Parathyroid hormone-related protein as a potential prostate cancer biomarker: Promoting prostate cancer progression through upregulation of c-Met expression.

Parathyroid hormone-related protein (PTHrP) plays a significant role in various tumor types, including prostate cancer. However, its specific role and underlying mechanisms in prostate cancer remain unclear. This study investigates the role of PTHrP and its interaction with the c-Met in prostate cancer. PTHrP was overexpressed and knocked down in prostate cancer cell lines to determine its effect on cell functions. Xenograft tumor models were employed to assess the impact of PTHrP overexpression on tumor growth. To delve into the interaction between PTHrP and c-Met, rescue experiments were conducted. Clinical data and tissue samples from prostate cancer patients were gathered and analyzed for PTHrP and c-Met expression. PTHrP overexpression in prostate cancer cells upregulates c-Met expression and augments cell functions. In contrast, PTHrP-knockdown diminishes c-Met expression and inhibits cell functions. In vivo experiments further demonstrated that PTHrP overexpression promoted tumor growth in xenograft models.Moreover, modulating c-Met expression in rescue experiments led to concurrent alterations in prostate cancer cell functions. Immunohistochemical analysis of clinical samples displayed a significant positive correlation between PTHrP and c-Met expression. Additionally, PTHrP expression correlated with clinical parameters like prostate-specific antigen (PSA) levels, tumor stage, lymph node involvement, distant metastasis, and Gleason score. PTHrP plays a crucial role in prostate cancer progression by upregulating c-Met expression. These insights point to PTHrP as a promising potential biomarker for prostate cancer.

First-principles investigation on multi-magnesium sulfide and magnesium sulfide clusters in magnesium-sulfide batteries.

Because of the abundance of magnesium and sulfur and their low cost, the development of magnesium sulfur batteries is very promising. In particular, the battery performance of nanoscale (MgS)n clusters is much better than that of bulk sized MgS. However, the structures, stability, and properties of MgxSy and (MgS)n clusters, which are very important to improve the performance of Mg-S batteries, are still unexplored. Herein, the most stable structures of MgxSy (x = 1-8, y = 1-8) and (MgS)n (n = 1-10) are reliably determined using the structure search method and density functional theory to calculate. According to calculation results, MgS3 and Mg6S8 may not exist in the actual charging and discharging products of magnesium sulfide batteries. The (MgS)n (n ≥ 5) clusters exhibit intriguing cage-like structures, which are favorable for eliminating dangling bonds and enhancing structural stability. Compared to the MgS monomer, each sulfur atom in the clusters is coordinated with more magnesium atoms, thus lengthening the Mg-S bond length and decreasing the Mg-S bond activation energy. Notably, with the increase of dielectric constant of electrolyte solvent, compared to the DME (ε = 7.2), THF (ε = 7.6) and C2H4Cl2 (ε = 10.0), MgxSy and (MgS)n clusters are most stable in the environment of C3H6O (ε = 20.7). It can delay the transformation of magnesium polysulfide to the final product MgS, which is conducive to improving the performance of Mg-S batteries. The predicted characteristic peaks of infrared and Raman spectra provide useful information for in situ experimental investigation. Our work represents a significant step towards understanding (MgS)n clusters and improving the performance of Mg-S batteries.

Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.

Numerous pieces of evidence have indicated that microRNA (miRNA) plays a crucial role in a series of significant biological processes and is closely related to complex disease. However, the traditional biological experimental methods used to verify disease-related miRNAs are inefficient and expensive. Thus, it is necessary to design some excellent approaches to improve efficiency. In this work, a novel method (CFSAEMDA) is proposed for the prediction of unknown miRNA-disease associations (MDAs). Specifically, we first capture the interactive features of miRNA and disease by integrating multi-source information. Then, the stacked autoencoder is applied for obtaining the underlying feature representation. Finally, the modified cascade forest model is employed to complete the final prediction. The experimental results present that the AUC value obtained by our method is 97.67%. The performance of CFSAEMDA is superior to several of the latest methods. In addition, case studies conducted on lung neoplasms, breast neoplasms and hepatocellular carcinoma further show that the CFSAEMDA method may be regarded as a utility approach to infer unknown disease-miRNA relationships.

Systematic review and meta-analysis of completely retroperitoneoscopic nephroureterectomy versus traditional retroperitoneoscopic nephroureterectomy in upper tract urothelial carcinoma.

BACKGROUND: This systematic review and meta-analysis aim to evaluate the efficacy and safety of completely retroperitoneoscopic nephroureterectomy (CRNU) for the treatment of upper urinary tract urothelial carcinoma (UTUC). METHODS: A systematic review of PubMed and Web of Science databases was conducted to identify trials comparing the outcomes of CRNU and other surgical procedures. A total of 6 case-control studies were selected for analysis. The efficacy and safety of CRNU were evaluated using mean difference or hazard ratio (HR) with 95% CIs, employing continuous or dichotomous method with a random or fixed-effect model. Meta-analysis was performed using STATA 11.0 software. RESULTS: The meta-analysis indicated that CRNU in subjects with UTUC was significantly associated with a shorter operation time (standardized mean difference, -1.36; 95% CI, -1.61 to -1.11, P < .001) and lower blood loss (standardized mean difference, -0.54; 95% CI, -0.77 to -0.31, P < .001) when compared to traditionally retroperitoneoscopic nephroureterectomy (TRNU). No significant difference was observed in the occurrence of grade I & II complications (HR, 1.04; 95% CI, 0.49-2.2, P = .915) and total complications (HR, 0.69; 95% CI, 0.38-1.27, P = .238) between CRNU and TRNU. CONCLUSION: The findings suggest that CRNU is an advanced surgical technique that is safe and effective for the treatment of UTUC. We recommend that CRNU be further employed for patients with UTUC. Further randomized, multicenter trials are needed to validate these results, given the limitations of this study.

A feature selection method based on graph theory for cancer classification.

OBJECTIVE: Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification. METHOD: In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data. RESULTS: In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods. CONCLUSION: In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant.

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

Cascade Forest-Based Model for Prediction of RNA Velocity.

In recent years, single-cell RNA sequencing technology (scRNA-seq) has developed rapidly and has been widely used in biological and medical research, such as in expression heterogeneity and transcriptome dynamics of single cells. The investigation of RNA velocity is a new topic in the study of cellular dynamics using single-cell RNA sequencing data. It can recover directional dynamic information from single-cell transcriptomics by linking measurements to the underlying dynamics of gene expression. Predicting the RNA velocity vector of each cell based on its gene expression data and formulating RNA velocity prediction as a classification problem is a new research direction. In this paper, we develop a cascade forest model to predict RNA velocity. Compared with other popular ensemble classifiers, such as XGBoost, RandomForest, LightGBM, NGBoost, and TabNet, it performs better in predicting RNA velocity. This paper provides guidance for researchers in selecting and applying appropriate classification tools in their analytical work and suggests some possible directions for future improvement of classification tools.

A hybrid breast cancer classification algorithm based on meta-learning and artificial neural networks.

The incidence of breast cancer in women has surpassed that of lung cancer as the world's leading new cancer case. Regular screening and measures become an effective way to prevent breast cancer and also provide a good foundation for later treatment. Women should receive regular checkups in the hospital after reaching a certain age. The use of computer-aided technology can improve the accuracy and efficiency of physicians' decision-making. Data pre-processing is required before data analysis, and 16 features are selected using a correlation-based feature selection method. In this paper, meta-learning and Artificial Neural Networks (ANN) are combined to create a hybrid algorithm. The proposed hybrid algorithm for predicting breast cancer was attempted to achieve 98.74% accuracy and 98.02% F1-score by creating a combination of various meta-learning models whose output was used as input features for creating ANN models. Therefore, the hybrid algorithm proposed in this paper can obtain better prediction results than a single model.

First-Principles-Based Optimized Design of Fluoride Electrolytes for Sodium-Ion Batteries.

Because of the abundance and low cost of sodium, sodium-ion batteries (SIBs) are next-generation energy storage mediums. Furthermore, SIBs have become an alternative option for large-scale energy storage systems. Because the electrolyte is a critical component of SIBs, fluorination is performed to improve the cycling performance of electrolytes. Based on the first-principles study, we investigated the effects of the type, quantity, and relative position relationships of three fluorinated units, namely -CF1, -CF2, and -CF3, on the cyclic ester molecule ethylene carbonate (EC) and the linear ether molecule 1,2-dimethoxylethane (DME). The optimal fluorination was proposed for EC and DME by studying the bond length, highest occupied molecular orbital, lowest unoccupied lowest orbital, and other relevant parameters. The results revealed that for EC, the optimal fluorination is 4 F fluorination based on four -CF1 units; for DME, CF3CF1CF1-, CF3CF2CF2-, CF3CF1CF2CF3, and CF3CF2CF2CF3, four combinations of three -CF1, -CF2, and -CF3 units are optimal. The designed fluorinated EC and DME exhibited a wide electrochemical stability window and high ionic solvation ability, which overcomes the drawback of conventional solvents and can improve SIB cycling performance.

Discovery and evaluation of cytisine N-isoflavones as novel EGFR/HER2 dual inhibitors.

Aberrant signaling of EGFR (ErbB) family members, in particular epidermal growth factor receptor (EGFR) and human epidermal growth factor 2 (HER2), is associated with the occurrence and development of many types of human malignancies (e.g., breast, lung, and gastric cancers), and dual targeting of EGFR/HER2 by small-molecular inhibitors has proven to be an effective therapeutic approach for treating these cancers. Herein we extracted and isolated from the medicinal plant Sophora alopecuroides L. a new natural product, dubbed Cytisine N-methylene-(4',7-dihydroxy-3'-methoxy)-isoflavone (CNI1) that features a unique molecular framework. Our biochemical kinase assay suggested that one of its derivative CNI3 exhibited the best, micromolar (µM) inhibition activities against the EGFR (IC50 of 1.1 µM; Ki of 0.6 µM) and HER2 (IC50 of 3.5 µM; Ki of 1.8 µM) kinases. By contrast, another derivative CNI4 was most potent in inhibiting the EGFR-overexpressing A431 cancer cell line (IC50 of 45.5 µM) and the HER2-overexpressing BT-474 cancer cell line (IC50 of 32.9 µM), while the respective cellular activities of Lapatinib (a marketed drug) were 24.9 and 20.3 µM under the same assay condition. Moreover, both CNI3 and CNI4 showed desirable anti-metastatic efficacy in another two breast cancer models (viz., MDA-MB-231 and 4T1). In addition, we explored the inhibitory mechanisms of the CNIs against EGFR and HER2 by molecular dynamics simulation and revealed a novel mode of action that engages the cytisine and chromone moieties in CNIs. By combining structure- and ligand-based analysis, we further rationally engineered a new CNI compound that exhibits considerably improved cytotoxicity against both types of A431 and BT-474 cancer cells. Our study demonstrates the CNI compounds as a new class of EGFR/HER2 dual inhibitors and paves a way for their further development.

Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier.

Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.

Molecular Mechanism of D1135E-Induced Discriminated CRISPR-Cas9 PAM Recognition.

The off-target effects of Streptococcus pyogenes Cas9 (SpCas9) pose a significant challenge to harness it as a therapeutical approach. Two major factors can result in SpCas9 off-targeting: tolerance to target DNA-guide RNA (gRNA) mismatch and less stringent recognition of protospacer adjacent motif (PAM) flanking the target DNA. Despite the abundance of engineered SpCas9-gRNA variants with improved sensitivity to target DNA-gRNA mismatch, studies focusing on enhancing SpCas9 PAM recognition stringency are quite few. A recent pioneering study identified a D1135E variant of SpCas9 that exhibits much-reduced editing activity at the noncanonical NAG/NGA PAM sites while preserving robust on-target activity at the canonical NGG-flanking sites (N is any nucleobase). Herein, we aim to clarify the molecular mechanism by which this single D1135E mutation confers on SpCas9 enhanced specificity for PAM recognition by molecular dynamics simulations. The results suggest that the variant maintains the base-specific recognition for the canonical NGG PAM via four hydrogen bonds, akin to that in the wild type (WT) SpCas9. While the noncanonical NAG PAM is engaged to the two PAM-interacting arginine residues (i.e., R1333 and R1335) in WT SpCas9 via two to three hydrogen bonds, the D1135E variant prefers to establish two hydrogen bonds with the PAM bases, accounting for its minimal editing activity on the off-target sites with an NAG PAM. The impaired NAG recognition by D1135E SpCas9 results from the PAM duplex displacement such that the hydrogen bond of R1333 to the second PAM base is disfavored. We further propose a mechanistic model to delineate how the mutation perturbs the noncanonical PAM recognition. We anticipate that the mechanistic knowledge could be leveraged for continuous optimization of SpCas9 PAM recognition specificity toward high-precision demanding applications.

Application of Combined Prediction Model Based on Core and Coritivity Theory in Continuous Blood Pressure Prediction.

BACKGROUND AND OBJECTIVE: Blood pressure is vital evidence for clinicians to predict diseases and check the curative effect of diagnosis and treatment. To further improve the prediction accuracy of blood pressure, this paper proposes a combined prediction model of blood pressure based on coritivity theory and photoplethysmography. METHOD: First of all, we extract eight features of photoplethysmogram, followed by using eight machine learning prediction algorithms, such as K-nearest neighbor, classification and regression trees, and random forest, to predict systolic blood pressure. Secondly, aiming at the problem of sub-model selection of combination forecasting model, from the point of graph theory, we construct an undirected network graph G, the results of each single prediction model constitute a vertex set. If the maximum mutual information coefficient between vertices is greater than or equal to 0.69, the vertices are connected by edges. The maximum core of graph G is a submodel of the combinatorial model. RESULTS: According to the definition of core and coritivity, the maximum core of G is random forest regression and Gaussian kernel support vector regression model. The results show that the SDP estimation error of the combined prediction model based on random forest regression and Gaussian kernel support vector regression is 3.56 ±5.28mmhg, which is better than other single models and meets the AAMI standards. CONCLUSION: The combined model determined by core and coritivity has higher prediction performance for blood pressure.

LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.

Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.

Solving 0-1 Integer Programming Problem Based on DNA Strand Displacement Reaction Network.

Chemical reaction networks (CRNs) based on DNA strand displacement (DSD) can be used as an effective programming language for solving various mathematical problems. In this paper, we design three chemical reaction modules by using the DNA strand displacement reaction as the basic principle, with a weighted reaction module, sum reaction module, and threshold reaction module. These modules are used as basic elements to form chemical reaction networks that can be used to solve 0-1 integer programming problems. The problem can be solved through the three steps of weighting, sum, and threshold, and then the results of the operations can be expressed through a single-stranded DNA output with fluorescent molecules. Finally, we use biochemical experiments and Visual DSD simulation software to verify and evaluate the chemical reaction networks. The results have shown that the DSD-based chemical reaction networks constructed in this paper have good feasibility and stability.

Dynamically NAND gate system on DNA origami template.

Molecular logic gates play an important role in many fields and DNA-based logic gates are the basis of DNA computers. A dynamically NAND gate system on the DNA origami template is established in this paper. Naturally, the system is stable in solution without any reaction. Different logical values are mapped into different DNA input strands. When logical values are entered into the system, the corresponding DNA input strands undergo a directed hybridization chain reaction (HCR) at corresponding positions on the DNA origami template. The operation results are identified by disassembly between the nanogold particles (AuNPs) and DNA origami template. The nanogold particles remain on the DNA origami template, indicating that the result is true; The nanogold particles are dynamically separated from the DNA origami template, indicating that the result is false. The simulation of the system through Visual DSD shows that the reaction strictly followed the designed direction, and no error products are generated during the reaction. These simulation results show that the system has the advantages of feasibility, stability and intelligence.

DNA computation model to solve 0-1 programming problem.

0-1 programming problem is an important problem in opsearch with very widespread applications. In this paper, a new DNA computation model utilizing solution-based and surface-based methods is presented to solve the 0-1 programming problem. This model contains the major benefits of both solution-based and surface-based methods; including vast parallelism, extraordinary information density and ease of operation. The result, verified by biological experimentation, revealed the potential of DNA computation in solving complex programming problem.

A Chinese Postman Problem based on DNA computing.

DNA computing is a novel method for solving a class of intractable computational problems, in which the computing can grow exponentially with the problem size. Up to now, many accomplishments have been achieved to improve its performance and increase its reliability. A Chinese Postman Problem has been solved by means of molecular biology techniques in the paper. A small graph was encoded in molecules of DNA, and the "operations" of the computation were performed with standard protocols and enzymes. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.