Distinguishing Ability Research Articles

Biomechanical properties analysis of forme fruste keratoconus and subclinical keratoconus.

To analyze the biomechanical properties of the eye in patients with unilateral keratoconus with normal (forme fruste keratoconus [FFKC]) or abnormal topography (subclinical keratoconus [SKC]). This study included 153 eyes of 153 participants, including 95 eyes of patients with unilateral keratoconus, and 58 eyes of 58 healthy controls. Contralateral eyes with unilateral keratoconus were divided into two groups according to clinical manifestations and global consensus: FFKC (n = 30) and SKC (n = 65). The biomechanical characteristics were analyzed using non-parametric tests; further analysis thereof was performed after adjusting for confounding factors (i.e., intraocular pressure, age, and corneal thickness). Receiver operating characteristic curve (ROC) was used to analyze the ability of the biomechanical parameters to distinguish FFKC from SKC. Statistically significant differences between the FFKC and SKC groups were found in 9 of the 18 corneal biomechanical parameters analyzed using non-parametric tests. After adjusting for confounding factors, the multivariate analysis still revealed significant statistical differences in A1-time (P = 0.017), integrated radius (IR) (P = 0.024), and tomographic and biomechanical index (TBI, P < 0.001) between the FFKC and SKC groups. Stiffness parameter at first applanation (SP-A1) (Area under ROC [AUROC] = 0.765) demonstrated the strongest distinguishing ability, except for TBI (AUROC = 0.858) and Corvis Biomechanical Index (AUROC = 0.849), however, there was no statistically significant difference in SP-A1 (P = 0.366) between FFKC and SKC. Biomechanical parameters A1-time and IR have a high diversity between FFKC and SKC, besides TBI, and may reflect more subtle changes in corneal biomechanical properties (BPs) preceding SP-A1. The BPs of SKC are weaker than FFKC, which might be a basic and clue for the classification and diagnosis of the severity of early keratoconus in terms of biomechanics.

Feature Selection Using Neighborhood based Entropy

Feature selection plays an important role as a preprocessing step for pattern recognition and machine learning. The goal of feature selection is to determine an optimal subset of relevant features out of a large number of features. The neighborhood discrimination index (NDI) is one of the newest and the most efficient measures to determine distinguishing ability of a feature subset. NDI is computed based on a neighborhood radius (E). Due to the significant impact of E on NDI, selecting an appropriate value of E for each data set might be challenging and very time-consuming. This paper proposes a new approach based on targEt PointS To computE neIgh- borhood relatioNs (EPSTEIN). At first, all the data points are sorted in the descending order of their density. Then, the highest density data points are selected as many as the number of classes. To determine the neighborhood relations, the circles centered on the target points are drawn and the points inside or on the circles are considered to be neighbors. In the next step, the significance of each feature is computed and a greedy algorithm selects appropriate features. The performance of the proposed approach is compared to both the commonest and newest methods of feature selection. The experimental results show that EPSTEIN could select more efficient subsets of features and improve the prediction accuracy of classifiers in comparison to the other state-of-the-art methods such as Correlation-based Feature Selection (CFS), Fast Correlation-Based Filter (FCBF), Heuris- tic Algorithm Based on Neighborhood Discrimination Index (HANDI), Ranking Based Feature Inclusion for Optimal Feature Subset (KNFI), Ranking Based Feature Elimination (KNFE) and Principal Component Analysis and Information Gain (PCA-IG).

CSCE-Net: Channel-Spatial Contextual Enhancement Network for Robust Point Cloud Registration

Seeking reliable correspondences between two scenes is crucial for solving feature-based point cloud registration tasks. In this paper, we propose a novel outlier rejection network, called Channel-Spatial Contextual Enhancement Network (CSCE-Net), to obtain rich contextual information on correspondences, which can effectively remove outliers and improve the accuracy of point cloud registration. To be specific, we design a novel channel-spatial contextual (CSC) block, which is mainly composed of the Channel-Spatial Attention (CSA) layer and the Nonlocal Channel-Spatial Attention (Nonlocal CSA) layer. The CSC block is able to obtain more reliable contextual information, in which the CSA layer can selectively aggregate the mutual information between the channel and spatial dimensions. The Nonlocal CSA layer can compute feature similarity and spatial consistency for each correspondence, and the CSA layer and Nonlocal CSA layer can support each other. In addition, to improve the distinguishing ability between inliers and outliers, we present an advanced seed selection mechanism to select more dependable initial correspondences. Extensive experiments demonstrate that CSCE-Net outperforms state-of-the-art methods for outlier rejection and pose estimation tasks on public datasets with varying 3D local descriptors. In addition, the network parameters of CSCE-Net are reduced from 1.05M to 0.56M compared to the recently learning-based outlier rejection method PointDSC.

A Clinical-Radiomics Model of Preoperative Prediction IMN Status of Breast Cancer Patients

<h3>Purpose/Objective(s)</h3> To establish a prediction model based on clinicopathological factors and dynamic contrast enhanced (DCE)-MRI radiomics features, and to make a non-invasive prediction of IMN metastasis risk before operation. <h3>Materials/Methods</h3> The DCE-MR images and clinicopathological data of 124 breast cancer patients who had undergone internal mammary sentinel lymph node biopsy and / or internal mammary lymph node (IMN)dissection with IMN negative on preoperative images from January 2013 to December 2019 were obtained. All patients received plain breast scan and dynamic contrast-enhanced MR in Shandong Cancer Hospital before operation. In this study, three types of predictive models were established, which are based on clinicopathological risk factors, DCE-MRI imaging features and the combination of the two: 1) Clinical model: a logistic regression model based on clinicopathological factors was developed to predict ALN status. 2) Radiomics model: radiomics model based on radiomics score (RS) score is developed. 3) Clinical-radiomics model: combining the imaging features with the independent risk factors for independent prediction of IMN status, the multiple logistic regression method was used to establish a nomogram for predicting the risk of IMN metastasis. All models were evaluated by ROC and AUC, and clinical decision analysis curves was used to evaluate the clinical application value of the model. <h3>Results</h3> Three variables including primary tumor location, PR status and axillary lymph node metastasis status were included in the clinical model, and the AUC was 0.913. 150 imaging features were extracted from DCE-MRI images of primary tumors. Finally, 4 imaging features were selected to participate in the development of RS. Finally, the AUC of the imaging model combined with DCE-MRI imaging features of IMN was 0.831. The clinical-radiomics model with an AUC of 0.964. Delong test showed that, there was no significant difference in ROC curve between clinical model and radiomics model (p=0.1337) , but there were significant differences in ROC curve between clinical model and clinical-radiomics model, and between clinical model and clinical-radiomics model (p<0.05). DCA curve analysis shows that the application of clinical-radiomics model has the greatest net benefit, optimal predictive efficiency and better distinguishing ability, which is better than clinical model and radiomics model. <h3>Conclusion</h3> Clinical-radiomics model is helpful to evaluate the IMN metastasis status of breast cancer patients before operation.

Relative Knowledge Distance Measure of Intuitionistic Fuzzy Concept

Knowledge distance is used to measure the difference between granular spaces, which is an uncertainty measure with strong distinguishing ability in a rough set. However, the current knowledge distance failed to take the relative difference between granular spaces into account under the given perspective of uncertain concepts. To solve this problem, this paper studies the relative knowledge distance of intuitionistic fuzzy concept (IFC). Firstly, a micro-knowledge distance (md) based on information entropy is proposed to measure the difference between intuitionistic fuzzy information granules. Then, based on md, a macro-knowledge distance (MD) with strong distinguishing ability is further constructed, and it is revealed the rule that MD is monotonic with the granularity being finer in multi-granularity spaces. Furthermore, the relative MD is further proposed to analyze the relative differences between different granular spaces from multiple perspectives. Finally, the effectiveness of relative MD is verified by relevant experiments. According to these experiments, the relative MD has successfully measured the differences in granular space from multiple perspectives. Compared with other attribute reduction algorithms, the number of subsets after reduction by our algorithm is in the middle, and the mean-square error value is appropriate.

Incomplete label distribution feature selection based on neighborhood-tolerance discrimination index

Label distribution learning (LDL), focusing on the relative importance of different labels to the instance, is proposed for solving label ambiguity problem in recent years. However, for label distribution data, the annotation information may be incomplete in the real-world and complete methods cannot be directly used to process these data. In addition, with the exponential growth of data volume, data in all walks of life tend to be high-dimensional, feature selection as an efficient preprocessing technique to reduce the dimension of data. Taking the problems of the incomplete label and high-dimensional data into consideration, an incomplete label distribution feature selection method based on neighborhood-tolerance discrimination index is proposed. The neighborhood-tolerance discrimination index is utilized to explore the distinguishing ability of the feature subset, and then a novel significance metric is constructed to evaluate the importance of features, which considers the correlations between features and labels. Compared with multi-label feature selection algorithms, the proposed algorithm is designed to directly process label distribution data without discretization, which reduces information loss in the process of discretization. Compared to existing label distribution feature selection algorithms, the proposed algorithm can directly process distribution data with missing label, which avoids the interference of noisy information. Furthermore, the superiority of the proposed method over other seven state-of-the-art methods is demonstrated by conducting comprehensive experiments with eight publicly available label distribution datasets on six widely-used metrics. The experimental results show that the proposed algorithm obtains superior performance in 91.67% of cases against compared algorithms.

Probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measurement

In order to solve the problem of multi-attribute decision-making with unknown weights under probabilistic hesitant fuzzy information, considering the shortcomings of the existing probabilistic hesitant fuzzy distance measure, such as weak distinguishing ability, a probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measures is proposed. Firstly, the hesitancy degree of probabilistic hesitant fuzzy element and the improved difference measure of probabilistic hesitant fuzzy element are defined, and an improved probabilistic hesitant fuzzy distance measure based on hesitancy degree, incompleteness degree and improved difference measure is proposed. Secondly, based on the improved distance measure, a mathematical programming model with the goal of minimizing the relative approach degree is con-structed to determine the attribute weights of evaluation indexes in multi-attribute decision making problems. Using it as a base, a new probabilistic hesitant fuzzy multi-attribute decision-making method is proposed by combining the improved probabilistic hesitant fuzzy distance measure with the compromise ratio method. Finally, the proposed method is applied to the problem of green supplier selection, and the feasibility and effectiveness of the proposed method are verified by case analysis and comparison with other methods.

Development and Validation of a Nomogram Model for Predicting the Risk of Readmission in Patients with Heart Failure with Reduced Ejection Fraction within 1 Year.

The high incidence of readmission for patients with reduced ejection fraction heart failure (HFrEF) can seriously affect the prognosis. In this study, we aimed to build a simple predictive model to predict the risk of heart failure (HF) readmission in patients with HFrEF within one year of discharge from the hospital. This retrospective study enrolled patients with HFrEF evaluated in the Heart Failure Center of the Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2020. The patients were allocated into the readmission or nonreadmission group, according to whether HF readmission occurred within 1 year of hospital discharge. Subsequently, all patients were randomly divided into training and validation sets in a 7 : 3 ratio. A nomogram was established according to the results of univariate and multivariate logistic regression analysis. Finally, the area under the receiver operating characteristic curve (AUC-ROC), calibration plot, and decision curve analysis (DCA) were used to validate the nomogram. Independent risk factors for HF readmission of patients with HFrEF within 1 year of hospital discharge were as follows: age, body mass index, systolic blood pressure, diabetes mellitus, left ventricular ejection fraction, and angiotensin receptor-neprilysin inhibitors. The AUC-ROC of the training and validation sets were 0.833 (95% confidence interval (CI): 0.793-0.866) and 0.794 (95% CI: 0.727-0.852), respectively, which have an excellent distinguishing ability. The predicted and observed values of the calibration curve also showed good consistency. DCA also confirmed that the nomogram had good clinical value. In conclusion, we constructed an accurate and straightforward nomogram model for predicting the 1-year HF readmission risk in patients with HFrEF. This nomogram can guide early clinical intervention and improve patient prognosis.

Tetrahedral DNA framework based CRISPR electrochemical biosensor for amplification-free miRNA detection

microRNA (miRNA) is a kind of small non-coding RNA that has been regarded as potential biomarkers for cancers. Sensitive and specific detection of miRNA at low expression levels is highly desirable but remains challenging, especially for amplification-free and portable point of care (POC) diagnostics. The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas13a has been recently discovered and used in the field of RNA detection. Nonetheless, most CRISPR/Cas13a-based methods were burdened with expensive equipment, time-consuming procedures, and complicated operations which were not suitable for POC analysis. In this work, we constructed a three-dimensional tetrahedral DNA framework based CRISPR-electrochemical biosensor (CRISPR-E). By combining tetrahedral DNA framework, CRISPR, and electrochemical biosensor, the process of activation, cleavage of Cas13a, and signal readout were all finished on the chip, and a simple, amplification-free and sensitive detection of miRNA-19b was realized. Under the optimal experimental conditions, a linear range from 10 pM to 104 pM with detection limit of 10 pM for miRNA-19b in buffer solution was achieved. Selectivity analysis indicated that our CRISPR-E had good distinguishing ability between miRNA-19b and miRNA-197. The results of miRNA-19b detection in mimic serum samples were consistent with that of the buffer solution. This all-on-chip strategy of our CRISPR-E is very suitable for POC testing.

A Credit Scoring Model Based on Integrated Mixed Sampling and Ensemble Feature Selection: RBR_XGB

&lt;p&gt;With the rapid development of the economy, financial institutions pay more and more attention to the importance of financial credit risk. The XGBoost algorithm is often used in credit scoring. However, it should be noted that XGBoost has three disadvantages when dealing with small samples of high-dimensional imbalance: (1) the model classification results are more biased towards the majority class when the XGBoost algorithm is used in training imbalanced data, this results in reduced model accuracy. (2) XGBoost algorithm is prone to overfitting in high-dimensional data because the higher the data dimension, the sparser the samples. (3) In small datasets, it is prone to form data fragmentation, resulting in reduced model accuracy. A Credit Scoring Model Based On Integrated Mixed Sampling And Ensemble Feature Selection (RBR_XGB) is proposed on the following issues in this paper. The model first aims at the model failure and overfitting problems of XGBoost in the face of highly imbalanced small samples, and uses the improved hybrid sampling algorithm combining RUS and BSMOTE1 to balance and expand the data set. For feature redundancy problems, the RFECV_XGB algorithm is used to filter features for reducing interference features. Then, considering the strength of the distinguishing ability of different models, the validation set is used to assign weights to different models, and the weighted ensemble is used to further improve the performance of the model. The experimental results show that the classification performance of the RBR_XGB algorithm for high-dimensional imbalanced small data is higher than that of the traditional XGBoost algorithm, and it can be used for commercial use. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Contactless Glucose Sensing at Sub‐Micromole Level Using a Deep‐Subwavelength Decimeter‐Wave Plasmonic Resonator

AbstractMicrowave resonance sensing has found a wide variety of applications and exhibits promising potentials in compact integrated sensors. However, its distinguishing ability to trace‐amount targets remains challenging, which can be characterized by the effective wavelength and the quality factor (Q‐factor). Here, a contactless sensing scheme for detecting sub‐micromole glucose using a deep‐subwavelength spoof localized surface plasmon (SLSP) resonator working at the decimeter‐wave band is proposed. The SLSP resonator sustains a mixed‐mode of an electric monopole and a magnetic dipole, compressing the electromagnetic mode into a diameter smaller than λ0/41 (λ0 is the free‐space wavelength) and achieving a measured Q‐factor of 187. In experiments, contactless sensing has been realized using a polydimethylsiloxane (PDMS) microfluidic channel, which separates the resonator and the solution. The solution sample under test features an ultra‐compact volume of 3.7 × 10–8 λ03, and a detection limit of 0.45 µmol glucose is demonstrated. This work envisions the applications of spoof localized surface plasmons in the contactless and trace‐amount biomedical sensing.

The influence of antimony additive on structural, mechanical, and nuclear radiation shielding parameters of rapidly quenched Pb-Sn binary alloy

This study aims to investigate the ability of as-prepared PbSn3Sb x (x = 0.0, 0.5, 1.5, and 2.5 wt.%) alloys for shielding against gamma-rays, the relationships between the mechanical properties and gamma-ray shielding parameters for four types of alloys have been studied and analyzed. Gamma-ray interaction with the as-prepared alloys has been discussed mainly in terms of total mass attenuation coefficient (µm ), linear attenuation coefficient (µ), half value layer (HVL), and mean free path (Xm ). From X-ray analysis, a crystalline intermetallic compound phase of SnSb is detected. The formation of the intermetallic compound phase and refinement of evaluated particle size causes a pronounced enhancement in internal friction, dynamic elastic modulus, and microhardness indentation. Attenuation shielding parameters of as-prepared alloys with free antimony are measured and calculated. It is noticed that the obtained results showed desirable values of shielding parameters. Mechanical properties and Vickers microhardness, Hv are critically evaluated and sensitive to changes in temperature and Sb antimony additions. Thus, the study indicates that 2.5 wt.% Sb alloy sample has a distinguished protection ability to attenuate the gamma-ray radiation. It can be concluded that the Sb additive for this type of binary alloys at content 2.5 wt.% can help to increase the shielding gamma-ray performance.

Robust Multiobjective Optimization for Vehicle Routing Problem With Time Windows.

In this article, we focus on the vehicle routing problem (VRP) with time windows under uncertainty. To capture the uncertainty characteristics in a real-life scenario, we design a new form of disturbance on travel time and construct robust multiobjective VRP with the time window, where the perturbation range of travel time is determined by the maximum disturbance degree. Two conflicting objectives include: 1)the minimization of both the total distance and: 2)the number of vehicles. A robust multiobjective particle swarms optimization approach is developed by incorporating an advanced encoding and decoding scheme, a robustness measurement metric, as well as the local search strategy. First, through particle flying in the decision space, the problem space characteristic under deterministic environment is fully exploited to provide guidance for robust optimization. Then, a designed metric is adopted to measure the robustness of solutions and help to search for the robust optimal solutions during the particle flying process. In addition to the updating process of particle, two local search strategies, problem-based local search and route-based local search, are developed for further improving the performance of solutions. For comparison, we develop several robust optimization problems by adding disturbances on selected benchmark problems. The experimental results validate our proposed algorithm has a distinguished ability to generate enough robust solutions and ensure the optimality of these solutions.

SiamATL: Online Update of Siamese Tracking Network via Attentional Transfer Learning.

Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.

Identifying RBBP7 as a Promising Diagnostic Biomarker for BK Virus-Associated Nephropathy

BK virus-associated nephropathy (BKVN) remains a major infectious complication due to powerful immunosuppression in kidney transplant recipients, and its histologic appearance can mimic rejection, leading to diagnostic and treatment dilemmas thus molecular diagnostic methods would be beneficial. We collected gene expression profiles of 169 kidney biopsies taken from BKVN, rejection, and stable functioning allografts, based on single sample gene set enrichment analysis and random forest algorithm, and three hallmark activities associated with DNA damage and proliferation were found to be relatively specific in BKVN. Subsequently, weighted gene co-expression network analysis and support vector machines (SVM) algorithm identified RBBP7 as a robust and promising biomarker with high accuracy in both training and validation cohorts (AUC =0.938, 0.977, respectively). Besides, potential drugs for BKVN treatment such as mepacrine were discovered, which may contribute to targeted antiviral therapy and effective patient management rather than simply reducing the doses of immunosuppressive agents in clinical practice. RBBP7 (retinoblastoma binding protein 7) serves as component of serval complexes that regulate chromatin metabolism and functions in affecting DNA replication and controlling cell proliferation. In this research, upregulation of RBBP7 was found to be associated with the higher infiltration of CD8 naïve T, iTreg, and neutrophil cells and the lower amounts of Th1, central memory T, NKT, CD8 T, and dendritic cells. Moreover, the infiltration of Th1, Th17, and NKT cells was steadily different between BKVN and rejection allografts through immune cell assessment. In conclusion, we identified and verified RBBP7 as a molecular biomarker for BKVN diagnosis, which demonstrated great distinguishing ability with allograft rejection and would support clinical decision-making.

Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion

To ensure the safety and stability of the rocket, it is essential to implement accurate anomaly detection on key parts such as the liquid rocket engine (LRE). However, due to the indistinct features of signals under the interference of extreme conditions and the weak distinguishing ability to exist unsupervised methods, it is difficult to distinguish abnormal samples from normal samples, which leads to the failure of reliable anomaly detection. Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not generated by an encoder but by a combination of memory items that record prototypical patterns of normal samples. Besides, each layer of the encoder and decoder has a skip connection to fully extract the multi-scale features of the normal sample in multi-dimensional space and suppress over-fitting caused by the memory-augmented network. Compared with existing methods and ablation control groups, experiments on four test sets prove the excellent generalization and satisfactory performances of the proposed Mem-SkipAE. Furthermore, the comparison of the single-source model and multi-source model verifies the effectiveness of multi-source fusion.

Comprehensive Analysis of TRP Channel-Related Genes in Patients With Triple-Negative Breast Cancer for Guiding Prognostic Prediction.

BackgroundTriple-negative breast cancer (TNBC) is a special subtype of breast cancer. Transient Receptor Potential (TRP) channel superfamily has emerged as a novel and interesting target in a variety of tumors. However, the association of TRP channel–related genes with TNBC is still unclear.MethodsThe The Cancer Genome Atlas (TCGA)-TNBC and GSE58812 datasets were downloaded from the public database. The differentially expressed TRP channel–related genes (DETGs) were screened by limma package, and mutations of the above genes were analyzed. Subsequently, new molecular subtypes in TNBC-based DETGs were explored by consensus clustering analysis. In addition, Lasso–Cox regression analysis was used to divide it into two robust risk subtypes: high-risk group and low-risk group. The accuracy and distinguishing ability of above models were verified by a variety of methods, including Kaplan–Meier survival analysis, ROC analysis, calibration curve, and PCA analysis. Meanwhile, CIBERSORT algorithm was used to excavate status of immune-infiltrating cells in TNBC tissues. Last, we explored the therapeutic effect of drugs and underlying mechanisms of risk subgroups by pRRophetic package and GSEA algorithm, respectively.ResultsA total of 19 DETGs were identified in 115 TNBC and 113 normal samples from TCGA database. In addition, missense mutation and SNP were the most common variant classification. According to Lasso–Cox regression analysis, the risky formula performed best when nine genes were used: TRPM5, TRPV2, HTR2B, HRH1, P2RY2, MAP2K6, NTRK1, ADCY6, and PRKACB. Subsequently, Kaplan–Meier survival analysis, ROC analysis, calibration curve, and Principal Components Analysis (PCA) analysis showed an excellent accuracy for predicting OS using risky formula in each cohort (P < 0.05). Specifically, high-risk group had a shorter OS compared with low-risk group. In addition, T-cell CD4 memory activated and macrophages M1 were enriched in normal tissues, whereas Tregs were increased in tumor tissues. Note that the low-risk group was better therapeutic effect to docetaxel, doxorubicin, cisplatin, paclitaxel, and gemcitabine than the high-risk group (P < 0.05). Last, in vitro assays, Quantitative Real-time PCR (qRT-PCR) indicated that TRPM5 was significantly highly expressed in MDA-MB-231 and MDA-MB-468 cells compared with that in MCF-10A cells (P < 0.01).ConclusionWe identified a risky formula based on expression of TRP channel–related genes that can predict prognosis, therapeutic effect, and status of tumor microenvironment for patients with TNBC.

A Personalized Management Approach of OHSS: Development of a Multiphase Prediction Model and Smartphone-Based App.

ObjectiveThis study aimed to develop multiphase big-data-based prediction models of ovarian hyperstimulation syndrome (OHSS) and a smartphone app for risk calculation and patients’ self-monitoring.MethodsMultiphase prediction models were developed from a retrospective cohort database of 21,566 women from January 2017 to December 2020 with controlled ovarian stimulation (COS). There were 17,445 women included in the final data analysis. Women were randomly assigned to either training cohort (n = 12,211) or validation cohort (n = 5,234). Their baseline clinical characteristics, COS-related characteristics, and embryo information were evaluated. The prediction models were divided into four phases: 1) prior to COS, 2) on the day of ovulation trigger, 3) after oocyte retrieval, and 4) prior to embryo transfer. The multiphase prediction models were built with stepwise regression and confirmed with LASSO regression. Internal validations were performed using the validation cohort and were assessed by discrimination and calibration, as well as clinical decision curves. A smartphone-based app “OHSS monitor” was constructed as part of the built-in app of the IVF-aid platform. The app had three modules, risk prediction module, symptom monitoring module, and treatment monitoring module.ResultsThe multiphase prediction models were developed with acceptable distinguishing ability to identify OHSS at-risk patients. The C-statistics of the first, second, third, and fourth phases in the training cohort were 0.628 (95% CI 0.598–0.658), 0.715 (95% CI 0.688–0.742), 0.792 (95% CI 0.770–0.815), and 0.814 (95% CI 0.793–0.834), respectively. The calibration plot showed the agreement of predictive and observed risks of OHSS, especially at the third- and fourth-phase prediction models in both training and validation cohorts. The net clinical benefits of the multiphase prediction models were also confirmed with a clinical decision curve. A smartphone-based app was constructed as a risk calculator based on the multiphase prediction models, and also as a self-monitoring tool for patients at risk.ConclusionsWe have built multiphase prediction models based on big data and constructed a user-friendly smartphone-based app for the personalized management of women at risk of moderate/severe OHSS. The multiphase prediction models and user-friendly app can be readily used in clinical practice for clinical decision-support and self-management of patients.

Discovery of Novel Small Molecule HDAC1, 2, 3 Inhibitors -- Combined Receptor-Based and Ligand-Based Virtual Screening Strategy

Aims:This study aims to investigate and validate the potential drug target to HDAC1.Background:Human histone deacetylase 1 (HDAC1) can catalyze the deacetylation of histones belonging to the family of human histone deacetylases (HDACs). Amide hydrolase HDAC1 plays a key role in the development of many serious cancers such as prostate cancer, gastric cancer, lung cancer, esophageal cancer, colon cancer, and breast cancer. Therefore, HDAC1 inhibitors, promoting the transcription of a series of key genes such as the p53 gene and inhibiting the development of cancer through various downstream mechanisms, have great potential for the treatment of cancer.Objective:The objective of this study is to discover new skeleton HDAC1 inhibitors efficiently and conveniently with therapeutic potential for cancer.Method:Based on the crystal structure of HDAC1, through the combination of receptor-based and ligand- based virtual screening from the commercial compound library, the top-ranked compounds are selected for purchase through binding modes analysis, and their activities were verified through in vitro HDAC1 inhibitory biological experiments.Results:Based on LeDock, 5ICN showed good distinguishing ability and was used as the receptor. According to the results of the LeDock docking scoring from receptor-based virtual screening, 69 compounds with binding energy less than -7.5 kcal/mol were obtained and used for ligand-based virtual screening. A total of 21 novel compounds with high potential HDAC1 inhibitory activity were collected by combining the similarity searching (NN) and the multinomial Naive Bayes machine learning model (NB) methods. Through binding modes analysis, 10 compounds with different structures with potential HDAC1 inhibitory activity were selected and screened HDAC1 inhibitory in vitro. May267 showed moderate HDAC1 inhibitory activity, and the inhibition rate was 48% at a concentration of 20 μM.Conclusion:This study discovers novel small molecule HDAC1 inhibitors by combined receptor-based and ligand-based virtual screening strategy, which provides an efficient method for the discovery of other small molecule drugs. May267 shows moderate HDAC1 inhibitory activity, which can be further optimized as a lead compound. However, it still has the problem of poor kinase selectivity to be solved.

Adaptive DDK Filter for GRACE Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient and the power index of signal covariances, which are adaptively determined from the data themselves according to the generalized cross validation (GCV) criterion. The regularization coefficient controls the global constraint on the signal variances of all degrees, while the power index adjusts the attenuation of the signal variances from low to high degrees, namely local constraint. By tuning these two parameters for the monthly SCM, the adaptability to the data and the optimality of filtering strength can be expected. In addition, we also devise a half-weight polygon area (HWPA) of the filter kernel to measure the filtering strength of the anisotropic filter more reasonably. The proposed adaptive DDK filter and filtering strength metric are tested based on CSR GRACE temporal gravity solutions with their ECMs from January 2004 to December 2010. Results show that the selected optimal power indices range from 3.5 to 6.9, with the corresponding regularization parameters range from 1 × 1014 to 5 × 1019. The adaptive DDK filter can retain comparable/more signal amplitude and suppress more high-degree noise than the conventional DDK filters. Compared with the equivalent smoothing radius (ESR) of filtering strength, the HWPA has stronger a distinguishing ability, especially when the filtering strength is similar.