsistent changes in BMD between the femoral head and proximal femur after surgery for femoral neck fracture indicate a great possibility of femoral head necrosis. [Orthopedics. 2021;44(2):e223-e228.].

A Multi-Source Data Fusion Framework for Revealing the Regulatory Mechanism of Breast Cancer Immune Evasion.

For precision medicine, there is an enormous need to understand the immune evasion mechanism of tumor development, especially when tumor heterogeneity significantly affects the effect of immunotherapy. Recognizing the subtypes of breast cancer based on the immune-related genes helps to understand the immune escape pathways dominated by different subtypes, so as to implement effective treatment measures for different subtypes. For that, we used non-negative matrix factorization and consistent clustering algorithm on The Cancer Genome Atlas RNA-seq breast cancer data and recognized 4 subtypes according to the curated immune-related genes. Then, we conducted differential expression analysis between each subtype of breast cancer and normal tissue of RNA-seq data from non-cancer individuals collected by the Genotype-Tissue Expression to find out subtype-related immune genes. After that, we carried out correlation analysis between copy number variants (CNV) and mRNA of immune genes and investigated the regulatory mechanism of the immune genes, which cannot be explained by CNV based on ATAC-seq data. The experimental results reveal that CDH1 and PVRL2 are potential for immune evasion in all 4 subgroups. The expression variations of CDH1 can be mainly explained by its CNV, while the expression variation of PVRL2 is more likely regulated by transcript factors.

Ultrasound imaging of carotid web with atherosclerosis plaque: a case report.

BACKGROUND: To the best of our knowledge, no previous studies on carotid webs with atherosclerosis plaque have been conducted. Thus, both radiologists and clinicians have insufficient knowledge of this disease, which could lead to misdiagnosis and missed diagnosis. An accurate diagnosis is beneficial to clinical management and prevention of stroke. Here, we present a case of a carotid web with an atherosclerotic plaque, which was confirmed by histopathology and was treated at the Department of Neurosurgery, Beijing Tiantan Hospital. CASE PRESENTATION: We report a rare case of a carotid web with an atherosclerotic plaque in a 61-year-old Han man. He presented to our hospital with history of intermittent dizziness and slurred speech for 1.5 years and numbness of both upper limbs for 4 months. A computed tomography angiography examination indicated severe stenosis at the beginning of the left internal carotid artery with plaque surface ulceration. Doppler ultrasound examination showed a carotid web with a thin isoechoic plaque and a membrane-like structure protruding into the lumen from the lateral posterior wall at the beginning of the left internal carotid artery. The thin isoechoic plaque could be seen at the base of the membrane-like structure. Carotid endarterectomy was performed to alleviate symptoms. A carotid web with atherosclerosis was diagnosed intraoperatively, and postoperative pathology confirmed extensive intima fibroid hyperplasia accompanied with myxoid degeneration. The base of the carotid web was attached to the thin atherosclerosis plaque, and between the web and the plaque, a cavity was observed. In this case report, we aim to discuss the diagnosis of carotid web with atherosclerosis, its physiopathology and management, and the possible reasons for missed diagnosis or misdiagnosis. CONCLUSION: Carotid webs with atherosclerosis have no known etiological factors and are rarely reported. Thus, carotid webs could be easily confused with ulcerations on the surface of the atherosclerosis plaque. The diagnosis could be difficult and effective management remains indeterminate. Moreover, prompt recognition of this disease is key to correct treatment and management. Hence, this case report and the relevant data in the literature could contribute to the improvement of the diagnosis and treatment of this disease.

Reactive Astrogliosis: Implications in Spinal Cord Injury Progression and Therapy.

Astrocytes are the most populous glial cells in the central nervous system (CNS). They are essential to CNS physiology and play important roles in the maintenance of homeostasis, development of synaptic plasticity, and neuroprotection. Nevertheless, under the influence of certain factors, astrocytes may also exert detrimental effects through a process of reactive astrogliosis. Previous studies have shown that astrocytes have more than one type of polarization. Two types have been extensively researched. One is a damaging change that occurs under inflammation and has been termed A1 astrocyte, while the other is a restorative change that occurs under ischemic induction and was termed A2 astrocyte. Researchers are now increasingly paying attention to the role of astrocytes in spinal cord injury (SCI), degenerative diseases, chronic pain, neurological tumors, and other CNS disorders. In this review, we discuss (a) the characteristics of polarized astrocytes, (b) the relationship between astrocyte polarization and SCI, and (c) new implications of reactive astrogliosis for future SCI therapies.