Tripchlorolide induces autophagy in lung cancer cells by inhibiting the PI3K/AKT/mTOR pathway and improves cisplatin sensitivity in A549/DDP cells Tripchlorolide Induces Autophagy in Lung Cancer Cells by Inhibiting the PI3K/AKT/mTOR Pathway and Improves Cisplatin Sensitivity in A549/DDP Cells

Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal treatment strategies and improving clinical prognoses. Faced with the challenge of improving diagnostic precision and stability, this study has developed an innovative deep learning-based model. This model employs a Feature Pyramid Network (FPN) and Squeeze-and-Excitation (SE) modules combined with a Residual Network (ResNet18), to enhance the processing capabilities for complex images and conduct multi-scale analysis of each channel's importance in classifying lung cancer. Moreover, the performance of the model is further enhanced by employing knowledge distillation from larger teacher models to more compact student models. Subjected to rigorous five-fold cross-validation, our model outperforms existing models on all performance metrics, exhibiting exceptional diagnostic accuracy. Ablation studies on various model components have verified that each addition effectively improves model performance, achieving an average accuracy of 98.84% and a Matthews Correlation Coefficient (MCC) of 98.83%. Collectively, the results indicate that our model significantly improves the accuracy of disease diagnosis, providing physicians with more precise clinical decision-making support.

The COVID-19 pandemic is alarmingly a global health catastrophe that has created an unprecedented mental health decline especially in young adults, who have been noted to be a vulnerable population. In this study, we investigated the prevalence of depression and anxiety in university students in China and Africa during the COVID-19 pandemic, the significant factors contributing to the prevalence of anxiety and depression, the differences in factors affecting the different groups being investigated and to emphasize that psychological intervention are as important as the physical interventions during and after the pandemic. The study was conducted through online surveys, with 684 participants using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 standardized scales. Comparing all groups combined, of the 636 participants, 361 (56.8%) had depression and 227 (35.7%) had anxiety. Chi squared tests at significance level (P<0.05) showed that country of citizenship, religion, parents’ educational background, household monthly income and, having family members with COVID-19 variables were strongly associated with depression and anxiety. In contrast, age, gender, educational background, and major showed no significant association. Comparing the individual groups separately using chi square (P<0.05), the Chinese students in China group had 35.6% with depression and 13.1% with anxiety. The variable associated with both depression and anxiety was education major, with depression only was parent’s educational background and with anxiety only was gender. The African students in China group had 70.3% with depression and 45.0% with anxiety. Gender was strongly associated with both depression and anxiety, and religion and having family members with COVID-19 with anxiety only. Africans in Africa had 66.0% with depression and 50.5% with anxiety. Educational background was strongly associated with depression. There was no statistically significant variable for anxiety. Chi square test showed a statistically significant difference in depression and anxiety levels with the Chinese group compared to both African groups, and no significant difference between both African groups. Our findings demonstrated that COVID-19 had a negative psychological impact on university students. Therefore, more attention should be put on youth’s mental health during this pandemic.

In patients with idiopathic pulmonary fibrosis (IPF), the role of 5‑methylcytosine (m5C)‑associated genes in the pathogenesis and development of the disease remains unclear. The present study aimed to identify reliable diagnostic markers based on the expression of m5C‑associated genes for the early detection of IPF. Count data were obtained by screening the IPF genome‑wide assay in the Gene Expression Omnibus database, followed by a comparison of m5C gene expression in patients with IPF and controls. The GSE150910 and GSE173355 datasets yielded a total of 23 differentially expressed m5C‑associated genes, which were then investigated for their functions. A diagnostic model was built using eight m5C genes and validated with training sets and the GSE124685 dataset. IPF subtypes were identified based on expression of m5C‑related genes as well as clinical and immunological characteristics. Furthermore, a pulmonary fibrosis model was established in mice by administering bleomycin into the trachea. Lungs were harvested and analyzed using quantitative PCR to determine the expression of m5C‑related genes. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that these genes were significantly enriched in ‘base excision repair’. Immunoassay results revealed that 13 immune cell markers (naive, memory and B cell plasma, T cell CD4 naive, T cell CD4 memory resting, T cell follicular helper, T cell regulatory Tregs, NK cell resting, Monocyte, Macrophage M0, Mast cell activated, Eosinophil, and Neutrophil) were significantly associated with IPF. Patients with IPF had lower levels of resting memory CD4+ T cells, which were positively associated with Tet methylcytosine dioxygenase2 (TET2) and Thymine‑DNA glycosylase (TDG) but negatively correlated with NOP2/Sun RNA methyltransferase5 (NSUN5) expression. All samples were classified into based on the levels of the eight diagnostic m5C genes. Samples with high m5C scores are subtype 1, and those with low m5C scores are subtype 2. In subtype 2, male patients had lower levels of CD27 and CD70 but higher levels of CD274, CD86, Cytotoxic T‑lymphocyte‑associated protein4 and Hepatitis A virus cellular receptor2 (HAVCR2). When compared with normal mouse lung tissue samples, expression levels of NOP2/Sun RNA methyltransferase6 (NSUN6), Ubiquitin‑like with PHD and RING Finger Domains1, TDG and TET2 in lung fibrosis tissue samples were significantly higher, while expression levels of NSUN5, NTH‑like DNA glycosylase1, DNA (cytosine‑5‑)‑methyltransferase3 β and Methyl‑CpG binding domain protein 3) were lower. It is possible that m5C‑associated genes play an important role in the diagnosis and typing of IPF. These genes may facilitate investigation of the pathophysiology of IPF and identification of potential treatment targets.

Non-SMC condensing I complex subunit G (NCAPG) has been implicated in tumor progression. However, its role, potential mechanism and prognostic significance in human non-small cell lung cancer (NSCLC) remain elusive. Through the conjoint analysis of the TCGA and The Gene Expression Omnibus (GEO) databases, we confirmed that NCAPG is an upregulated gene. The prognostic value of NCAPG was elucidated through data analysis. The functional roles and mechanistic insights of NCAPG in NSCLC growth and metastasis were evaluated in vitro and in vivo. NCAPG expression was significantly increased in NSCLC. Multivariate Cox regression analysis demonstrated that NCAPG was an independent prognostic factor in patients with NSCLC. The high expression of NCAPG was significantly correlated with lymphatic metastasis. Additionally, the high expression of NCAPG effectively promoted the growth and metastasis of NSCLC in vitro and in vivo. In terms of mechanism, the interaction between NCAPG and Cyclin-dependent kinase 1 (CDK1) promotes the phosphorylation of Extracellular signal-regulated kinase (ERK). Overall, our results reveal the key role of NCAPG in NSCLC and highlight the regulatory function of the NCAPG/CDK1/ERK axis in regulating the progression of NSCLC, providing potential prognosis and therapeutic targets for the treatment of NSCLC.

Background: Systemic inflammation is closely associated with adverse outcomes in pulmonary embolism (PE). This study aimed to develop and evaluate a novel inflammatory prognostic score (IPS) derived from multiple inflammation‐related biomarkers to predict long‐term all‐cause mortality in patients with acute PE. Methods: This retrospective cohort study included 1642 patients with confirmed acute PE admitted between January 2016 and January 2024 at the First Affiliated Hospital of Ningbo University and the Affiliated People Hospital of Ningbo University. The primary outcome was all‐cause mortality. Follow‐up was conducted in January 2025. Fifteen inflammatory biomarkers were analyzed, including C‐reactive protein (CRP), white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), monocyte count (MON), red cell distribution width (RDW), platelet count (PLT), and eight derived indices: neutrophil‐to‐lymphocyte ratio (NLR), derived NLR (dNLR), monocyte‐to‐lymphocyte ratio (MLR), neutrophil–MLR (NMLR), platelet‐to‐lymphocyte ratio (PLR), systemic immune‐inflammation index (SII), systemic inflammation response index (SIRI), and inflammatory burden index (IBI). IPS was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression. Prognostic performance was assessed using Kaplan–Meier survival analysis, multivariable Cox regression, time‐dependent receiver operating characteristic (ROC) analysis, and random survival forest (RSF). Results: During a median follow‐up of 43.87 months, 262 patients (16.0%) died. The IPS was calculated as: IPS = 0.395 × IBI + 0.236 × RDW + 0.208 × SIRI + 0.115 × MLR + 0.040 × CRP. Patients with high IPS (≥0.55) had a significantly higher risk of all‐cause mortality compared to those with low IPS (adjusted hazard ratio [HR] = 2.55, 95% confidence intervals [CI]: 1.92–3.38; p < 0.001). Time‐dependent area under the curve (AUC) for IPS were 0.756, 0.768, and 0.773 at 1, 3, and 5 years, respectively. When added to the baseline clinical model—including age, length of hospital stay, deep vein thrombosis, diastolic blood pressure, body mass index (BMI), lactate, log‐transformed serum creatinine, blood urea nitrogen (BUN), log‐transformed D‐dimer, left ventricular ejection fraction (LVEF), pulmonary artery systolic pressure (PASP), and computed tomography pulmonary angiography (CTPA)‐based embolism location (main pulmonary artery)—IPS improved the AUC from 0.820 to 0.886 at 3 years ( p < 0.001, DeLong test). RSF analysis further identified IPS as the most informative inflammatory predictor of mortality. Conclusions: The IPS, derived from five routinely available inflammatory biomarkers, was independently associated with long‐term mortality and significantly enhanced risk stratification beyond traditional clinical predictors in patients with acute PE. This score may support early prognostic assessment and individualized management.