Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

OBJECTIVE: To explore the current status, evolution, hot topics, and future research trends in the field of burn-related sepsis research through a visual analysis of literature. METHODS: A bibliometric method was employed to retrieve articles related to burn-related sepsis from January 1, 1994, to May 16, 2024, in the China National Knowledge Infrastructure (CNKI) and the Web of Science database. The CiteSpace 6.3.R1 software was used to analyze the retrieved literature. The number of publications, authors, countries, and institutions in both Chinese and English literature was statistically analyzed. Co-occurrence analysis, clustering analysis, and co-citation analysis of keywords were performed. RESULTS: A total of 1 090 articles from the CNKI database and 1 143 articles from the Web of Science database were retrieved. Over the past 20 years, the volume of Chinese publications has remained stable, although there has been a slight decline in the past two years. In contrast, the number of English publications, after a period of growth, showed a sharp decline over the past three years. In Chinese literature, 1 457 authors published articles on burn-related sepsis as first authors, with 14 core authors publishing four or more articles. In English literature, 98 authors published articles on burn-related sepsis as first authors. Research on burn-related sepsis was conducted by 76 countries, with the United States having the most collaborations and publications. Globally, 1 349 institutions published articles on burn-related sepsis, with the top institutions being the First Affiliated Hospital of the PLA General Hospital (8 articles) for Chinese literature and the University of Texas Medical Branch (57 articles) for English literature. In the co-occurrence analysis, 208 Chinese keywords and 211 English keywords were included. Excluding keywords related to search terms, the top five most frequent keywords in Chinese literature were burn, sepsis, infection, severe burn, and procalcitonin; the top five most frequent keywords in English literature were sepsis, septic shock, mortality, injury, and burn injury. Chinese keyword analysis identified six clusters, with the largest being sepsis, followed by procalcitonin, infection, and severe burn. English keyword analysis identified seven clusters, with the largest being expression, followed by epidemiology, inhalation injury, and acute kidney injury. The persistent clusters in Chinese literature were procalcitonin, with recent emerging nodes being severe burn, inflammatory response, platelets, and predictive value. In English literature, the persistent clusters were inhalation injury and nitric oxide, with recent emerging nodes being continuous renal replacement therapy, hemorrhagic shock, and early enteral nutrition. The longest-lasting emergent keyword in Chinese literature was delayed resuscitation (2003-2010), with the highest emergent strength being severe burn. In English literature, the longest-lasting emergent keywords, each lasting five years, were nitric oxide (2007-2012), management (2019-2024), and impact (2019-2024), with the highest emergent strength being thermal injury. CONCLUSIONS Research on burn-related sepsis has shifted from focusing on early studies on pathogenesis and mortality to focus on prevention, treatment, and early diagnosis. Future research is expected to focus on early diagnosis and risk factors of burn-related sepsis.
Burns/complications* ; Sepsis/etiology* ; Humans ; Bibliometrics ; China

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

TROP-2, a tumor-associated antigen, has been implicated in the progression of various epithelial tumors. Due to its favorable expression profile, TROP-2 has emerged as a promising target for antibody-drug conjugates (ADCs) based anti-tumor therapies. Although ADCs have shown efficacy in cancer treatment, their application in solid tumors is hindered by their high molecular weight, poor tumor penetration, and release of cytotoxic molecules. Therefore, a recombinant immunotoxin was developed based on a shark-derived variable domain of immunoglobulin new antigen receptor (VNAR) antibody. VNARs are only one-tenth the size of IgG antibodies and possess remarkable tissue penetration capabilities and high stability. In this study, a shark VNAR phage display library was created, leading to the identification of shark VNAR-5G8 that targets TROP-2. VNAR-5G8 exhibited a high affinity and cellular internalization ability towards cells expressing high levels of TROP-2. Epitope analysis revealed that VNAR-5G8 recognizes a hidden epitope consisting of CRD and TY-1 on TROP-2. Subsequently, VNAR-5G8 was fused with a truncated form of Pseudomonas exotoxin (PE38) to create the recombinant immunotoxin (5G8-PE38), which exhibited significant anti-tumor activity in vitro and in vivo. Overall, this study highlights the promise of 5G8-PE38 as a valuable candidate for cancer therapy.

The construction of a medication safety culture is important for medication safety management and rational drug use.The construction of medication safety culture standards is formulated based on relevant national policies and regulations,accreditation standards for hospitals,expert opinions,the current situation,and the development trend of the healthcare industry.With scientificity,general applicability,instructive guidance,and practicality,they standardized basic requirements,management processes,and improvement of the construction of medication safety culture.To facilitate understanding and the implementation of the standards,we describe the process of standards formulation and explain the key points of the standards.