Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for this cohort. DESIGN: Utilizing a retrospective design, this investigation leverages data extracted from the Surveillance, Epidemiology, and End Results (SEER) database, specifically the January 2024 subset, to conduct the analysis. PARTICIPANTS: The study cohort consists of 15,775 patients diagnosed with HCC as identified within the SEER database, spanning 2016 to 2020. METHOD: In the construction of the diagnostic model, recursive feature elimination (RFE) is employed for variable selection, incorporating five critical predictors: age, tumor size, radiation therapy, T-stage, and serum alpha-fetoprotein (AFP) levels. These variables are the foundation for a stacking ensemble model, which is further elucidated through Shapley Additive Explanations (SHAP). Conversely, the prognostic model is crafted utilizing stepwise backward regression to select pertinent variables, including chemotherapy, radiation therapy, tumor size, and age. This model culminates in the development of a prognostic nomogram, underpinned by the Cox proportional hazards model. MAIN OUTCOME MEASURES: The outcome of the diagnostic model is the occurrence of DLNM in patients. The outcome of the prognosis model is determined by survival time and survival status. RESULTS: The integrated model developed based on stacking demonstrates good predictive performance and high interpretative variability and differentiation. The area under the curve (AUC) in the training set is 0.767, while the AUC in the validation set is 0.768. The nomogram, constructed using the Cox model, also demonstrates consistent and strong predictive capabilities. At the same time, we recognized elements that have a substantial impact on DLNM and the prognosis and extensively discussed their significance in the model and clinical practice. CONCLUSION: Our study identified key predictive factors for DLNM and elucidated significant prognostic indicators for HCC patients with DLNM. These findings provide clinicians with valuable tools to accurately identify high-risk individuals for DLNM and conduct more precise risk stratification for this patient subgroup, potentially improving management strategies and patient outcomes.

Local delivery of gaseous signaling molecules for orthopedic disease therapy.

Over the past decade, a proliferation of research has used nanoparticles to deliver gaseous signaling molecules for medical purposes. The discovery and revelation of the role of gaseous signaling molecules have been accompanied by nanoparticle therapies for their local delivery. While most of them have been applied in oncology, recent advances have demonstrated their considerable potential in diagnosing and treating orthopedic diseases. Three of the currently recognized gaseous signaling molecules, nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), are highlighted in this review along with their distinctive biological functions and roles in orthopedic diseases. Moreover, this review summarizes the progress in therapeutic development over the past ten years with a deeper discussion of unresolved issues and potential clinical applications.

Strategies to prevent, curb and eliminate biofilm formation based on the characteristics of various periods in one biofilm life cycle.

Biofilms are colonies of bacteria embedded inside a complicated self-generating intercellular. The formation and scatter of a biofilm is an extremely complex and progressive process in constant cycles. Once formed, it can protect the inside bacteria to exist and reproduce under hostile conditions by establishing tolerance and resistance to antibiotics as well as immunological responses. In this article, we reviewed a series of innovative studies focused on inhibiting the development of biofilm and summarized a range of corresponding therapeutic methods for biological evolving stages of biofilm. Traditionally, there are four stages in the biofilm formation, while we systematize the therapeutic strategies into three main periods precisely:(i) period of preventing biofilm formation: interfering the colony effect, mass transport, chemical bonds and signaling pathway of plankton in the initial adhesion stage; (ii) period of curbing biofilm formation:targeting several pivotal molecules, for instance, polysaccharides, proteins, and extracellular DNA (eDNA) via polysaccharide hydrolases, proteases, and DNases respectively in the second stage before developing into irreversible biofilm; (iii) period of eliminating biofilm formation: applying novel multifunctional composite drugs or nanoparticle materials cooperated with ultrasonic (US), photodynamic, photothermal and even immune therapy, such as adaptive immune activated by stimulated dendritic cells (DCs), neutrophils and even immunological memory aroused by plasmocytes. The multitargeted or combinational therapies aim to prevent it from developing to the stage of maturation and dispersion and eliminate biofilms and planktonic bacteria simultaneously.

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity.

With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.