Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer deaths worldwide. Recent studies have shown that cancer stem cells (CSCs) are an important cause of ...tumor recurrence and metastasis. We hypothesized that CSCs marker CD166-positive CRC and colorectal adenoma (CAD) cells consist of more hotspot mutations than CD166-negative CRC and colorectal adenoma cells. To verify this, formalin fixed paraffin embedded tissue specimens from 42 patients each with CRC and CAD were recruited and CD166 immunohistochemical (IHC) staining followed by macrodissection was performed. DNA extracted was used for quantitative polymerase chain reaction detection on a somatic mutation array. Results showed that the immunoreactivity of CD166 protein had significant difference among CRC, CAD, and normal colorectal epithelial tissues (NCET) (
< 0.0001, Kruskal-Wallis test). Moreover, nucleotide changes were found in
,
,
,
,
and
genes. Among those genes,
exon 2 mutations were validated in another cohort of 70 CRC and 72 CAD specimens. Results showed that the difference in percentage of
exon 2 mutations between CD166 positive and CD166 negative CRC specimens was significant (
< 0.05, chi-square test). Long term follow-up of the CRC patients showed that CD166-positive KRAS exon 2 mutations was useful in discriminating CRC patients with worse outcome. This study has provided evidence that
exon 2 mutations are concentrated in CD166-positive cancer cells, with prognostic significance in CRC, and those mutations are also detected in CAD.
Cardiomyopathy is a clinical problem that occurs in the hearts of type 2 diabetic patients as well as cancer patients undergoing doxorubicin chemotherapy. The number of diabetic cancer patients is ...increasing but surprisingly the cardiac damaging effects of doxorubicin, a commonly used chemotherapeutic drug, on diabetic hearts have not been well-examined. As the signaling mechanisms of the doxorubicin-induced cardiomyopathy in type 2 diabetic heart are largely unknown, this study examined the molecular signaling pathways that are responsible for the doxorubicin-induced cardiotoxicity in type 2 diabetic hearts. Male 14- to 18-week-old db/db mice were used as the type 2 diabetic model, and age-matched non-diabetic db/+ mice served as controls. The db/+ non-diabetic and db/db diabetic mice were randomly assigned to the following groups: db/+CON, db/+DOX-5d, db/+DOX-7d, db/dbCON, db/dbDOX-5d, and db/dbDOX-7d. Mice assigned to doxorubicin (DOX) group were exposed to an intraperitoneal (i.p.) injection of DOX at a dose of 15 mg/kg to induce cardiomyopathy. Mice in control (CON) groups were i.p. injected with the same volume of saline instead of DOX. Mice were euthanized by overdose of ketamine and xylazine 5 or 7 days after the DOX injection. Microarray analysis was adopted to examine the changes of the whole transcriptional profile in response to doxorubicin exposure in diabetic hearts. Ventricular fractional shortening was examined as an indicator of cardiac function by transthoracic echocardiography. The presence of diabetic cardiomyopathy in db/db mice was evident by the reduction of fractional shortening. There was a further impairment of cardiac contractile function 7 days after the DOX administration in db/db diabetic mice. According to our microarray analysis, we identified a panel of regulatory genes associated with cardiac remodeling, inflammatory response, oxidative stress, and metabolism in the DOX-induced cardiac injury in diabetic heart. The microarray results of selected genes were confirmed by real time PCR. Notably, S100A8 and S100A9 were found to have a unique specific expression pattern that was coincident with the DOX-induced cardiomyopathy in diabetic hearts. Correspondingly, NF-κB expression in diabetic hearts was increased together with the elevation of S100A8/9 and activation of p38 MAPK signaling after DOX administration, which induced cardiac inflammation as demonstrated by the elevation of cardiac IL-6 level. These findings provide novel pre-clinical information for revealing the S100A8/A9-associated molecular signaling pathways that mediate the doxorubicin-induced cardiotoxicity in diabetic hearts.
Next-generation sequencing comprehensive genomic panels (NGS CGPs) have enabled the delivery of tailor-made therapeutic approaches to improve survival outcomes in patients with cancer. Within the ...China Greater Bay Area (GBA), territorial differences in clinical practices and health care systems and strengthening collaboration warrant a regional consensus to consolidate the development and integration of precision oncology (PO). Therefore, the Precision Oncology Working Group (POWG) formulated standardized principles for the clinical application of molecular profiling, interpretation of genomic alterations, and alignment of actionable mutations with sequence-directed therapy to deliver clinical services of excellence and evidence-based care to patients with cancer in the China GBA.
Thirty experts used a modified Delphi method. The evidence extracted to support the statements was graded according to the GRADE system and reported according to the Revised Standards for Quality Improvement Reporting Excellence guidelines, version 2.0.
The POWG reached consensus in six key statements: harmonization of reporting and quality assurance of NGS; molecular tumor board and clinical decision support systems for PO; education and training; research and real-world data collection, patient engagement, regulations, and financial reimbursement of PO treatment strategies; and clinical recommendations and implementation of PO in clinical practice.
POWG consensus statements standardize the clinical application of NGS CGPs, streamline the interpretation of clinically significant genomic alterations, and align actionable mutations with sequence-directed therapies. The POWG consensus statements may harmonize the utility and delivery of PO in China's GBA.
The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image ...enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification.
Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models.
Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images.
The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.
The molecular investigation of lung cancer has opened up an advanced area for the diagnosis and therapeutic management of lung cancer patients. Gene alterations in cancer initiation and progression ...provide not only information on molecular changes in lung cancer but also opportunities in advanced therapeutic regime by personalized targeted therapy. EGFR mutations and ALK rearrangement are important predictive biomarkers for the efficiency of tyrosine kinase inhibitor treatment in lung cancer patients. Moreover, epigenetic aberration and microRNA dysregulation are recent advances in the early detection and monitoring of lung cancer. Although a wide range of molecular tests are available, standardization and validation of assay protocols are essential for the quality of the test outcome. In this review, current and new advancements of molecular biomarkers for non-small-cell lung cancer will be discussed. Recommendations on future development of molecular diagnostic services will also be explored.
Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a ...two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR).
In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation.
Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19).
A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.
This study investigated regional volumetric trabecular bone mineral density (tBMD) and bone area at the ultradistal tibia in Chinese women using peripheral quantitative computed tomography. Fifty-six ...postmenopausal women aged 47-62 yr participated in BMD measurements at baseline and 22 of them were followed at both 1-yr and 3-yr follow-up scans. Regional baseline tBMD, rate of annual bone loss, and trabecular bone area were determined. Baseline measurements showed that the tBMD of both the posterior (252.9+/-63.4 mg/cm(3)) and medial (226.6+/-68.9 mg/cm(3)) regions was significantly higher than that of the anterior (126.3+/-61.9 mg/cm(3)) and lateral regions (149.8+/-50.6 mg/cm(3)), respectively (p<0.001). Both the 1-yr and 3-yr follow-up measurements showed that there was significant physiological annual tBMD loss on an average of 1.61%, at the four regions. Inter-slice regional tBMD and trabecular bone area measurements demonstrated a significant linear decrease from the distal to proximal aspects (p<0.001). Findings suggest that dynamic compressive loading during the heel strike and the body weight vector shifting toward the medial aspect during the stance phase in a normal gait might account for the regional tBMD differences. Increased tBMD and bone area toward the distal tibial endplate may adapt to withstand the axial impact loading. However, the low-impact weight-bearing nature of a normal gait may not be osteogenic to prevent regional bone loss. An exercise program specific to the women at risk should be contemplated.
BackgroundCoronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources ...and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. MethodsTwo bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). ResultsBone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed). For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. ConclusionsRajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.
MicroRNAs are a class of non-coding RNAs and the dysregulated expression of these short RNA molecules was frequently observed in cancer cells. The steady state level of microRNA concentration may ...differentiate the biological function of the cells between normal and impaired. To understand the steady state or equilibrium of microRNAs, their interactions with transcription factors and target genes need to be explored and visualized through prediction and network analysis algorithms. This article discusses the application of mathematical model for simulating the dynamics of network feedback loop so as to decipher the mechanism of microRNA regulation.
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