Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

BackgroundEarly identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.MethodsThis retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.ResultsTen features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.ConclusionsThe CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations

Seismic effects of loess slopes using physical modeling and numerical simulation

A large shaking-table test of a loess slope with a geometric similarity ratio of 1:25 was established as a prototype model of the loess landslide in Yongguang Village, which occurred during the 2013 Minxian–Zhangxian M6.6 earthquake. Combined with finite-element–numerical-simulation software, the dynamic response characteristics of the loess slope were compared and analyzed under the action of the same Minxian horizontal seismic wave characteristics. The results show that the seismic waves observed in the model test and numerical simulation clearly exhibit non-linear amplification effects along the slope surface and in the loess vertical direction, reaching a maximum value at the top of the slope. The ground-motion acceleration waveforms observed in the model test and numerical simulation at each observation point of the slope were similar, and the horizontal peak ground acceleration (PGA) obtained from the numerical simulation at these locations was larger than that of the slope top. The horizontal aSimulated-max/aModel-max values ranged from 1.5 to 2.2 and the vertical aSimulated-max/aModel-max values were more discrete in the range of 0.5–2.7, while the horizontal and vertical-PGA-amplification factors at the slope surface were similar. The Fourier amplitude and main horizontal frequencies were mainly distributed between 0.1 and 12 Hz. The Fourier spectrum observed in the model test was approximately 0.5 Hz, while the main frequency of the vertical Fourier spectrum was approximately 4 Hz. Additionally, the main frequency of the horizontal and vertical Fourier spectra observed in the numerical simulation was approximately 2 and 1.2 Hz, respectively

Aptamer-Functionalized Magnetic Bead-Based Fluorescence Sensor for the Detection of Cronobacter sakazakii in Milk Powder Using Hybridization Chain Reaction Amplification

In this study, an aptamer-functionalized magnetic bead-based fluorescence sensor for the detection of Cronobacter sakazakii in milk powder using hybridization chain reaction (HCR) amplification was constructed. First, the sequence HP, combining a trigger sequence and an aptamer sequence which complement each other to form a stable secondary structure, and the hairpin sequences H1 and H2 were cleverly designed. Then, aptamer-functionalized magnetic beads were prepared by pentanediol reaction and avidin-biotin reaction. C. sakazakii was incubated with the aptamer magnetic beads. The aptamer sequence in HP recognized the target, causing conformational change of HP to expose its trigger sequence. The chain assembly of H1 and H2 was triggered by HCR to produce long double-stranded DNA, and the fluorescent dye SYBR Green I (SG) bound to the long double strands of HCR products by intercalation and slot binding. Finally, graphene oxide (GO) was added to adsorb free H1, H2 and SG on its surface via π-π stacking, so the fluorescence signal was quenched. However, the HCR products could not be adsorbed on the surface of GO, so SG binding to the HCR product emitted a strong fluorescence signal dependent on the target concentration, thus allowing quantitative detection of C. sakazakii. The detection limit of this method was 2 CFU/mL for pure culture and 8 CFU/g for milk powder. It gave results for milk powder samples in good agreement with those obtained by the traditional microbial culture method. This method has the advantages of no requirement for DNA extraction, fast operation, high stability, specificity and sensitivity, so it provides a potential method for the on-site rapid detection of C. sakazakii

An evaluation of the lighting environment in the public space of shopping centres

Interior lighting quality influences people's visual comfort and satisfaction with a space. Based on a field study about the effects of lighting environments on occupancies in eight shopping malls with three different latitudes and a wide range of size from 30,000 to 210,000 m2 all over China, this study investigates the differences in subjective evaluations, the correlations between the lighting environments of public spaces and people's evaluations, and the regressions of scene mean luminance. A questionnaire survey and HDR-image techniques have been used to gather subjective feedback and collect physical lighting data. The results show that the subjective evaluations among different groups (gender, age, education background, and duration of stay in the shopping malls) are similar. The existence of daylighting plays an important role in subjective satisfaction, but not crucial enough to their brightness perception. The mean luminance values of these scenes are closely correlated to the diversity of the subjective evaluations. For shopping centres, the optimal Lmean value is 1000 cd/m2 for a mixed daylighting and artificial lighting environment, and 75 cd/m2 as the recommended Lmean value for an artificial lighting environment

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility