Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.

MicroRNA-192-5p, a liver-enriched miRNA downregulated in hepatocellular carcinoma (HCC), is a promising biomarker, but its clinical use is limited by technical challenges in detecting low-abundance plasma miRNAs. This study innovatively uses droplet digital PCR (ddPCR) with locked nucleic acid (LNA)-modified probes to develop an ultrasensitive and standardized method for miRNA quantification in liquid biopsies. Following Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines, seven primer-probe combinations were screened by qPCR, and one with the lowest Ct variability (Ct <35) was selected. LNA-modified Probe P-2 was designed to enhance target hybridization. Reaction conditions were optimized to 1 μM primers, 300 nM probes, and 55 cycles. Analytical validation included trueness, precision, sensitivity, linear range, and interference testing. Plasma from 87 HCC patients and 57 controls was analyzed, and a logistic model combining miR-192-5p, AFP, and AFU was evaluated. The LNA probe improved positive droplet counts by 32 %. The dPCR showed excellent precision (intra-batch CV 2.31-21.63 %, inter-batch 17.54 %) and trueness (R=0.92 vs. RT-qPCR). Sensitivity thresholds were LoB=1.75, LoD=3.33, LoQ=13.45 copies/μL, with linear range 13.45-129,693 copies/μL (R2=0.9965). It tolerated low hemoglobin and triglycerides but was affected by bilirubin. HCC patients had lower miR-192-5p (444.2 vs. 753.5 copies/μL, p<0.001), with AUC=0.70. The multi-marker model had AUC=0.88. This LNA-optimized ddPCR assay resolves miRNA liquid biopsy barriers. The combinatorial model outperforms single biomarkers, offering a clinical tool for the precise quantification of HCC-specific miRNAs. Standardized workflows ensure reproducibility, and multicenter studies are needed for validation.

This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem of order-picking optimization, leveraging the latest in attention-based deep learning techniques. The attention model is combined with Apriori and the Adaptive Large Neighborhood Search (ALNS) algorithm to solve the bilevel combinatorial optimization model for mobile shelves. Compared to existing methods, our innovative model shows superior performance, offering significant potential in warehousing solutions.

In 2020, Coregliano and Razborov introduced a general framework to study limits of combinatorial objects, using logic and model theory. They introduced the abstract chromatic number and proved/reproved multiple Erdős-Stone-Simonovits-type theorems in different settings. In 2022, Coregliano extended this by showing that similar results hold when we count copies of $K_t$ instead of edges. Our aim is threefold. First, we provide a purely combinatorial approach. Second, we extend their results by showing several other graph parameters and other settings where Erdős-Stone-Simonovits-type theorems follow. Third, we go beyond determining asymptotics and obtain corresponding stability, supersaturation, and sometimes even exact results.

With the rapid development of wireless communication has made efficient spectrum assignment a crucial factor in enhancing network performance. As a combinatorial optimization model for channel assignment, the radio labeling is recognized as an NP-hard problem. Therefore, converting the spectrum assignment problem into the radio labeling of graphs and studying the radio labeling of specific graph classes is of great significance. For G, a radio labeling φ : V ( G ) → { 0 , 1 , 2 , … } is required to satisfy | φ ( u ) − φ ( v ) | ≥ diam ( G ) + 1 − d G ( u , v ) , where diam ( G ) and d G ( u , v ) are diameter and distance between u and v. For a radio labeling φ, its span is defined as the largest integer assigned by φ to the vertices of G; the radio labeling specifically denotes the labeling with the minimal span among possible radio labeling. The strong product is a crucial tool for constructing regular networks, and studying its radio labeling is necessary for the design of optimal channel assignment in wireless networks. Within this manuscript, we discuss the radio labeling of strong prismatic network with star, present the relevant theorems and examples, and propose a parallel algorithm to improve computational efficiency in large-scale network scenarios.

BackgroundMigrasomes, a newly identified subtype of extracellular vesicles generated during cell migration, play crucial roles in tumor microenvironment modulation. However, their systematic characterization in lung adenocarcinoma (LUAD) remains unexplored. This study is aimed at deciphering migrasome‐related molecular features and their clinical significance through multiomics integration.MethodsWe integrated bulk transcriptomes (541 LUAD samples from TCGA/GEO) with single‐cell RNA‐seq (GSE156632). Migrasome‐related genes (MIGgenes) were identified through WGCNA and differential expression analysis. A machine learning framework incorporating 10 algorithms generated 101 combinatorial models, with the optimal prognostic signature (MIGsig) selected via 10‐fold cross‐validation. Biological mechanisms were investigated through ssGSEA, TME analysis, and in vitro validation.ResultsOur analysis revealed significant migrasome activity enrichment in endothelial cells and fibroblasts, with 115 cross‐omics MIGgenes identified including 31 prognostic markers. The Lasso–Cox‐derived 3‐gene signature (GSTM5/DNASE1L3/PDGFB) demonstrated robust predictive performance (training set C index = 0.703; validation set GSE50081 AUC = 0.678). The low‐MIGsig group exhibited characteristic “hot tumor” features, including elevated immune infiltration and higher tumor mutational burden, and significantly improved immunotherapy response rates in the IMvigor210 cohort. Finally, MIGsig‐related genes were further validated by in vitro experiments and public database.ConclusionsThis study establishes the first migrasome‐based prognostic model for LUAD, demonstrating both independent survival prediction capability and clinical utility for identifying immunotherapy beneficiaries. The MIGsig signature provides novel biological insights into migrasome‐mediated tumor–immune interactions and represents a promising tool for precision oncology applications in LUAD management.

Combining conventional hemogram and reticulocyte metrics enhances iron deficiency detection in asymptomatic individuals.

Acute stress uncovers latent β2-adrenergic receptor and Corticotropin Releasing Factor interactions in the ventral Bed Nucleus of the Stria Terminalis critical for long-term stress-avoidance behavior.

Plastic deformation as a phase transition: A combinatorial model of plastic flow in copper single crystals

To address the challenges of low interpretability in model selection and solve limited cross-scenario adaptability in power load forecasting, this paper proposes a knowledge graph-guided robust optimization recommendation (KGROR) framework. By leveraging structured knowledge representation and a two-layer robust optimization strategy, KGROR enables scenario-adaptive model recommendation for power load forecasting. To this end, we firstly construct a knowledge graph for power load forecasting models, systematically organizing their application scenarios and performance metrics into a unified representation. Then, we develop a two-layer robust combinatorial optimization model that evaluates candidate models from two perspectives: scenario compatibility and predictive performance. Finally, we introduce an interactive verification mechanism, by dynamically adjusting the forecasting model and the parameters used, the optimal performance and robustness of the model in different scenarios are calculated, and the final recommendation model is output. Experiments have verified the significant advantages of the proposed framework.

With the vigorous development of China's economy, the internationalization level of Renminbi (RMB) is still improving, and the offshore RMB market is also expanding. Compared with onshore RMB, the price of offshore RMB is formed by free bidding of buyers and sellers. Market supply and demand are two extremely important factors affecting the exchange rate of offshore RMB, and the fluctuation of exchange rate is relatively free, so the offshore RMB exchange rate is relatively volatile. Under such circumstances, it is of great significance to forecast the offshore RMB exchange rate. This review focuses on the application of combinatorial models related to machine learning in offshore RMB exchange rate forecasting, and systematically combs the data types, modeling methods, and related forecasting performance involved in recent research. This paper evaluates the application effectiveness of the machine learning fusion Autoregressive Integrated Moving Average (ARIMA) model, supervised learning based on neural network and random forest, and the Long Short-Term Memory-Temporal Convolutional Network-Convolutional Neural Network (LSTM-TCN-CNN) hybrid model in offshore RMB exchange rate forecasting, compares and analyzes the advantages of each model, and summarizes the applicable scenarios of different models. The results show that the machine learning fusion ARIMA model is suitable for the prediction of time series with obvious rules and a small amount of data, the combination model of random forest and neural network is suitable for the prediction of structured data, and the LSTM-TCN-CNN model is suitable for the prediction of multivariate time series with a large amount of data and high dimension.

PIPI-C: A Combinatorial Optimization Framework for Identifying Post-translational Modification Hot-spots in Mass Spectrometry Data

Cellular senescence, an inevitable phase in the cellular lifecycle, is increasingly implicated in cancer development. Clear cell renal cell carcinoma (ccRCC), a lethal malignancy of the urinary system, underscores the need for senescence-based risk models. Through single-cell analysis, we identified senescent cells within ccRCC tumors and delineated their distinct biological features. We then integrated ten machine learning algorithms—plsRcox, Ridge, Enet, CoxBoost, Lasso, StepCox, RSF, SuperPC, GBM, and survivalSVM—generating 101 combinatorial models via pairwise integration. The optimal Lasso-StepCox model was selected based on the highest mean concordance index (C-index), yielding a minimized senescence-related gene signature of only 9 genes (significantly below the typical 15–30-gene range). This signature formed the basis of a senescence-related scoring model (SRSM) for ccRCC patient survival assessment. Patients with high SRSM exhibited significantly poorer survival (P < 0.001), enhanced oxidative phosphorylation, and an immunosuppressive tumor microenvironment (TME) characterized by elevated regulatory T cell (Treg) infiltration. In vitro validation confirmed that NME2 knockdown suppressed ccRCC proliferation and invasion. Collectively, the SRSM framework provides a precise tool for prognostic stratification and therapeutic targeting in ccRCC.Therefore, to bridge the gap between the intricate landscape of cellular senescence revealed by scRNA-seq and the pressing clinical need for robust prognostic tools, we aimed to develop a quantifiable senescence-related scoring model (SRSM). By integrating multiple machine learning algorithms, we sought not only to achieve superior predictive accuracy but also to derive a minimized gene signature for enhanced clinical applicability, ultimately translating the biological insights of senescence into a precise prognostic framework for ccRCC.

SUMMARY Large-scale geodetic monitoring of volcanic earthquakes is essential for understanding the physical mechanisms governing volcanic activity and magma migration. Recently, a significant earthquake swarm occurred around the Scotia plate. Geodetic data of 14 permanent GNSS stations on the Antarctic Peninsula, King George Island, the South Sandwich Islands and South America were collected and analysed, to monitor the crustal deformation of King George Island, the expansion of Bransfield Strait, the drift of Scotia plate and sea level anomalies. Tidal data from four permanent tide stations were analysed to monitor sea level anomalies. Results showed that after earthquakes the King George Island’s movement speed increased tenfold and its direction altered by 90 degrees. Land surface fluctuations in southeast King George Island were observed a year before the earthquakes, followed by continuous uplift. A combinatorial model including a point pressure source and expanding dike fit well with new geodetic monitoring data, revealing the impact of volcanic activity on this region. Geodetic monitoring and modelling quantitatively depicted the pre-seismic, co-seismic and post-seismic phases of geological changes, providing new evidence and insights into the complex geological structures.

To meet sales targets with limited resources, business-to-business service firms must prioritize promising opportunities within large pipelines. Yet, both theory and practice indicate that such decisions often rely on intuition or ad hoc rules, resulting in suboptimal sales and operations planning. Drawing on the relationship management and organizational buying literature, we develop a theory-informed sales-operations framework that links buyer typology (e.g., new vs. rebid) and opportunity characteristics (e.g., size and relationship strength) to the firm’s bid and win decisions. Using archival data from a global on-site services provider encompassing 4,574 opportunities across 23 countries (2010–2021), we document a persistent tradeoff: while low-risk, relationship-based opportunities yield higher win probabilities, they are insufficient to achieve regional sales goals. We address this challenge through an ensemble machine-learning model that predicts win likelihood and a combinatorial optimization model that allocates bidding capacity strategically. The integrated framework improves predictive accuracy by 11% and could have increased realized sales by 21% while bidding on 38% fewer opportunities. Extensions incorporating stochastic programming and a Heckman-style two-stage correction enhance the framework’s robustness to uncertainty and data selection bias, providing managers with a rigorous, data-driven approach to opportunity management.

Serological biomarker models composed of luteinizing hormone, kisspeptin, vitamin D and estradiol, and their clinical test value in girls

IntroductionParkinson’s disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra and pathological aggregation of α-synuclein. Although existing therapies alleviate clinical symptoms, however, due to the unclear etiology, it remains impossible to completely halt this process through currently available approaches. This study aims to elucidate molecular mechanisms underlying PD pathogenesis and identify novel candidate biomarkers.MethodsWe integrated bioinformatics analysis of GEO datasets to pinpoint pivotal genes in PD progression from metabolic and stem cell perspectives. Hub genes were empirically validated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting in animal specimens. A combinatorial predictive model was constructed and evaluated via nomogram. Single-cell RNA sequencing (scRNA-seq) data from PD cohorts were interrogated to localize cell-type-specific expression patterns of signature genes and delineate subtype-specific mechanisms. Our analytical workflow entailed: differential expression screening, functional enrichment, protein–protein interaction (PPI) network construction, and machine learning (ML) algorithms.ResultsOur study reveals BMX and CA4 as key hub genes. Experimental confirmation of their dysregulation in in vivo PD models. Development of a high-accuracy PD prediction model (AUC >0.6). scRNA-seq analysis identified an NK cell subtype (NK1) enriched with CA4 expression. KEGG pathway analysis of NK1 marker genes implicated their role in neuroimmune crosstalk during PD progression.DiscussionThis work establishes a novel CA4-NK1-PD axis, providing a potential therapeutic entry point for future interventions.

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive subtype of kidney cancer. This study aimed to construct a prognostic model for ccRCC based on glycosyltransferase genes, which play important roles in cell processes like proliferation, apoptosis. Glycosyltransferase genes were collected from four public databases and analyzed using RNA-seq data with clinical information from three ccRCC datasets. Prognostic models were constructed using eight machine learning algorithms, generating a total of 117 combinatorial algorithm models, and the StepCox[forward]+Ridge model with the highest predictive accuracy (C-index = 0.753) which selected and named the Glycosyltransferases Risk Score (GTRS) model. The GTRS effectively stratified patients into high- and low-risk groups with significantly different overall survival and maintained robust performance across TCGA, CPTAC, and E-MTAB1980 cohorts (AUC > 0.75). High-risk patients exhibited higher tumor mutational burden, immunosuppressive microenvironment, and poorer response to immunotherapy. TYMP and GCNT4 were experimentally validated as key genes, functioning as oncogenic and tumor-suppressive factors. In conclusion, GTRS serves as a reliable prognostic tool for ccRCC and provides mechanistic insights into glycosylation-related tumor progression.

Abstract With the increasing frequency of disruptions in construction projects, the traditional Critical Path Method (CPM) shows limitations in handling schedule disturbances due to its static structure and inflexible adjustment mechanisms. This paper proposes a schedule sensitivity assessment method under disruptive conditions, based on a combinatorial thinking model. By representing the project network with matrices and scanning multi-task combinations, we introduce two indices-Schedule Sensitivity Index (SSI) and Cost Sensitivity Index (CSI)-to quantify how combinations of tasks affect overall project objectives under different disturbance scenarios. A simulated case based on a construction project is used to illustrate the approach. For example, under combined schedule-cost variation, the AGF task group induces the greatest schedule delay, while the ILM group exhibits the lowest overall sensitivity. The proposed method indicates potential to support decision-making in resource allocation and may improve adaptability the adaptability of construction schedule management under uncertain and dynamic conditions.

Abstract We consider a graph theory problem motivated by the self-assembly of DNA graph structures using branched junction molecules with flexible arms (called ‘tiles’ in the combinatorial model). More precisely, we want to determine a set of tiles that realizes a target graph G using the minimum number of bond-edge types so that no graph with order smaller than $$\vert V(G)\vert$$ can be realized; the parameter of interest is denoted by $$B_2(G)$$ . We present an approach that provides an upper bound for $$B_2(G)$$ using certain multipartite subgraphs of G. We provide some numerical conditions characterizing such multipartite graphs in terms of the degree of their vertices. Then, we apply our method to the graphs corresponding to the Platonic solids.