Lung cancer is the leading cause of cancer-related deaths worldwide， and tumor metastasis is a key factor contributing to the mortality of most lung cancer patients. Aberrant activation of epithelial-mesenchymal transition （EMT） is a major driver of lung cancer progression and metastasis. EMT is characterized by the loss of apical-basal polarity and intercellular adhesion in highly differentiated， polarized， and organized epithelial cells， which acquire motility， migratory potential， and invasive properties. During this process， cells undergo cytoskeletal remodeling and transform into a mesenchymal phenotype， accompanied by associated changes in cellular markers. The EMT process is highly complex and is tightly regulated by intricate networks involving multiple transcription factors， post-translational controls， epigenetic modifications， and non-coding RNAs. Therefore， therapies targeting the mechanisms of malignant transformation and their associated pathways in lung cancer are of significant clinical importance. In recent years， EMT has attracted increasing attention as a potential target for cancer therapy. Chinese medicine， with its characteristics of multi-target action， low side effects， and good therapeutic efficacy， has demonstrated an important role in anticancer treatment. A series of studies have investigated the role of Chinese medicine in inhibiting EMT in lung cancer. Active ingredients of Chinese medicine， including flavonoids， glycosides， phenols， terpenoids， saccharides， and alkaloids， as well as Chinese medicine compound formulas， have shown significant regulatory effects on EMT. Their mechanisms mainly involve multiple pathways， targets， and links， including signaling pathways， exosomes， microRNAs （miRNAs）， and the tumor-associated immune microenvironment. This article summarizes the mechanisms by which EMT promotes malignant tumor progression and reviews the current research on how Chinese medicine active ingredients， monomers， and compound formulas inhibit EMT and suppress lung cancer cell migration and invasion. This study is expected to provide comprehensive theoretical information for basic and translational research on lung cancer.

ObjectiveTo explore the feasibility of constructing a programmed death receptor-1（PD-1） molecular probe for non-invasive micro-positron emission tomography/computed tomography （Micro-PET/CT） imaging of PD-1 protein in mouse S180 sarcoma. MethodsA transgenic PD-1 C57 S180 sarcoma mouse model was established using the S180 sarcoma cell injection. Furthermore， ¹²⁴I-anti-PD-1 monoclonal antibody probe was synthesized. 18.5 MBq of the ¹²⁴I-anti-PD-1 probe was injected into the tail vein of transgenic PD-1 C57 mice. Subsequently， S180 sarcoma was imaged using Micro-PET/CT. ResultsStudy successfully established a transgenic PD-1 C57 S180 sarcoma mouse model. Immunohistochemical （IHC） results showed PD-1 protein expression in S180 sarcoma. Micro-PET/CT imaging successfully visualized the PD-1 protein receptor in S180 sarcoma at different time points （20， 48， 72， and 120 h） after probe injection. ConclusionThe ¹²⁴I-anti-PD-1 monoclonal antibody molecular probe successfully targets the PD-1 receptor in S180 sarcoma of transgenic PD-1 C57 mice， and presents clear Micro-PET/CT immunoassay results， thus it potentially enables the non-invasive screening of patients with PD-1 positive malignant tumors.

OBJECTIVE To develop a prediction model for durable clinical benefit （DCB） in patients with advanced esophageal squamous cell carcinoma （ESCC） receiving programmed death-1 （PD-1） inhibitor. METHODS The clinical data of patients with advanced ESCC who received PD-1 inhibitor in Jieyang People’s Hospital were retrospectively collected between January 2020 to December 2023. Predictors were screened by least absolute shrinkage and selection operator （Lasso） regression， and a multivariable Logistic regression model was developed to predict DCB. A nomogram was constructed based on the model. Internal validation of the prediction model was performed by using the Bootstrap method， and the model was evaluated by the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis. RESULTS A total of 91 patients with advanced ESCC were included. The results of Lasso regression combined with Logistic regression analysis indicated that the baseline lymphocyte monocyte ratio （LMR） [odds ratio （OR）＝1.97， 95% confidence interval （CI）： 1.15-3.36， P＝0.013]， albumin （ALB） content （OR＝1.35， 95%CI： 1.13-1.60， P＜0.001）， body mass index （BMI） category 1 [normal vs. low： OR＝ 0.28， 95%CI （0.09-0.96）， P＝0.042]， BMI category 2 [overweight-obesity vs. low： OR＝0.08， 95%CI （0.01-0.59）， P＝0.013]， and treatment regimen [monotherapy vs. monotherapy combination therapy： OR＝0.07， 95%CI （0.01-0.50）， P＝0.008] were predictive factors for patients with advanced ESCC to achieve DCB when treated with PD-1 inhibitor. A prediction model was constructed based on the above indicators. Internal validation of the model using the Bootstrap method showed an area under the curve of 0.831 （95%CI： 0.746-0.904）， with specificity of 74.4% and sensitivity of 75.0%. The Hosmer-Lemeshow test yielded χ2＝ 9.930， P＝0.270， and the calibration curve slope was close to 1. The decision curve analysis demonstrated that the model exhibited good clinical utility within a threshold range of 0.1 to 1.0. CONCLUSIONS The prediction model based on baseline LMR， ALB content， BMI， and treatment regimen demonstrates robust predictive performance and clinical utility for assessing therapeutic efficacy of PD-1 inhibitor in the treatment of advanced ESCC.

Objective: To analyze the mediating effect of social support between family resilience and quality of life among elderly patients with Alzheimer's disease (AD), so as to provide the basis for improving the quality of life among elderly patients with AD. Methods: Elderly patients with AD who aged >60 years admitted to Wenzhou Seventh People's Hospital from August 2017 to June 2021 were selected. Data on demographic information, the severity of AD, and the profile of the primary caregivers were collected through questionnaire surveys. Social support, family resilience, and quality of life were assessed using the Social Support Rating Scale, the Shortened Chinese Version of the Family Resilience Assessment Scale, and the Chinese Version of the Quality of Life in Alzheimer's Disease Scale, respectively. The Process macro program was used to analyze the mediating effect of social support between family resilience and quality of life. Results: A total of 137 elderly patients with AD were surveyed. The mean age of the participants was (69.26±10.93) years. Among them, there were 63 males (45.99%) and 74 females (54.01%). The mean scores for social support, family resilience, and quality of life were (27.93±4.28), (97.34±10.06), and (27.82±7.27) points, respectively. The results of the mediating effect analysis indicated that family resilience could directly and positively affect the quality of life, with an effect value of 0.319 (95%CI: 0.122-0.491). It could also indirectly and positively affect the quality of life through social support, with an effect value of 0.118 (95%CI: 0.030-0.248). The mediating effect accounted for 26.42% of the total effect. Conclusion Social support plays a positive mediating role between family resilience and quality of life among elderly patients with AD.

BACKGROUND: The benefits of ideal cardiovascular-health metrics (ICVHMs) in patients with renal insufficiency remain unclear. This study aimed to investigate the associations between ICVHM and prognosis in a renal insufficiency population. METHODS: The trial enrolled 29,682 participants from the US National Health and Nutrition Examination Survey (NHANES), 2007-2018, with mortality follow-up through December 31, 2019. Participants were divided into three groups based on estimated glomerular filtration rates. Cardiovascular health was assessed using new "Life's Essential 8" metrics. Cox regression analyses based on NHANES data were used to determine the associations between ICVHMs and cardiovascular mortality in patients with renal insufficiency. RESULTS: During a mean follow-up of 6.58 years, ideal cardiovascular health (hazard ratio [HR] = 0.42; 95% confidence interval [CI]; 0.25-0.70) and ideal health behavior (HR = 0.53; 95% CI; 0.39-0.73) reduced cardiovascular mortality in participants with renal insufficiency. For each one ICVHM increment, a 25% reduction in cardiovascular mortality was recorded (95% CI; 0.69-0.82). When compared with participants with normal renal function, for those with mild renal insufficiency, the HR for cardiovascular mortality gradually decreased from 1.47 (95% CI; 0.85-2.52) in those who had ≤1 ICVHMs to 0.30 (95% CI; 0.12-0.77) in participants who had >6 ICVHMs. CONCLUSIONS From an ICVHM perspective, enhanced cardiovascular benefits were observed in individuals with renal insufficiency, coupled with a reduced risk of all-cause mortality. Furthermore, when compared with individuals with normal renal function, increased ICVHMs can mitigate adverse risks associated with renal impairment.
Humans ; Male ; Female ; Nutrition Surveys ; Middle Aged ; Renal Insufficiency/physiopathology* ; Aged ; Prognosis ; Adult ; Cardiovascular Diseases/mortality* ; Glomerular Filtration Rate/physiology* ; Proportional Hazards Models

OBJECTIVE: To evaluate the impact of critical care warning platform (CWP) on clinical outcomes of patients transferred from internal medical ward to intensive care unit (ICU) based on real-world data. METHODS: A retrospective cohort study was conducted. The patients transferred from internal medical ward to ICU of Zhongda Hospital, Southeast University, between January 2022 and October 2024, were enrolled. They were divided into critical care warning group and conventional treatment group based on whether they were connected to the CWP. The patients in the critical care warning group were connected to the CWP, which collected real-time vital signs and treatment data. The platform automatically calculated severity scores, generated individualized risk assessments, and triggered warning alerts, allowing clinicians to adjust treatment plans accordingly. The patients in the conventional treatment group were not connected to the CWP and relied on conventional clinical judgment and nursing measures for treatment management. Baseline characteristics [gender, age, body mass index (BMI), admission type, severity score of illness, underlying diseases, and disease type at ICU admission], primary clinical outcome (in-hospital mortality), and secondary clinical outcomes [ICU mortality, length of ICU stay, total length of hospital stay, and mechanical ventilation and continuous renal replacement therapy (CRRT) status] were collected. Multivariate Logistic regression was used to analyze the impact of CWP on in-hospital death, and subgroup analyses were performed based on different patient characteristics. RESULTS: A total of 1 281 patients were enrolled, with 768 in the critical care warning group and 513 in the conventional treatment group. Compared with the conventional treatment group, the proportion of patients in the critical care warning group with underlying diseases of diabetes and malignancy and transferred to ICU due to sepsis was lowered, however, there were no statistically significant differences in other baseline characteristics between the two groups. Regarding the primary clinical outcome, the in-hospital mortality in the critical care warning group was significantly lower than that in the conventional treatment group [17.6% (135/768) vs. 25.7% (132/513), P < 0.01]. For secondary clinical outcomes, compared with the conventional treatment group, the patients in the critical care warning group had significantly fewer days of mechanical ventilation within 28 days [days: 2 (1, 6) vs. 2 (1, 8), P < 0.05], significantly shorter length of ICU stay [days: 3 (2, 8) vs. 4 (2, 10), P < 0.01], and significantly lower ICU mortality [15.1% (116/768) vs. 21.4% (110/513), P < 0.01]. Multivariate Logistic regression analysis showed that, after adjusting for age and underlying diseases, the use of CWP was significantly associated with a reduction of in-hospital mortality among patients transferred from internal medical ward to ICU [odds ratio (OR) = 0.670, 95% confidence interval (95%CI) was 0.502-0.894, P = 0.006]. Further subgroup analysis revealed that, among patients transferred to ICU due to sepsis, the use of CWP significantly reduced in-hospital mortality (OR = 0.514, 95%CI was 0.367-0.722, P < 0.001). In patients aged ≥ 70 years old (OR = 0.587, 95%CI was 0.415-0.831, P = 0.003) and those with underlying diseases of malignancy (OR = 0.124, 95%CI was 0.046-0.330, P < 0.001), CWP also showed significant protective effects on in-hospital prognosis. CONCLUSION The use of CWP is significantly associated with a reduction in in-hospital mortality among patients transferred from internal medical ward to ICU, demonstrating its potential in assessing the deterioration of hospitalized patients.
Humans ; Intensive Care Units ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Critical Care ; Male ; Female ; Patient Transfer ; Middle Aged ; Aged ; Cohort Studies

OBJECTIVE: To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. METHODS: Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. RESULTS: This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. CONCLUSIONS Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.
Humans ; Machine Learning ; Sepsis/therapy* ; Respiration, Artificial ; Intensive Care Units ; Organ Dysfunction Scores ; Male ; Female ; Middle Aged ; Adult

The plant F-box protein more axillary growth 2 (MAX2) is a key factor in the signal transduction of strigolactones (SLs) and karrinkins (KARs). As the main component of the SKP1-CUL1-FBX (SCF) complex ubiquitin ligase E3, MAX2 is responsible for specifically recognizing the target proteins, suppressor of MAX2 1/SMAX1-like proteins (SMAX1/SMXLs), which would be degraded after ubiquitination. It can thereby regulate plant morphogenesis and stress responses. There exist homologous genes of MAX2 in the important grain and oil crop soybean (Glycine max). However, its role in plant defense responses has not been investigated yet. Here, GmMAX2b, a homologous gene of MAX2, was successfully cloned from stressed soybean. Bioinformatics analysis revealed that there were two MAX2 homologous genes, GmMAX2a and GmMAX2b, with a similarity of 96.2% in soybean. Their F-box regions were highly conserved. The sequence alignment and cluster analysis of plant MAX2 homologous proteins basically reflected the evolutionary relationship of plants and also suggested that soybean MAX2 might be a multifunctional protein. Expression analysis showed that plant pathogen infection and salicylic acid treatment induced the expression of GmMAX2b in soybean, which is consistent with that of MAX2 in Arabidopsis. Ectopic expression of GmMAX2b compensated for the susceptibility of Arabidopsis max2-2 mutant to pathogen, indicating that GmMAX2b positively regulated plant disease resistance. In addition, yeast two hybrid technology was used to explore the potential target proteins of GmMAX2b. The results showed that GmMAX2b interacted with SMXL6 and weakly interacted with SMXL2. In summary, GmMAX2b is a positive regulator in plant defense responses, and its expression is induced by pathogen infection and salicylic acid treatment. GmMAX2b might exert its effect through interaction with SMXL6 and SMXL2. This study expands the theoretical exploration of soybean disease resistant F-box and provides a scientific basis for future soybean disease resistant breeding.
Glycine max/metabolism* ; Disease Resistance/genetics* ; Plant Diseases/immunology* ; Plant Proteins/genetics* ; Cloning, Molecular ; Gene Expression Regulation, Plant ; F-Box Proteins/genetics* ; Arabidopsis/genetics* ; Phylogeny