Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse.

BACKGROUND: Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data. METHODS: In this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation. RESULTS: The proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests. CONCLUDES: Overall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.

Amplitude of Low-Frequency Fluctuations During Resting State Differentially Predicts Authentic and Hubristic Pride.

OBJECTIVE: Pride is an important, self-conscious emotion composed of two distinct conceptual facets: arrogant, egotistic "hubristic pride," and pro-social, achievement-oriented "authentic pride." However, little is known about the neural basis of the two facets of pride. Here, we investigated the association between spontaneous brain activity and these two facets of pride in resting state. METHOD: We measured 276 participants on authentic and hubristic pride. The fractional amplitude of low-frequency fluctuations (fALFF) was used to identify pride-related regions. RESULTS: The results revealed individual differences in authentic pride were associated with the fALFF in the bilateral superior temporal gyrus (STG), which has been implicated in social processing. In contrast, individual differences in hubristic pride were associated with the fALFF in the left orbitofrontal cortex (OFC) and posterior cingulate cortex (PCC), which have been implicated in self-referential and reward processing. CONCLUSIONS: Together, our results provide initial evidence for the distinct neural substrates for authentic and hubristic pride.

Development of membrane-targeting TPP+-chloramphenicol conjugates to combat methicillin-resistant staphylococcus aureus (MRSA) infections.

Infections caused by drug-resistant bacteria have become a new challenge in infection treatment, gravely endangering public health. Chloramphenicol (CL) is a well-known antibiotic which has lost its efficacy due to bacterial resistance. To address this issue, herein we report the design, synthesis and biological evaluations of novel triphenylphosphonium chloramphenicol conjugates (TPP+-CL). Study results indicated that compounds 39 and 42 possessed remarkable antibacterial effects against clinically isolated methicillin-resistant Staphylococcus aureus (MRSA) with MIC values ranging from 1 to 2 µg/mL, while CL was inactive to the tested MRSA strains. In addition, these conjugates exhibited rapid bactericidal properties and low toxicity, and did not readily induced bacterial resistance, obviously outperforming the parent drug CL. In a mouse model infected with a clinically isolated MRSA strain, compound 39 at a dose of 20 mg/kg exhibited a comparable or even better in vivo anti-MRSA efficacy than the golden standard drug vancomycin, while no toxicity was observed.

Development of dual inhibitors targeting pyruvate dehydrogenase kinases and human lactate dehydrogenase A: High-throughput virtual screening, synthesis and biological validation.

Most cancer cells feature an altered glucose metabolism from oxidative phosphorylation to cytoplasmic glycolysis. Pyruvate dehydrogenase kinases (PDKs) and lactate dehydrogenase A (LDHA) play crucial roles in promotion of glycolysis, thus the inhibition of both enzymes is considered a promising strategy for developing of anticancer therapeutics. Herein, we describe the first discovery of series novel dual inhibitors targeting PDKs and LDHA. We identified 6 hits from a library database containing 485465 compounds through a high-throughput virtual screening assay. Hit-to-lead optimization enabled us to discover two compounds, namely 20e and 20k, which inhibited PDKs with IC50 values of 0.8, and 1.6 µM, respectively, and inhibited LDHA with IC50 values of 0.15 and 0.7 µM, respectively. Meanwhile, the two compounds reduced A549 cell proliferation with EC50 values of 13.2, and 15.7 µM. Furthermore, 20e and 20k decreased the lactate formation, and increased oxygen consumption, suggesting the two compounds modulated the glucose metabolic pathways in cancer cells.

The dissociable neural dynamics of cognitive conflict and emotional conflict control: An ERP study.

This study investigated differences in the neural time-course of cognitive conflict and emotional conflict control, using event-related potentials (ERPs). Although imaging studies have provided some evidence that distinct, dissociable neural systems underlie emotional and nonemotional conflict resolution, no ERP study has directly compared these two types of conflict. Therefore, the present study used a modified face-word Stroop task to explore the electrophysiological correlates of cognitive and emotional conflict control. The behavioral data showed that the difference in response time of congruency (incongruent condition minus the congruent condition) was larger in the cognitive conflict task than in the emotional conflict task, which indicated that cognitive conflict was stronger than the emotional conflict in the present tasks. Analysis of the ERP data revealed a main effect of task type on N2, which may be associated with top-down attention. The N450 results showed an interaction between cognitive and emotional conflict, which might be related to conflict detection. In addition, we found the incongruent condition elicited a larger SP than the congruent condition, which might be related to conflict resolution.