A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer.

OBJECTIVE: The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients. METHODS: This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets. RESULTS: The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0. CONCLUSION: Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

Objective: This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses. Methods: The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores. Results: A total of 62 DEGs associated with CRC lymph node metastasis were obtained. Among the 20 hub genes identified via PPI, only calcium-activated chloride channel regulator 1 (CLCA1) expression was down-regulated in lymph node metastasis, and the rest were up-regulated. A total of nine characteristic genes associated with CRC lymph node metastasis (KIF1A, TMEM59L, CLCA1, COL9A3, GDF5, TUBB2B, STMN2, FOXN1, and SCN5A) were screened using the LASSO regression method. The nine characteristic genes were significantly related to different kinds of immune cell infiltration, from which three survival-related genes (TMEM59L, CLCA1, and TUBB2B) were screened. A multi-factor COX regression showed that the risk scores obtained from TMEM59L, CLCA1, and TUBB2B were independent prognostic factors. Immunohistochemical validation was performed in tissue samples from patients with rectal and colon cancer. Conclusion: TMEM59L, CLCA1, and TUBB2B were independent prognostic factors associated with lymphatic metastasis of CRC.

Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer.

This study aimed to develop a radiomics model for predicting lateral lymph node (LLN) metastasis in rectal cancer patients using MR-T2WI and CT images, and assess its clinical value. This prospective study included rectal cancer patients with complete MR-T2WI and portal enhanced CT images who underwent LLN dissection at Tianjin Union Medical Center between June 2017 and November 2022. Primary lesions and LLN were segmented using 3D slicer. Radiomics features were extracted from the region of interest using pyradiomics in Python. Least absolute shrinkage and selection operator algorithm and backward stepwise regression were employed for feature selection. Three LLN metastasis radiomics prediction models were established via multivariable logistic regression analysis. The performance of the model was evaluated using receiver operating characteristic curve analysis, and the area under the curve (AUC), sensitivity, specificity were calculated for the training, validation, and test sets. A nomogram was constructed for visualization, and decision curve analysis (DCA) was performed to evaluate clinical value. We included 94 eligible patients in the analysis. For each patient, we extracted a total of 1344 radiomics features. The CT combined with MR-T2WI model had the highest AUC for all sets compared to CT and MR-T2WI models. AUC values for the CT combined with MR-T2WI model in the training, validation, and test sets were 0.957, 0.901, and 0.936, respectively. DCA revealed high prediction value for the combined MR-T2WI and CT model. A radiomics model based on CT and MR-T2WI data effectively predicted LLN metastasis in rectal cancer patients preoperatively.

Network Pharmacology-Based Analysis of the Potential Biological Mechanisms of Coix Seed against Colorectal Cancer.

Objective: The aim of this study was to explore the potential biological mechanisms of coix seed in the treatment of colorectal cancer (CRC) based on network pharmacology analysis. Methods: The active components of coix seed and their potential action targets were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The disease targets related to CRC were obtained from the DisGeNET database. The intersection targets of the drug targets and disease targets were selected, and a component-target-disease network was built using Cytoscape 3.8.0 tool. A global network of the core target protein interactions was constructed using String database. Biological function analysis and pathway enrichment analysis of core targets were conducted to explore the potential. Results: A total of nine active components were obtained from the TCMSP database corresponding to 37 targets. Further analysis showed that 18 overlapping targets were associated with CRC. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted based on the 18 targets and 11 significantly enriched signaling pathways implicated in CRC were identified. Conclusion: The multicomponent and multitarget characteristics of coix seed are preliminarily verified, and the potential biological mechanisms of coix seed in the treatment of CRC are predicted, which provides a theoretical basis for the experimental research.

Potential role of mitochondria in synoviocytes.

Synoviocytes are located in the synovium lining layer, which is composed of macrophage-like synoviocytes (MLS) and fibroblast-like synoviocytes (FLS) with different characteristics. Mitochondria, which exist in most cells, are two membrane-covered organelles. In addition to providing the necessary ATP for synoviocytes, mitochondria are involved in the regulation of redox homeostasis and the integration of synoviocytes death signals. In recent years, mitochondrial dysfunction has been found in rheumatoid arthritis (RA) and osteoarthritis (OA). Interestingly, recent studies have started uncovering that mitochondria that were previously reported to play a role in chondrocytes or immune cells, but not known to have pronounced roles in synoviocytes, can actually play crucial roles in the regulation of the pathological properties of the synoviocytes. The purpose of this review is to summarize our current understanding of the key role of mitochondria in synoviocytes, including mitochondrial dysfunction in synoviocytes can induce and aggravate inflammatory responses and changes in mitochondrial structure and function with the involvement of multiple cytokines, signal pathway, and hypoxic state of synovial tissue alter the response of synoviocytes to apoptotic stimulation. Also, mitochondrial abnormalities in synoviocytes promote the synoviocytes invasion and proliferation.

Bundled Causal History Interaction.

Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of pH in an observed stream chemistry system. In the studied catchment, the dynamics of pH is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems.

Biomechanical study of medial meniscus after posterior horn injury: a finite element analysis.

We established an effective finite element model of knee joint for observation of stress and displacement of meniscus related changes after medial meniscus injury. Different types of medial meniscus injury can lead to varied meniscus stress and displacement changes. Stress and displacement concentration were found in fissure tip of meniscus tear compared to normal meniscus. The posterior horn injury of medial meniscus may initiate combined injury of medial meniscus posterior horn (MMPH) and that of medial meniscus body, and combined injury of MMPH and that of lateral meniscus anterior horn; fissure expansions regarding horizontal fissure, longitudinal fissure and grip-shaped fissure of MMPH were spotted.

Information transfer from causal history in complex system dynamics.

In a multivariate evolutionary system, the present state of a variable is a resultant outcome of all interacting variables through the temporal history of the system. How can we quantify the information transfer from the history of all variables to the outcome of a specific variable at a specific time? We develop information theoretic metrics to quantify the information transfer from the entire history, called causal history. Further, we partition this causal history into immediate causal history, as a function of lag τ from the recent time, to capture the influence of recent dynamics, and the complementary distant causal history. Further, each of these influences are decomposed into self- and cross-feedbacks. By employing a Markov property for directed acyclic time-series graph, we reduce the dimensions of the proposed information-theoretic measure to facilitate an efficient estimation algorithm. This approach further reveals an information aggregation property, that is, the information from historical dynamics are accumulated at the preceding time directly influencing the present state of variable(s) of interest. These formulations allow us to analyze complex inter-dependencies in unprecedented ways. We illustrate our approach for: (1) characterizing memory dependency by analyzing a synthetic system with short memory; (2) distinguishing from traditional methods such as lagged mutual information using the Lorenz chaotic model; (3) comparing the memory dependencies of two long-memory processes with and without the strange attractor using the Lorenz model and a linear Ornstein-Uhlenbeck process; and (4) illustrating how dynamics in a complex system is sustained through the interactive contribution of self- and cross-dependencies in both immediate and distant causal histories, using the Lorenz model and observed stream chemistry data known to exhibit 1/f long memory.

Interactions of information transfer along separable causal paths.

Complex systems arise as a result of interdependences between multiple variables, whose causal interactions can be visualized in a time-series graph. Transfer entropy and information partitioning approaches have been used to characterize such dependences. However, these approaches capture net information transfer occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within a subgraph of interest through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [Phys. Rev. E 92, 062829 (2015)PLEEE81539-375510.1103/PhysRevE.92.062829] to develop a framework for quantifying information partitioning along separable causal paths. Momentary information transfer along causal paths captures the amount of information transfer between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique, and redundant information transfer through separable causal paths. Through a graphical model, we analyze the impact of the separable and nonseparable causal paths and the causality structure embedded in the graph as well as the noise effect on information partitioning by using synthetic data generated from two coupled logistic equation models. Our approach can provide a valuable reference for an autonomous information partitioning along separable causal paths which form a causal subgraph influencing a target.