Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.

BACKGROUND: Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients. METHODS: A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) were analyzed. We applied five ML algorithms-logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and neural network (NN)-to develop the models. The area under the receiver operating characteristic (AUC-ROC) curve, calibration curve, and variable importance were used to evaluate the models' performance. RESULTS: During a median follow-up of 45.5 months, 179 patients (8.0%) experienced structural recurrence. The non-stimulated thyroglobulin, LN dissection, number of LNs dissected, lymph node metastasis ratio, N stage, comorbidity of hypertension, comorbidity of diabetes, body mass index, and low-density lipoprotein were used to develop the models. All models showed a greater AUC (AUC = 0.738 to 0.767) than did the ATA risk stratification (AUC = 0.620, DeLong test: P < 0.01). The SVM, XGBoost, and RF model showed greater sensitivity (0.568, 0.595, 0.676), specificity (0.903, 0.857, 0.784), accuracy (0.875, 0.835, 0.775), positive predictive value (PPV) (0.344, 0.272, 0.219), negative predictive value (NPV) (0.959, 0.959, 0.964), and F1 score (0.429, 0.373, 0.331) than did the ATA risk stratification (sensitivity = 0.432, specificity = 0.770, accuracy = 0.742, PPV = 0.144, NPV = 0.938, F1 score = 0.216). The RF model had generally consistent calibration compared with the other models. The Tg and the LNR were the top 2 important variables in all the models, the N stage was the top 5 important variables in all the models. CONCLUSIONS: The RF model achieved the expected prediction performance with generally good discrimination, calibration and interpretability in this study. This study sheds light on the potential of ML approaches for improving the accuracy of risk stratification for PTC patients. TRIAL REGISTRATION: Retrospectively registered at www.chictr.org.cn (trial registration number: ChiCTR2300075574, date of registration: 2023-09-08).

The newly proposed plasma-glycosylated hemoglobin A1c/High-Density lipoprotein cholesterol ratio serves as a simple and practical indicator for screening metabolic associated fatty liver disease: an observational study based on a physical examination population.

BACKGROUND: Glycotoxicity and lipotoxicity are key pathophysiological mechanisms underlying the development of metabolic associated fatty liver disease (MAFLD). The primary objective of this study is to investigate the association between the newly proposed Plasma-Glycosylated Hemoglobin A1c/High-Density Lipoprotein Cholesterol Ratio (HbA1c/HDL-C ratio) and the risk of MAFLD. METHODS: A study population of 14,251 individuals undergoing health examinations was included. The association between the HbA1c/HDL-C ratio and MAFLD was analyzed using multivariable logistic regression and restricted cubic spline (RCS) analysis. Exploratory analyses were conducted to assess variations in this association across subgroups stratified by gender, age, body mass index (BMI), exercise habits, drinking status, and smoking status. The discriminatory value of the HbA1c/HDL-C ratio and its components for screening MAFLD was evaluated using receiver operating characteristic (ROC) curves. RESULTS: A total of 1,982 (13.91%) subjects were diagnosed with MAFLD. After adjusting for confounding factors, we found a significant positive association between the HbA1c/HDL-C ratio and MAFLD [odds ratio (OR) 1.34, 95% confidence interval (CI): 1.25, 1.44]. No significant differences in this association were observed across all subgroups (All P for interaction > 0.05). Furthermore, through RCS analysis, we observed a nonlinear positive correlation between the HbA1c/HDL-C ratio and MAFLD (P for non-linearity < 0.001), with a potential threshold effect point (approximately 3 for the HbA1c/HDL-C ratio). Beyond this threshold point, the slope of the MAFLD prevalence curve increased rapidly. Additionally, in further ROC analysis, we found that for the identification of MAFLD, the HbA1c/HDL-C ratio was significantly superior to HbA1c and HDL-C, with an area under the curve (AUC) and optimal threshold of 0.81 and 4.08, respectively. CONCLUSIONS: Our findings suggest that the newly proposed HbA1c/HDL-C ratio serves as a simple and practical indicator for assessing MAFLD, exhibiting well-discriminatory performance in screening for MAFLD.

Prognostic value of geriatric nutritional risk index in patients with stable coronary artery disease undergoing percutaneous coronary intervention.

BACKGROUND: Malnutrition increases the risk of poor prognosis in patients with cardiovascular disease, and our current research was designed to assess the predictive performance of the Geriatric Nutrition Risk Index (GNRI) for the occurrence of poor prognosis after percutaneous coronary intervention (PCI) in patients with stable coronary artery disease (SCAD) and to explore possible thresholds for nutritional intervention. METHODS: This study retrospectively enrolled newly diagnosed SCAD patients treated with elective PCI from 2014 to 2017 at Shinonoi General Hospital, with all-cause death as the main follow-up endpoint. Cox regression analysis and restricted cubic spline (RCS) regression analysis were used to explore the association of GNRI with all-cause death risk and its shape. Receiver operating characteristic curve (ROC) analysis and piecewise linear regression analysis were used to evaluate the predictive performance of GNRI level at admission on all-cause death in SCAD patients after PCI and to explore possible nutritional intervention threshold points. RESULTS: The incidence of all-cause death was 40.47/1000 person-years after a mean follow-up of 2.18 years for 204 subjects. Kaplan-Meier curves revealed that subjects at risk of malnutrition had a higher all-cause death risk. In multivariate Cox regression analysis, each unit increase in GNRI reduced the all-cause death risk by 14% (HR 0.86, 95% CI 0.77, 0.95), and subjects in the GNRI > 98 group had a significantly lower risk of death compared to those in the GNRI < 98 group (HR 0.04, 95% CI 0.00, 0.89). ROC analysis showed that the baseline GNRI had a very high predictive performance for all-cause death (AUC = 0.8844), and the predictive threshold was 98.62; additionally, in the RCS regression analysis and piecewise linear regression analysis we found that the threshold point for the GNRI-related all-cause death risk was 98.28 and the risk will be significantly reduced when the subjects' baseline GNRI was greater than 98.28. CONCLUSIONS: GNRI level at admission was an independent predictor of all-cause death in SCAD patients after PCI, and GNRI equal to 98.28 may be a useful threshold for nutritional intervention in SCAD patients treated with PCI.

Relative importance of triglyceride glucose index combined with body mass index in predicting recovery from prediabetic state to normal fasting glucose: a cohort analysis based on a Chinese physical examination population.

BACKGROUND: Prediabetes is a high-risk state for diabetes, and numerous studies have shown that the body mass index (BMI) and triglyceride-glucose (TyG) index play significant roles in risk prediction for blood glucose metabolism. This study aims to evaluate the relative importance of BMI combination with TyG index (TyG-BMI) in predicting the recovery from prediabetic status to normal blood glucose levels. METHODS: A total of 25,397 prediabetic subjects recruited from 32 regions across China. Normal fasting glucose (NFG), prediabetes, and diabetes were defined referring to the American Diabetes Association (ADA) criteria. After normalizing the independent variables, the impact of TyG-BMI on the recovery or progression of prediabetes was analyzed through the Cox regression models. Receiver Operating Characteristic (ROC) curve analysis was utilized to visualize and compare the predictive value of TyG-BMI and its constituent components in prediabetes recovery/progression. RESULTS: During the average observation period of 2.96 years, 10,305 individuals (40.58%) remained in the prediabetic state, 11,278 individuals (44.41%) recovered to NFG, and 3,814 individuals (15.02%) progressed to diabetes. The results of multivariate Cox regression analysis demonstrated that TyG-BMI was negatively associated with recovery from prediabetes to NFG and positively associated with progression from prediabetes to diabetes. Further ROC analysis revealed that TyG-BMI had higher impact and predictive value in predicting prediabetes recovering to NFG or progressing to diabetes in comparison to the TyG index and BMI. Specifically, the TyG-BMI threshold for predicting prediabetes recovery was 214.68, while the threshold for predicting prediabetes progression was 220.27. Additionally, there were significant differences in the relationship of TyG-BMI with prediabetes recovering to NFG or progressing to diabetes within age subgroups. In summary, TyG-BMI is more suitable for assessing prediabetes recovery or progression in younger populations (< 45 years old). CONCLUSIONS: This study, for the first time, has revealed the significant impact and predictive value of the TyG index in combination with BMI on the recovery from prediabetic status to normal blood glucose levels. From the perspective of prediabetes intervention, maintaining TyG-BMI within the threshold of 214.68 holds crucial significance.

Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.

The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance]. / 从CTå›¾åƒä¸­æ£€æµ‹æ–°åž‹å† çŠ¶ç— æ¯’æ„ŸæŸ“å¯¼è‡´çš„è‚ºç‚Žï¼šä¸€ç§ç»†èŠ‚ä¸Šé‡‡æ ·å’Œæ³¨æ„åŠ›å¼•å¯¼çš„æ·±åº¦å­¦ä¹ æ–¹æ³•.

Objective: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. Methods: We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Results: Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. Conclusion: The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

PredictProtein - Predicting Protein Structure and Function for 29 Years.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

Mechanical Force Induced Self-Assembly of Chinese Herbal Hydrogel with Synergistic Effects of Antibacterial Activity and Immune Regulation for Wound Healing.

Skin wounds, especially infected chronic wounds, have attracted worldwide attention due to the high prevalence and poor treatment outcomes. Hydrogel dressings with antibacterial ability and immune regulation property are urgently required. Herein, inspired by the grinding treatment of traditional Chinese medicine, mechanical force is introduced to promote the effective molecular collision and accelerate the self-assembly of chitosan (CS) and puerarin (PUE) for fabricating Chinese-herb-based hydrogels. The antibacterial rate of CS@PUE (C@P) hydrogel is more than 95%, and the wound closed rate is twice that of the control group. Interestingly, the rational design of C@P hydrogels with different PUE ratios enables a refined control over hydrogel formation, nanofiber appearance, viscoelastic, physicochemical, and biological properties. The extraordinary antibacterial ability of C@P hydrogels may originate from the nanofiber structure and the improved zeta potential on account of the orientation of amino groups in CS . Thus, the synergistically antibacterial and immune regulation properties of C@P hydrogels kill bacteria and relieve inflammation in the wound bed, ensuring the anti-infection effect, and boosting wound healing. In addition to providing a universal mechanosynthesis of PUE-based hydrogel for wound healing, this finding is expected to increase the attention paid to Chinese herbal medicines in the construction of biomaterials.

RNA editing regulates lncRNA splicing in human early embryo development.

RNA editing is a co- or post-transcriptional modification through which some cells can make discrete changes to specific nucleotide sequences within an RNA molecule after transcription. Previous studies found that RNA editing may be critically involved in cancer and aging. However, the function of RNA editing in human early embryo development is still unclear. In this study, through analyzing single cell RNA sequencing data, 36.7% RNA editing sites were found to have a have differential editing ratio among early embryo developmental stages, and there was a great reprogramming of RNA editing rates at the 8-cell stage, at which most of the differentially edited RNA editing sites (99.2%) had a decreased RNA editing rate. In addition, RNA editing was more likely to occur on RNA splicing sites during human early embryo development. Furthermore, long non-coding RNA (lncRNA) editing sites were found more likely to be on RNA splicing sites (odds ratio = 2.19, P = 1.37×10-8), while mRNA editing sites were less likely (odds ratio = 0.22, P = 8.38×10-46). Besides, we found that the RNA editing rate on lncRNA had a significantly higher correlation coefficient with the percentage spliced index (PSI) of lncRNA exons (R = 0.75, P = 4.90×10-16), which indicated that RNA editing may regulate lncRNA splicing during human early embryo development. Finally, functional analysis revealed that those RNA editing-regulated lncRNAs were enriched in signal transduction, the regulation of transcript expression, and the transmembrane transport of mitochondrial calcium ion. Overall, our study might provide a new insight into the mechanism of RNA editing on lncRNAs in human developmental biology and common birth defects.

Abdominal obesity phenotypes are associated with the risk of developing non-alcoholic fatty liver disease: insights from the general population.

BACKGROUND: The diversity of obesity-related metabolic characteristics generates different obesity phenotypes and corresponding metabolic diseases. This study aims to explore the correlation of different abdominal obesity phenotypes with non-alcoholic fatty liver disease (NAFLD). METHODS: The current study included 14,251 subjects, 7411 males and 6840 females. Abdominal obesity was defined as waist circumference ≥ 85 cm in males and ≥ 80 cm in females; according to the diagnostic criteria for metabolic syndrome recommended by the National Cholesterol Education Program Adult Treatment Panel III, having more than one metabolic abnormality (except waist circumference criteria) was defined as metabolically unhealthy. All subjects were divided into 4 abdominal obesity phenotypes based on the presence ( +) or absence (- ) of metabolically healthy/unhealthy (MH) and abdominal obesity (AO) at baseline: metabolically healthy + non-abdominal obesity (MH-AO-); metabolically healthy + abdominal obesity (MH-AO+); metabolically unhealthy + non-abdominal obesity (MH+AO-); metabolically unhealthy + abdominal obesity (MH+AO+). The relationship between each phenotype and NAFLD was analyzed using multivariate logistic regression. RESULTS: A total of 2507 (17.59%) subjects in this study were diagnosed with NAFLD. The prevalence rates of NAFLD in female subjects with MH-AO-, MH-AO+, MH+AO-, and MH+AO+ phenotypes were 1.73%, 24.42%, 7.60%, and 59.35%, respectively. Among male subjects with MH-AO-, MH-AO+, MH+AO-, and MH+AO+ phenotypes, the prevalence rates were 9.93%, 50.54%, 25.49%, and 73.22%, respectively. After fully adjusting for confounding factors, with the MH-AO- phenotype as the reference phenotype, male MH-AO+ and MH+AO+ phenotypes increased the risk of NAFLD by 42% and 47%, respectively (MH-AO+: OR 1.42, 95%CI 1.13,1.78; MH+AO+: OR 1.47, 95%CI 1.08,2.01); the corresponding risks of MH-AO+ and MH+AO+ in females increased by 113% and 134%, respectively (MH-AO+: OR 2.13, 95%CI 1.47,3.09; MH+AO+: OR 2.34, 95%CI 1.32,4.17); by contrast, there was no significant increase in the risk of NAFLD in the MH+AO- phenotype in both sexes. CONCLUSIONS: This first report on the relationship of abdominal obesity phenotypes with NAFLD showed that both MH-AO+ and MH+AO+ phenotypes were associated with a higher risk of NAFLD, especially in the female population. These data provided a new reference for the screening and prevention of NAFLD.

Association of LDL:HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults.

BACKGROUND: Low-density lipoprotein:high-density lipoprotein cholesterol ratio (LDL:HDL ratio) has a good performance in identifying diabetes mellitus (DM) and insulin resistance. However, it is not yet clear whether the LDL:HDL ratio is associated with a high-risk state of prediabetes. METHODS: This cohort study retrospectively analyzed the data of 100,309 Chinese adults with normoglycemia at baseline. The outcome event of interest was new-onset prediabetes. Using multivariate Cox regression and smoothing splines to assess the association of LDL:HDL ratio with prediabetes. RESULTS: During an average observation period of 37.4 months, 12,352 (12.31%) subjects were newly diagnosed with prediabetes. After adequate adjustment for important risk factors, the LDL:HDL ratio was positively correlated with the prediabetes risk, and the sensitivity analysis further suggested the robustness of the results. Additionally, in stratified analysis, we discovered significant interactions between LDL:HDL ratio and family history of DM, sex, body mass index and age (all P-interaction < 0.05); among them, the LDL:HDL ratio-related prediabetes risk decreased with the growth of body mass index and age, and increased significantly in women and people with a family history of DM. CONCLUSIONS: The increased LDL:HDL ratio in the Chinese population indicates an increased risk of developing prediabetes, especially in women, those with a family history of DM, younger adults, and non-obese individuals.

Genome-wide hypermethylation is closely associated with abnormal expression of genes involved in neural development in induced pluripotent stem cells derived from a Down syndrome mouse model.

Mental retardation is the main clinical manifestation of Down syndrome (DS), and neural abnormalities occur during the early embryonic period and continue throughout life. Tc1, a model mouse for DS, carries the majority part of the human chromosome 21 and has multiple neuropathy phenotypes similar to patients with DS. To explore the mechanism of early neural abnormalities of Tc1 mouse, induced pluripotent stem (iPS) cells from Tc1 mice were obtained, and genome-wide gene expression and methylation analysis were performed for Tc1 and wild-type iPS cells. Our results showed hypermethylation profiles for Tc1 iPS cells, and the abnormal genes were shown to be related to neurodevelopment and distributed on multiple chromosomes. In addition, important genes involved in neurogenesis and neurodevelopment were shown to be downregulated in Tc1 iPS cells. In short, our study indicated that genome-wide hypermethylation leads to the disordered expression of genes associated with neurodevelopment in Tc1 mice during early development. Overall, our work provided a useful reference for the study of the molecular mechanism of nervous system abnormalities in DS.

Protein-protein and protein-nucleic acid binding residues important for common and rare sequence variants in human.

BACKGROUND: Any two unrelated people differ by about 20,000 missense mutations (also referred to as SAVs: Single Amino acid Variants or missense SNV). Many SAVs have been predicted to strongly affect molecular protein function. Common SAVs (> 5% of population) were predicted to have, on average, more effect on molecular protein function than rare SAVs (< 1% of population). We hypothesized that the prevalence of effect in common over rare SAVs might partially be caused by common SAVs more often occurring at interfaces of proteins with other proteins, DNA, or RNA, thereby creating subgroup-specific phenotypes. We analyzed SAVs from 60,706 people through the lens of two prediction methods, one (SNAP2) predicting the effects of SAVs on molecular protein function, the other (ProNA2020) predicting residues in DNA-, RNA- and protein-binding interfaces. RESULTS: Three results stood out. Firstly, SAVs predicted to occur at binding interfaces were predicted to more likely affect molecular function than those predicted as not binding (p value < 2.2 × 10-16). Secondly, for SAVs predicted to occur at binding interfaces, common SAVs were predicted more strongly with effect on protein function than rare SAVs (p value < 2.2 × 10-16). Restriction to SAVs with experimental annotations confirmed all results, although the resulting subsets were too small to establish statistical significance for any result. Thirdly, the fraction of SAVs predicted at binding interfaces differed significantly between tissues, e.g. urinary bladder tissue was found abundant in SAVs predicted at protein-binding interfaces, and reproductive tissues (ovary, testis, vagina, seminal vesicle and endometrium) in SAVs predicted at DNA-binding interfaces. CONCLUSIONS: Overall, the results suggested that residues at protein-, DNA-, and RNA-binding interfaces contributed toward predicting that common SAVs more likely affect molecular function than rare SAVs.

Nanostructural Surfaces with Different Elastic Moduli Regulate the Immune Response by Stretching Macrophages.

A proper immune response is key for the successful implantation of biomaterials, and designing and fabricating biomaterials to regulate immune responses is the future trend. In this work, three different nanostructures were constructed on the surface of titanium using a hydrothermal method, and through a series of in vitro and in vivo experiments, we found that the aspect ratio of nanostructures can affect the elastic modulus of a material surface and further regulate immune cell behaviors. This work demonstrates that nanostructures with a higher aspect ratio can endow a material surface with a lower elastic modulus, which was confirmed by experiments and theoretical analyses. The deflection of nanostructures under the cell adsorption force is a substantial factor in stretching macrophages to enhance cell adhesion and spreading, further inducing macrophage polarization toward the M1 phenotype and leading to intense immune responses. In contrast, a nanostructure with a lower aspect ratio on a material surface leads to a higher surface elastic modulus, making deflection of the material difficult and creating a surface that is not conducive to macrophage adhesion and spreading, thus reducing the immune response. Moreover, molecular biology experiments indicated that regulation of the immune response by the elastic modulus is primarily related to the NF-κB signaling pathway. These findings suggest that the immune response can be regulated by constructing nanostructural surfaces with the proper elastic modulus through their influence on cell adhesion and spreading, which provides new insights into the surface design of biomaterials.

A radiomics signature to identify malignant and benign liver tumors on plain CT images.

BACKGROUND: In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE: This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS: We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signature was developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS: Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than Rα = 0.3703 (the critical value of R at significant level α = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS: The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians' diagnostic abilities and play an important role in RCADs.

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis.

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.

Achieving stem cell imaging and osteogenic differentiation by using nitrogen doped graphene quantum dots.

Nitrogen doped graphene quantum dots (N-GQDs) were synthesized to explore and extend their potential applications in biomedical field. The hemocompatibility and cytotoxity of the obtained N-GQDs were primarily assessed at concentrations ranging from 10 to 100 µg/ml. From the results, it was found that the proliferation of rat Bone Mesenchymal Stem Cells (rBMSCs) was depressed to a certain extent after incubating with the high concentration (100 µg/ml) of N-GQDs. The nanoscale size and superior dispersibility endow N-GQDs with good cell permeability. Meanwhile, owing to their intrinsic photoluminescence characteristic, the N-GQDs can be used to label cells with high uniformity and light stability in absence of chemical dyes. More importantly, the up-regulated expression of alkaline phosphate (ALP), extracellular matrix, osteopontin (OPN) and osteocalcin (OCN) in rBMSCs cultured with N-GQDs, indicating N-GQDs have the abilities to promote rBMSCs osteogenic differentiation. This work would help give a new insight into the advantages of N-GQDs and pave the way for application of N-GQDs in regenerative medicine fields.

Long non-coding RNA MALAT1 protects preterm infants with bronchopulmonary dysplasia by inhibiting cell apoptosis.

BACKGROUND: Bronchopulmonary dysplasia (BPD) is a neonatal chronic lung disease characterized by impaired pulmonary alveolar development in preterm infants. Until now, little is known about the molecular and cellular basis of BPD. There is increasing evidence that lncRNAs regulate cell proliferation and apoptosis during lung organogenesis. The potential role of lncRNAs in the pathogenesis of BPD is unclear. This study aims to clarify the role of MALAT1 during the process of BPD in preterm infants and illustrate the protective effect of MALAT1 involved in preterm infants. METHODS: We assessed the expression of MALAT1 in BPD mice lung tissues by reanalyzing dataset GSE25286 (Mouse GEO Genome 4302 Array) from gene expression database gene expression omnibus (GEO), and verified MALAT1 expression in BPD patients by realtime q-PCR. Then the role of MALAT1 in regulating cell biology was examined by profiling dataset GSE43830. The expression of CDC6, a known antiapoptopic gene was verified in BPD patients and the alveolar epithelial cell line A549 cells in which MALAT1 was knocked down. Cell apoptosis was determined by FACS using PI/Annexin-V staining. RESULTS: The expression of MALAT1 was significantly evaluated in lung tissues of BPD mice at day 14 and day 29 compared to WT (P < 0.05). In consistent with mRNA array profiling analysis, MALAT1 expression level in blood samples from preterm infants with BPD was significantly increased. Bioinformative data analysis of MALAT1 knockdown in WI-38 cells showed various differentially expressed genes were found enriched in apoptosis related pathway. Down-regulation of antiapoptopic gene, CDC6 expression was further verified by q-PCR result. PI/Annexin-V apoptisis assay results showed that MALAT1 knocked down in the alveolar epithelial cell line (A549) promotes cell apoptosis. CONCLUSIONS: In our study, we found that up-regulation of lncRNA MALAT1 could protect preterm infants with BPD by inhibiting cell apoptosis. These data provide novel insights into MALAT1 regulation which may be relevant to cell fate and shed light on BPD prevention and treatment.

Long non-coding RNA LINC01296 is a potential prognostic biomarker in patients with colorectal cancer.

Colorectal cancer (CRC), one of the most malignant cancers, is currently the fourth leading cause of cancer deaths worldwide. Recent studies indicated that long non-coding RNAs (lncRNAs) could be robust molecular prognostic biomarkers that can refine the conventional tumor-node-metastasis staging system to predict the outcomes of CRC patients. In this study, the lncRNA expression profiles were analyzed in five datasets (GSE24549, GSE24550, GSE35834, GSE50421, and GSE31737) by probe set reannotation and an lncRNA classification pipeline. Twenty-five lncRNAs were differentially expressed between CRC tissue and tumor-adjacent normal tissue samples. In these 25 lncRNAs, patients with higher expression of LINC01296, LINC00152, and FIRRE showed significantly better overall survival than those with lower expression (P < 0.05), suggesting that these lncRNAs might be associated with prognosis. Multivariate analysis indicated that LINC01296 overexpression was an independent predictor for patients' prognosis in the test datasets (GSE24549, GSE24550) (P = 0.001) and an independent validation series (GSE39582) (P = 0.027). Our results suggest that LINC01296 could be a novel prognosis biomarker for the diagnosis of CRC.