Enhanced glycolysis in the myometrium with ectopic endometrium of patients with adenomyosis: a preliminary study.

OBJECTIVES: The objective of this study was to investigate the glycolytic activity of adenomyosis, which is characterized by malignant biological behaviors including abnormal cell proliferation, migration, invasion, cell regulation, and epithelial-mesenchymal transition. METHODS: From January 2021 to August 2022, a total of 15 patients who underwent total hysterectomy for adenomyosis and 14 patients who had non-endometrial diseases, specifically with cervical squamous intraepithelial neoplasia and uterine myoma, were included in this study. Myometrium with ectopic endometrium from patients with adenomyosis while normal myometrium from patients in the control group were collected. All samples were confirmed by a histopathological examination. The samples were analyzed by liquid chromatography-mass spectrometry (LC-MS), real-time quantitative PCR, NAD+/NADH assay kit as well as the glucose and lactate assay kits. RESULTS: Endometrial stroma and glands could be observed within the myometrium of patients in the adenomyosis group. We found that the mRNA expressions of HK1, PFKFB3, glyceraldehyde-3-phospate dehydrogenase (GAPDH), PKM2, and PDHA as well as the protein expressions of PFKFB3 were elevated in ectopic endometrial tissues of the adenomyosis group as compared to normal myometrium of the control group. The level of fructose 1,6-diphosphate was increased while NAD + and NAD+/NADH ratio were decreased compared with the control group. Besides, increased glucose consumption and lactate production were observed in myometrium with ectopic endometrium. CONCLUSIONS: We concluded that altered glycolytic phenotype of the myometrium with ectopic endometrium in women with adenomyosis may contribute the development of adenomyosis.

Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma.

BACKGROUND: Although many patients receive good prognoses with standard therapy, 30-50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. METHODS: Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. RESULTS: Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. CONCLUSIONS: Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.

Applying probability calibration to ensemble methods to predict 2-year mortality in patients with DLBCL.

BACKGROUND: Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. METHODS: We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. RESULTS: Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. CONCLUSIONS: Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.

Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods.

BACKGROUND: Treatments are limited for patients with relapsed/refractory Diffuse large B-cell lymphoma (DLBCL), and their survival rate is low. Prediction of the recurrence hazard for each patient could provide a reference regarding chemotherapy regimens for clinicians to extend patients' period of long-term remission. As current strategies cannot satisfy such need, we have established predictive models to classify patients with DLBCL with complete remission who had recurrences in 2 years from ones who did not. METHODS: We assessed 518 patients with DLBCL and measured 52 variables of each patient. They were treated between January 2011 and July 2016. 17 variables were first selected by variable selection methods (including Lasso, Adaptive Lasso, and Elastic net). Then, we set classifiers and probability models for imbalanced data by combining the SMOTE sampling, cost-sensitive, and ensemble learning (consisting of AdaBoost, voting strategy, and Stacking) methods with the machine learning methods (Support Vector Machine, BackPropagation Artificial Neural Network, Random Forest), respectively. Last, assessed their performance. RESULTS: The disease stage and other 5 variables are significant indicators for recurrence. The SVM with AdaBoost ensemble learning method modeling by SMOTE data performs the best (Sensitivity=97.3%, AUC=96%, RMSE=19.6%, G-mean=96%) in all classifiers. The SVM with AdaBoost method(AUC=98.7%, RMSE=17.7%, MXE=12.7%, Cal mean=3.2%, BS0=2.5%, BS1=4%, BSALL=3.1%) and random forest (AUC=99.5%, RMSE=19.8%, MXE=16.2%, Cal mean=9.1%, BS0=4.8%, BS1=2.9%, BSALL=3.9%) both modeling by SMOTE sampling data perform well in probability models. CONCLUSIONS: This predictive model has high accuracy for almost all DLBCL patients and the six indicators can be recurrence signals.

Separation of α-glucosidase inhibitors from Potentilla kleiniana Wight et Arn using solvent and flow-rate gradient high-speed counter-current chromatography target-guided by ultrafiltration HPLC-MS screening.

INTRODUCTION: Potentilla kleiniana Wight et Arn is widely used as a herbal medicine to treat type 2 diabetes. However, detailed information about its active compounds is lacking. OBJECTIVE: To develop an efficient method for the rapid screening and separation of α-glucosidase inhibitors from Potentilla kleiniana Wight et Arn. METHODOLOGY: Potential α-glucosidase inhibitors from Potentilla kleiniana Wight et Arn were rapidly screened out through ultrafiltration high-performance liquid chromatography mass spectrometry (HPLC-MS), and then followed by a target-guided high-speed counter-current chromatography (HSCCC) separation using two-phase solvent systems composed of n-hexane/ethyl acetate/methanol/water (1:10:1:10, v/v/v/v and 1:10:5:6, v/v/v/v), and adopting increasing flow-rate from 1.5 to 3.0 mL/min after 200 min. Their structures were identified by ultraviolet (UV), MS, proton nuclear magnetic resonance (1 H-NMR) and carbon-13 (13 C)-NMR, and their α-glucosidase inhibitory activities were assessed by in vitro assay. RESULTS: Five α-glucosidase inhibitors including gallic acid (25.7 mg, 98.2%, 1), brevifolincarboxylic acid (9.86 mg, 95.3%, 2), ethyl evifolincarboxylate (13.26 mg, 97.6%, 3), 3,3'-di-O-methylellagic acid-4'-O-ß-d-glucopyranoside (16.26 mg, 95.1%, 4), and 3,3'-di-O-methylellagic acid (10.54 mg, 96.8%, 5) were successfully purified from 250 mg n-butanol extract in a single run. Compounds 1, 2, 4 and 5 exhibited stronger α-glucosidase inhibitory activities[half maximal inhibition concentration (IC50 ) values at 173.41 ± 6.35, 323.46 ± 8.08, 44.63 ± 2.50, and 20.73 ± 2.56 µM, respectively] than acarbose (IC50 value at 332.12 ± 5.52 µM, reference compound). CONCLUSIONS: Notably, compounds 2-5 were reported in the Potentilla kleiniana Wight et Arn for the first time. The results indicated that the proposed method could be applied for the rapid screening and preparative separation of α-glucosidase inhibitors from a complex matrix.

Nitrogen-doped carbon quantum dots as a fluorescent probe to detect copper ions, glutathione, and intracellular pH.

A facile one-step hydrothermal method was developed to synthesize nitrogen-doped carbon quantum dots (N-CQDs) by utilizing hexamethylenetetramine as the carbon and nitrogen source. The quantum yield (QY) of 21.7% was under the excitation wavelength of 420 nm with maximum emission at 508 nm. This N-CQD fluorescent probe has been successfully applied to selectively determine the concentration of copper ion (Cu2+) with a linear range of 0.1-40 µM and a limit of detection of 0.09 µM. In addition, the fluorescence of N-CQDs could be effectively quenched by Cu2+ and specifically recovered by glutathione (GSH), which render the N-CQDs as a premium fluorescent probe for GSH detection. This fluorescence "turn-on" protocol was applied to determine GSH with a linear range of 0.1-30 µM as well as a detection limit of 0.05 µM. For pH detection, there is good linearity in the pH range of 2.87-7.24. Furthermore, N-CQD is a promising and convenient fluorescent pH, Cu2+, and glutathione sensor with brilliant biocompatibility and low cytotoxicity in environmental monitoring and bioimaging applications.