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Abstract Objectives Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach. Methods Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman’s correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis. Results Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD. Conclusions The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Abstract Background Cervical cancer (CC) is a danger to women’s health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions. Method In this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO). Result Here, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration. Conclusion Our study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage. Impact These findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers.

Abstract Objectives Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. Methods A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. Results Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. Conclusions These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

Abstract Background Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels. Methods Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC–MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. Results Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort. Conclusion Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

Abstract Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Abstract An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.

IntroductionThe retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments.MethodsThis study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases.ResultsNotably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models.DiscussionWith the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Abstract As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

Summary: Background: Patients with pancreatobiliary tract cancer usually have a poor clinical outcome, with a 5-year overall survival rate below 20%. This is mainly associated with the late diagnosis. In addition, the standard-of-care for patients with malignant biliary stenosis involves a major surgery, the Whipple procedure. An accurate preoperative diagnosis, including differentiation from benign diseases, is critical to avoid unnecessary treatment. Here we developed BileScreen, a sensitive detection modality for the diagnosis of pancreatobiliary tract cancer based on massively parallel sequencing mutation and methylation changes in bile samples. Methods: A total of 338 patients, from five hospitals in China, with pancreatobiliary system disorders were enrolled in this study between November 2018 and October 2020, and 259 were included for the analysis of BileScreen. We profiled 23 gene mutations and 44 genes with methylation modifications in parallel from bile samples, and set up a model for the detection of malignancy based on multi-level biomarkers. Findings: We applied the BileScreen assay in a training cohort (n = 104) to set up the model and algorithm. The model was further evaluated in a validation cohort (n = 105), resulting in 92% sensitivity and 98% specificity. The performance of BileScreen was further assessed in a prospective test cohort (n = 50) of patients diagnosed with suspicious or negative pathology by endoscopic retrograde cholangiopancreatography and were confirmed in follow-up. BileScreen yielded 90% sensitivity and 80% specificity, and outcompeted serum carbohydrate antigen 19-9 in detecting pancreatobiliary tract cancer in all three cohorts, especially in terms of specificity. Interpretation: Taken together, BileScreen has the ability to interrogate mutations and methylation changes in bile samples in parallel, thus rendering it a potentially sensitive detection method to help in the diagnosis of pancreatobiliary tract cancer in a safe, convenient and less-invasive manner. Funding: This study was supported by the Capital's Funds for Health Improvement and Research (2020-2-4025 to S.H.), the National Natural Science Foundation of China (81972311 to H.Z.), CAMS Innovation Fund for Medical Sciences (CIFMS) (2017-12M-4-002 to H.Z.), the CAMS Innovation Fund for Medical Sciences(CIFMS) (2021-I2M-1-066 to CJQ), the Non-profit Central Research Institution Fund of Chinese Academy of Medical Sciences (2019PT310026 to H.Z.) and Sanming Project of Medicine in Shenzhen (SZSM202011010 to H.Z.).

ObjectiveRheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA.MethodsMetabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively.ResultsThere were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion.ConclusionThe plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.

Hypertension is a complex disease which is mainly influenced by genetic factors. Recently, genome-wide association study (GWAS) found three novel endothelial dysfunction-related sites: Vascular endothelial growth factor A (VEGFA) rs9472135, Faciogenital dysplasia 5 (FGD5) rs11128722, Zinc Finger C3HC-type Containing 1 (ZC3HC1) rs11556924. Endothelial dysfunction is one of the early events in pathophysiology of essential hypertension. To investigate the association of endothelial dysfunction-related genes with essential hypertension, we conducted a case-control study of 431 patients with hypertension and 345 controls. The polymorphisms were detected using Taqman Probe. The alleles and genotypes of ZC3HC1 rs11556924 and VEGFA rs9472135 were not statistically different between the two groups, while the allele of FGD5 rs11128722 was different [P = 0.045, OR = 1.265, 95% CI = (1.009–1.586)], especially in the male [P = 0.035, OR = 1.496, 95% CI = (1.037–2.158)]. Analyzing the different of genotype distribution of 3 SNPs in the two groups under different genetic models, the genotypes of FGD5 rs11128722 showed difference in male under dominant model [P = 0.049, OR = 1.610, 95% CI = (1.018–2.544)]. The polymorphism of FGD5 rs11128722 had a significant difference in Body Mass Index (BMI) among different genotypes; In the additive genetic model, BMI of GA genotype was higher than that of GG (P = 0.038); GA + AA was higher than GG in the dominant genetic model (P = 0.011). In our study, we found that the polymorphisms of VEGFA rs9472135 and ZC3HC1 rs11556924 may not significantly associated with the risk of essential hypertension, and FGD5 rs11128722 may increase the risk of it, especially in elderly men.

Abstract Glioblastoma (GBM), one of the most common primary intracranial malignant tumours, is very difficult to be completely excised by surgery due to its irregular shape. Here, we use an MRI/NIR fluorescence dual-modal imaging nanoprobe that includes superparamagnetic iron oxide nanoparticles (SPIONs) modified with indocyanine (Cy7) molecules and peptides (ANG or DANG) to locate malignant gliomas and guide accurate excision. Both peptides/Cy7-SPIONs probes displayed excellent tumour-homing properties and barrier penetrating abilities in vitro, and both could mediate precise aggregation of the nanoprobes at gliomas sites in in vivo magnetic resonance imaging (MRI) and ex vivo near-infrared (NIR) fluorescence imaging. However, compared with ANG/Cy7-SPIONs probes, DANG/Cy7-SPIONs probes exhibited better enhanced MR imaging effects. Combining all these features together, this MRI/NIR fluorescence imaging dual-modal nanoprobes modified with retro-enantio isomers of the peptide have the potential to accurately display GBMs preoperatively for precise imaging and intraoperatively for real-time imaging.

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

Abstract The triple-negative breast carcinoma (TNBC) is the most aggressive subtype of breast cancer. In TNBC, Aquaporin 1 (AQP1), a water-transporting transmembrane protein, is aberrantly enriched in cytoplasm and causes tumor cell death evasion. However, the carcinogenetic bioactivities of cytoplasmic AQP1 cannot be attributed to the canonical “osmotic engine model”. In the present study, the receptor-interacting protein kinase 1 (RIPK1), a cell death regulator, was identified to negatively mediate AQP1-driven TNBC progression and metastasis. AQP1 overabundance and RIPK1 depletion occurred in TNBC, which were correlated with aggressive oncological features and poor prognosis. AQP1 bound with RIPK1, resulting in the inhibition of RIPK1/RIPK3/MLKL-mediated necroptosis and RIPK1/caspase-8/caspase-3-mediated apoptosis. Genetic inhibition of RIPK1 significantly exacerbated the pro-tumor effect of AQP1, while ectopic expression of RIPK1 notably blunted AQP1 signaling. Mechanistically, AQP1 binds to the D324 site of RIPK1, and facilitates RIPK1 cleavage and inactivation by excessively activating the caspase-8/RIPK1 negative feedback loop. RIPK1D324K overexpression significantly prevented RIPK1 cleavage and weakened the aggressiveness of AQP1-enriched TNBC cells. Overall, our findings clarify the underlying mechanism of cytoplasmic AQP1-driven TNBC progression and metastasis, in which RIPK1 exerts an essential role as a negative mediator and exhibits the potential as a therapeutic target for TNBC.

Abstract The 2019 coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus is an ongoing global health emergency. However, the virus’ pathogenesis remains unclear, and there is no cure for the disease. We investigated the dynamic changes of blood immune response in patients with COVID-19 at different stages by using 5’ gene expression, T cell receptor (TCR), and B cell receptors (BCR) V(D)J transcriptome analysis at a single-cell resolution. We obtained single-cell mRNA sequencing (scRNA-seq) data of 341,420 peripheral blood mononuclear cells (PBMCs) and 185,430 clonotypic T cells and 28,802 clonotypic B cells from 25 samples of 16 patients with COVID-19 for dynamic studies. In addition, we used three control samples. We found expansion of dendritic cells (DCs), CD14+ monocytes, and megakaryocytes progenitor cells (MP)/platelets and a reduction of naïve CD4+ T lymphocytes in patients with COVID-19, along with a significant decrease of CD8+ T lymphocytes, and natural killer cells (NKs) in patients in critical condition. The type I interferon (IFN-I), mitogen-activated protein kinase (MAPK), and ferroptosis pathways were activated while the disease was active, and recovered gradually after patient conditions improved. Consistent with this finding, the mRNA level of IFN-I signal-induced gene IFI27 was significantly increased in patients with COVID-19 compared with that of the controls in a validation cohort that included 38 patients and 35 controls. The concentration of interferon-α (IFN-α) in the serum of patients with COVID-19 increased significantly compared with that of the controls in an additional cohort of 215 patients with COVID-19 and 106 controls, further suggesting the important role of the IFN-I pathway in the immune response of COVID-19. TCR and BCR sequences analyses indicated that patients with COVID-19 developed specific immune responses against SARS-CoV-2 antigens. Our study reveals a dynamic landscape of human blood immune responses to SARS-CoV-2 infection, providing clues for therapeutic potentials in treating COVID-19.

Abstract The lung lesions of this COVID‐19 patient were slowly absorbed, and the clinical symptoms with shortness of breath were improved slowly in the recovery period.

Abstract Background The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients. Methods Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared. Results Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P

Abstract Accurate prediction of metastasis is an important determinant for selecting appropriate treatment for advanced colorectal cancer (CRC). In this study, 1250 patients in two hospitals from 2014 to 2019 histologically diagnosed with CRC were enrolled. We performed the transcriptome analysis on 141 CRC patients. RNA‐seq analysis revealed that long noncoding RNA (LncRNA) colorectal neoplasia differentially expressed (CRNDE) played an important role in CRC metastasis. The least absolute shrinkage and selection operator regression was used to select features and develop radiomics model. Multivariate logistic regression analysis was used to develop combined model. The radiomics model with 13 filtered radiomics features had good discrimination in predicting expression level of LncRNA CRNDE in training set (receiver operating characteristic [AUC] = 0.809) and testing set (AUC = 0.755). Furthermore, the radiomics model could predict the metastasis of CRC in internal validation set (AUC, 0.665) and in external validation set (AUC = 0.690). The combined model developed with radiomics score and carcinoembryonic antigen had better performance, and the AUC was 0.708, 0.700 in internal validation set and in external validation set, respectively. In conclusion, we proposed a radiomics model and combined model, which could predict the expression level of LncRNA CRNDE and further predict CRC metastasis, thereby helping clinician make treatment decisions.

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

Background Assessment of colorectal cancer (CRC) lymph node metastasis (LNM) is critical to the decision of surgery, prognosis, and therapy strategy. In this study, we aimed to develop and validate a multiple tumor marker nomogram for predicting LNM in CRC patients. Methods A total of 674 patients who met the inclusion criteria were collected and randomly divided into primary cohort and internal test cohort at a ratio of 7:3. An external test cohort enrolled 178 CRC patients from the West China Hospital. Clinicopathologic variables were obtained from electronic medical records. The least absolute shrinkage and selection operator (LASSO) and interquartile range analysis were carried out for variable dimensionality reduction and feature selection. Multivariate logistic regression analysis was conducted to develop predictive models of LNM. The performance of the established models was evaluated by the receiver operating characteristic (ROC) curve, calibration belt, and clinical usefulness. Results Based on minimum criteria, 18 potential features were reduced to six predictors by LASSO and interquartile range in the primary cohort. The model demonstrated good discrimination and ROC curve (AUC = 0.721 in the internal test cohort, AUC = 0.758 in the external test cohort) in LNM assessment. Good calibration was shown for the probability of CRC LNM in the internal and external test cohorts. Decision curve analysis illustrated that multi-tumor markers nomogram was clinically useful. Conclusions The study proposed a reliable nomogram that could be efficiently and conveniently utilized to facilitate the assessment of individually-tailored LNM in patients with CRC, complementing imaging and biopsy tests.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic threatening global public health. In the current paper, we describe our successful treatment of three COVID-19 pneumonia patients cases including severe cases and cases with mortality risk factors. One 32-year-old male COVID-19 patient was diagnosed with severe COVID-19 pneumonia and moderate ARDS. The second COVID-19 pneumonia patient had a history of diabetes and chronic bronchitis. The third case of COVID-19 pneumonia was an 82-year old female patient. All three cases had severe COVID pneumonia and therefore were aggressively managed with a multidisciplinary and personalized therapeutic approach that included nutritional support, antiviral pharmacotherapy, active control of comorbidities, prevention of complication development and psychological intervention. Our experience highlights the importance of the use of a multidisciplinary therapeutic approach that tailors to the specific condition of the patient in achieving a favorable clinical outcome.

Abstract Background This study aimed to explore the possibility of serum tumor markers (TMs) combinations in assessing tumor metastasis in patients with lung cancer. Methods We performed a retrospective analysis of 541 patients diagnosed with lung cancer between January 2016 and December 2017 at the Pneumology Department of Dazhou Central Hospital. Serum carcinoembryonic antigen (CEA), carbohydrate antigen (CA)125, CA153, CA199, CA724, cytokeratin 19 fragment (CYFRA), and neuron‐specific enolase (NSE) levels were quantified in each patient at the time of lung cancer diagnosis. Metastasis was confirmed by computed tomography, and/or positron emission tomography, and/or surgery or other necessary methods. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the performance of the model. Results Of the 541 patients eligible for final analysis, 253 were detected with metastasis and 288 were detected without metastasis. Compared with those in nonmetastatic patients, the serum CEA, CA125, CA199, CA153, CYFRA, and NSE levels were notably higher in metastatic patients (P

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness in severe cases. Therefore, this article proposes an algorithm for detecting diabetic retinopathy based on deep ensemble learning and attention mechanism. First, image samples were preprocessed and enhanced to obtain high quality image data. Second, in order to improve the adaptability and accuracy of the detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101, and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning, and attention mechanisms to improve its ability to detect DR. Finally, a weighted voting algorithm was used to determine which category (normal, mild, moderate, severe, or proliferative DR) the images belonged to. We also tuned the trained network model on the hospital data, and the real test samples in the hospital also confirmed the advantages of the algorithm in the detection of the diabetic retina. Experiments show that compared with the traditional single network model detection algorithm, the auc, accuracy, and recall rate of the proposed method are improved to 95, 92, and 92%, respectively, which proves the adaptability and correctness of the proposed method.

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.

ObjectivesFor colorectal cancer patients, traditional biomarker deficient mismatch repair/microsatellite instability (dMMR/MSI) is an accurate predictor of immune checkpoint inhibitors (ICIs). Recent years, researchers considered tumor mutation burden (TMB) as another predictive biomarker which means the number of nonsynonymous mutations in cancer cells. Several studies have proven that TMB can evaluate the efficacy of ICI therapy in diverse types of cancer, especially in non-small cell lung cancer and melanoma. However, studies on the association between TMB and the response to ICI therapy in colorectal cancer alone are still lacking. In this study, we aim to verify the effect of TMB as a biomarker in predicting the efficacy of ICIs in colorectal cancer.MethodsWe searched the PubMed and Ovid MEDLINE databases up to May 1, 2021 and screened studies for eligibility. Thirteen studies published from 2015 to 2021 with 5062 patients were included finally. We extracted and calculated hazard ratios (HRs) and odds ratios (ORs) of overall survival (OS) and objective response rates (ORRs) and their 95% confidence intervals (95% CIs). Pooled HR and OR were evaluated to compare OS and ORR between TMB-high and TMB-low groups in colorectal cancer patients. Meanwhile, we assessed heterogeneity with the I2 statistic and p-values and performed publication bias assessments, sensitivity analyses, and subgroup analyses to search the cause of heterogeneity.ResultsThe TMB-high patient group had a longer OS than the TMB-low patient group (HR = 0.68, 95% CI: 0.51, 0.92, p = 0.013) among colorectal cancer patients receiving ICIs. In addition, the TMB-high patient group was superior in terms of ORR (OR = 19.25, 95% CI: 10.06, 36.82, p < 0.001) compared to the TMB-low patient group.ConclusionsIn conclusion, this meta-analysis revealed that TMB can be used as a potential predictive biomarker of colorectal cancer patients receiving ICI therapy. Nevertheless, this finding is not stable enough. Therefore, many more randomized controlled trials are needed to prove that TMB is reliable enough to be used clinically to predict the efficacy of immunotherapy in colorectal cancer. And the most relevant biomarker remains to be determined when TMB high overlaps with other biomarkers like MSI and TILs.

By developing new mathematical descriptions of 4q (integer q 2) quadrature amplitude modulation (QAM) constellation, a novel construction producing 4q-QAM complementary sequences (CSs) of length 2m (integer m 2) is presented. The proposed sequences include the known 4q-QAM CSs constructed from Cases Ito III constructions, proposed by Li, as special cases. For 16-QAM CSs, the number of the resultant sequences is determined precisely. New sequences have a larger family size so as to increase the code rates. When used in orthogonal frequency-division multiplexing (OFDM) systems, new sequences possess the same peak envelope power (PEP) upper bounds as those of the known sequences referred to above.

We synthesized a series of quinazolinone derivates as tyrosinase inhibitors and evaluated their inhibition constants. We synthesized 2-(2,6-dimethylhepta-1,5-dien-1-yl)quinazolin-4(3H)-one (Q1) from the natural citral. The concentration, which led to 50% activity loss of Q1, was 103 ± 2 μM (IC50 = 103 ± 2 μM). Furthermore, we considered Q1 to be a mixed-type and reversible tyrosinase inhibitor, and determined the KI and KIS inhibition constants to be 117.07 μM and 423.63 μM, respectively. Our fluorescence experiment revealed that Q1 could interact with the substrates of tyrosine and L-DOPA in addition to tyrosinase. Molecular docking studies showed that the binding of Q1 to tyrosinase was driven by hydrogen bonding and hydrophobicity. Briefly, the current study confirmed a new tyrosinase inhibitor, which is expected to be developed into a novel pigmentation drug.

Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

Background: Lupus enteritis (LE), a main cause of acute abdominal pain in systemic lupus erythematosus (SLE) patients, is a serious and potentially fatal complication. This study aimed to identify clinical serological indicators to establish a nomogram to assess LE in SLE patients with gastrointestinal manifestations. Methods: The clinical and laboratory data of SLE patients with gastrointestinal manifestations that were hospitalized in the West China Hospital from January 2010 to January 2020 were retrospectively analyzed. The least absolute shrinkage and selection operator logistic regression model was used to select potentially relevant features. Subsequently, a nomogram was developed using multivariable logistic analysis. The performance of the nomogram was evaluated using a receiver operating characteristic curve, a calibration curve, and decision curve analysis (DCA). Findings: We included a total of 8,505 SLE patients, of which 251 had experienced gastrointestinal manifestations. The patients were randomly divided into training (n = 176) and validation (n = 75) groups. The LRA (LE Risk Assessment) model consisted of 11 significantly associated variables, which included complement 4, antineutrophil cytoplasmic antibody, albumin, anion gap, age, d-dimer, platelet, serum chlorine, anti-Sjögren's-syndrome-related antigen A, anti-ribosomal P protein, and anti-ribonucleoprotein. In the training and validation cohorts, the areas under the curve were 0.919 (95% confidence interval [CI]: 0.876–0.962) and 0.870 (95% CI: 0.775–0.964), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: The LRA model exhibits good predictive ability in assessing LE risk in SLE patients with gastrointestinal manifestations.

ObjectiveImmediate early response 5 (IER5) plays a core role in cell cycle and response to irradiation. However, its role in glioma remains unclear. We aimed to evaluate its prognostic significance in glioma based on The Cancer Genome Atlas data resource.MethodsThe Kruskal–Wallis test, Wilcoxon signed-rank test, and logistic regression were employed to explore the relationship between IER5 expression and clinicopathological features. Kaplan–Meier and Cox regression analyses were implemented to investigate the relationship of IER5 with prognosis. A nomogram to estimate the impact of IER5 on prognosis was created based on the Cox multivariate data. We performed gene set enrichment analysis (GSEA) to determine the key signaling cascades associated with IER5. Immunohistochemistry was performed to examine IER5 expression in a tissue microarray (TMA) of glioma samples.ResultsImmediate early response 5 gene expression was elevated in glioma patients. The level of IER5 was significantly correlated with WHO grade [OR = 6.71 (4.34–10.68) for G4 vs. G2 and G3], IDH (isocitrate dehydrogenase enzyme) status [OR = 13.35 (8.92–20.46) for wild-type (WT) vs. mutated (Mut)], epidermal growth factor receptor status [OR = 8.42 (4.32–18.43) for Mut vs. WT], age [OR = 0.27 (0.18–0.41) for ≤ 60 years vs. >60 years], and histological type [OR = 7.13 (4.63–11.31] for glioblastoma vs. astrocytoma, oligoastrocytoma, and oligodendroglioma). Univariate analyses revealed that high IER5 expression was linked to short overall survival (OS) [hazard ratio (HR): 3.747; 95% confidence interval (CI): 2.847–4.933; and P < 0.001]. High IER5 expression was linked to poor OS in multivariate analyses (HR: 2.474; 95% CI: 1.552–3.943; and P < 0.001). TMA results showed that high IER5 protein levels were related to short OS (HR: 1.84; 95% CI: 1.10–3.07; and P = 0.021) and poor disease-specific survival (HR: 1.82; 95% CI: 1.09–3.04; and P = 0.023). GSEA showed that many tumor related pathways were enriched differentially in the IER5-high expression group. The C-index and calibration plots of the nomogram showed an effective estimation performance in glioma patients.ConclusionHerein, we established that IER5 plays a critical role in glioma progression and prognosis, which might be an important biomarker for the prognosis of glioma patients.

Objectives: To evaluate whether incorporating the radiomics, genomics, and clinical features allows prediction of metastasis in colorectal cancer (CRC) and to develop a preoperative nomogram for predicting metastasis.Methods: We retrospectively analyzed radiomics features of computed tomography (CT) images in 134 patients (62 in the primary cohort, 28 in the validation cohort, and 44 in the independent-test cohort) clinicopathologically diagnosed with CRC at Dazhou Central Hospital from February 2018 to October 2019. Tumor tissues were collected from all patients for RNA sequencing, and clinical data were obtained from medical records. A total of 854 radiomics features were extracted from enhanced venous-phase CT of CRC. Least absolute shrinkage and selection operator regression analysis was utilized for data dimension reduction, feature screen, and radiomics signature development. Multivariable logistic regression analysis was performed to build a multiscale predicting model incorporating the radiomics, genomics, and clinical features. The receiver operating characteristic curve, calibration curve, and decision curve were conducted to evaluate the performance of the nomogram.Results: The radiomics signature based on 16 selected radiomics features showed good performance in metastasis assessment in both primary [area under the curve (AUC) = 0.945, 95% confidence interval (CI) 0.892–0.998] and validation cohorts (AUC = 0.754, 95% CI 0.570–0.938). The multiscale nomogram model contained radiomics features signatures, four-gene expression related to cell cycle pathway, and CA 19-9 level. The multiscale model showed good discrimination performance in the primary cohort (AUC = 0.981, 95% CI 0.953–1.000), the validation cohort (AUC = 0.822, 95% CI 0.635–1.000), and the independent-test cohort (AUC = 0.752, 95% CI 0.608–0.896) and good calibration. Decision curve analysis confirmed the clinical application value of the multiscale model.Conclusion: This study presented a multiscale model that incorporated the radiological eigenvalues, genomics features, and CA 19-9, which could be conveniently utilized to facilitate the individualized preoperatively assessing metastasis in CRC patients.

IntroductionWorldwide, the incidence and mortality of lung cancer are at the highest levels, and the most lesions are located in the lung periphery. Despite extensive screening and diagnosis, the pathologic types of peripheral pulmonary lesions (PPLs) are difficult to diagnose by noninvasive examination. This study aimed to identify a novel index—time difference of arrival (TDOA)—to discriminate between benign inflammation and malignant PPLs.MethodsUsing contrast-enhanced ultrasound (CEUS), we retrospectively analyzed 96 patients with PPLs who had undergone biopsy to confirm the pathologic types. All data were collected from Dazhou Central Hospital between December 2012 and July 2019. The parameters of CEUS were analyzed by two assistant chief physicians of ultrasound diagnosis. Area under the receiver operating characteristic curve analysis, sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the diagnostic ability of different indices.ResultsWe found that the TDOA significantly distinguished benign inflammation from malignant lesions. The TDOA was markedly increased in patients with malignant lesions than benign inflammation lesions (P < 0.001). Compared with conventional time-intensity curve (TIC) indices, TDOA showed high diagnostic accuracy (area under the curve = 0.894). Moreover, conventional diagnostic indices did not affect the diagnostic performance of TDOA by adjusting the receiver operating characteristic curve.ConclusionTDOA is feasible for the diagnosis of benign inflammation and malignant PPLs.

Introduction: Prognosis prediction is essential to improve therapeutic strategies and to achieve better clinical outcomes in colorectal cancer (CRC) patients. Radiomics based on high-throughput mining of quantitative medical imaging is an emerging field in recent years. However, the relationship among prognosis, radiomics features, and gene expression remains unknown.Methods: We retrospectively analyzed 141 patients (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training (N = 99) and testing (N = 42) cohorts. Radiomics features in venous phase image were extracted from preoperative computed tomography (CT) images. Gene expression was detected by RNA-sequencing on tumor tissues. The least absolute shrinkage and selection operator (LASSO) regression model was used for selecting imaging features and building the radiomics model. A total of 45 CRC patients (study 2) with immunohistochemical (IHC) staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. A clinical model was validated for prognosis prediction in prognostic testing cohort (163 CRC patients from 2014 to 2018, study 3). We performed a combined radiomics model that was composed of radiomics score, tumor stage, and CXCL8-derived radiomics model to make comparison with the clinical model.Results: In our study, we identified the CXCL8 as a hub gene in affecting prognosis, which is mainly through regulating cytokine–cytokine receptor interaction and neutrophil migration pathway. The radiomics model incorporated 12 radiomics features screened by LASSO according to CXCL8 expression in the training cohort and showed good performance in testing and IHC testing cohorts. Finally, the CXCL8-derived radiomics model combined with tumor stage performed high ability in predicting the prognosis of CRC patients in the prognostic testing cohort, with an area under the curve (AUC) of 0.774 [95% confidence interval (CI): 0.674–0.874]. Kaplan–Meier analysis of the overall survival probability in CRC patients stratified by combined model revealed that high-risk patients have a poor prognosis compared with low-risk patients (Log-rank P < 0.0001).Conclusion: We demonstrated that the radiomics model reflected by CXCL8 combined with tumor stage information is a reliable approach to predict the prognosis in CRC patients and has a potential ability in assisting clinical decision-making.

Background: The aim of this study is to evaluate the clinical efficacy of vitamin D (VitD) supplementation in terms of response to treatment and improvement of disease activity in rheumatoid arthritis (RA).Methods: This study analyzed 1180 RA patients' records treated at Mianyang Central Hospital from February 2015 to July 2019. The patients were allocated into VitD group and control group based on their medical regimens. The outcome measures were primary efficacy, defined as treatment response-based EULAR response criteria in RA, and secondary efficacy, defined as improvement in disease activity indicators. Safety was evaluated according to the incidence of all-cause infections.Results: At month 6, the primary efficacy revealed that there were 22.8% good responders and 19.0% moderate responders in the VitD group, and 22.3% good responders and 22.3% moderate responders in the control group; there were no differences between the two groups (p = 0.754). The similar primary efficacy outcomes were observed at months 3, 12, and >12. The secondary efficacy indicated that there were no differences in most indexes between the two groups at months 1, 3, 6, 12, and >12. The subgroups (based on baseline DAS28 (CRP), glucocorticoids use and disease duration) analysis results suggested that VitD group didn't have the advantage for treating RA. The incidence of infections was similar in the two groups.Conclusion: VitD supplementation did not provide additional benefit for anti-rheumatic treatment. These data supported the need for prospective, randomized, controlled trials to evaluate the role of VitD supplementation in treating RA.

C-X-C motif chemokine ligand 8 (CXCL8) is involved in tumor proliferation, migration, and invasion. However, the function of CXCL8 in colorectal cancer (CRC) is controversial. Here, we analyzed RNA-sequencing (RNA-seq) data to identify differentially expressed genes and pathways according to gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with CRC. The levels of the mRNA encoding CXCL8 were significantly increased in early and advanced stages of CRC, as well as in metastases and nonmetastasis cases using RNA-seq analysis (n = 91). These findings were consistent with immunohistochemical analysis of CXCL8 expression (n = 87). Protein-protein interaction (PPI) prediction combined with transcriptional profiling data revealed that CXCL8 levels positively correlated with cAMP responsive element binding protein 1 (CREB1)/ribosomal protein S6 kinase B1 (RPS6KB1) expression, which promotes cell proliferation and differentiation in high expression, while inversely correlated with the expression of Bcl2 associated agonist of cell death (BAD) protein to inhibit apoptosis during the progression of CRC. These findings provide compelling clinical and molecular evidence to support the conclusion that CXCL8 contributes to the genesis and progression of CRC.

Coumarin possesses the aromatic group and showed plentiful activities, such as antioxidant, preventing asthma and antisepsis. In addition, coumarin derivatives usually possess good solubility, low cytotoxicity and excellent cell permeability. In our study, we synthesized the compound bridge methylene tacrine (BMT), which has the classical pharmacophore structure of Tacrine (THA). Based on the principle of active substructure splicing, BMT was used as a lead compound and synthesized coumarin–BMT hybrids by introducing coumarin to BMT. In this work, 21 novel hybrids of BMT and coumarin were synthesized and evaluated for their inhibitory activity on AChE. All obtained compounds present preferable inhibition. Compound 8b was the most active compound, with the value of Ki as 49.2 nM, which was higher than Galantamine (GAL) and lower than THA. The result of molecular docking showed that the highest binding free energy was −40.43 kcal/mol for compound 8b, which was an identical trend with the calculated Ki.

Hyperinsulinemia can precede the development of insulin resistance. Here the authors identify a PKD2 mutation that leads to hyperinsulinemia and insulin resistance in Rhesus monkey and show that PKD2 deficiency promotes beta cell insulin secretion by activating L-type Ca2+ channels.

Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.

Background Carcinoembryonic antigen (CEA) as a diagnostic or prognostic marker has been widely studied in patients with lung cancer. However, the relationship between serum CEA and tumor metastasis in lung cancer remains controversial. This study aimed to investigate the ability of serum CEA to assess tumor metastasis in lung cancer patients. Methods We performed a retrospective analysis of 238 patients diagnosed with lung cancer from January to December 2016 at pneumology department of Dazhou Central Hospital (Dazhou, China). Serum CEA levels were quantified in each patient at the time of diagnosis of lung cancer. Metastasis was confirmed by computed tomography (CT), and/or positron emission tomography (PET) and/or surgery or other necessary detecting methods. Results Of the 213 patients eligible for final analysis, 128 were diagnosed with metastasis and 85 were diagnosed without metastasis. Compared to non-metastatic patients, the serum CEA was markedly higher in patients with metastasis (p

To obtain α-glucosidase inhibitors with high activity, 19 NB-DNJDs (N-benzyl-deoxynojirimycin derivatives) were designed and synthesized. The results indicated that the 19 NB-DNJDs displayed different inhibitory activities towards α-glucosidase in vitro. Compound 18a (1-(4-hydroxy-3-methoxybenzyl)-2-(hydroxymethyl) piperidine-3,4,5-triol) showed the highest activity, with an IC50 value of 0.207 ± 0.11 mM, followed by 18b (1-(3-bromo-4-hydroxy-5-methoxybenzyl)-2-(hydroxymethyl) piperidine-3,4,5-triol, IC50: 0.276 ± 0.13 mM). Both IC50 values of 18a and 18b were significantly lower than that of acarbose (IC50: 0.353 ± 0.09 mM). According to the structure-activity analysis, substitution of the benzyl and bromine groups on the benzene ring decreased the inhibition activity, while methoxy and hydroxyl group substitution increased the activity, especially with the hydroxyl group substitution. Molecular docking results showed that three hydrogen bonds were formed between compound 18a and amino acids in the active site of α-glucosidase. Additionally, an arene−arene interaction was also modelled between the phenyl ring of compound 18a and Arg 315. The three hydrogen bonds and the arene−arene interaction resulted in a low binding energy (−5.8 kcal/mol) and gave 18a a higher inhibition activity. Consequently, compound 18a is a promising candidate as a new α-glucosidase inhibitor for the treatment of type Ⅱ diabetes.

Methods of constructing Z-complementary sequence sets are described. Different from those constructions using complementary sequences as a kernel, the proposed constructions are based on sequences with periodic and aperiodic zero-correlation zone (PAZCZ). These PAZCZ sequences are unitary sequences, not complementary sequences. By means of interleaving iteration and orthogonal matrix expansion of PAZCZ sequences, desirable bivalued zero cross-correlation zone (ZCCZ) characteristics can be obtained. Compared with Z-complementary sequence sets with single value ZCCZ which is generally equal to the length of an element sequence, the proposed Z-complementary sequence sets with bivalued ZCCZ have larger set size and then can support more users.