Yang Liu

Circadian syndrome (CircS) involves the integration of systemic circadian disruption with cardiometabolic risk factors, but its longitudinal effect on incident chronic lung disease (CLD) remains unknown. This prospective dual-cohort study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS, n = 7,553) and the English Longitudinal Study of Ageing (ELSA, n = 4,957). CircS was defined by the clustering of at least 4 out of 7 circadian-metabolic components. Multivariate Cox proportional hazards models estimated hazard ratios (HRs) for incident CLD over a seven-year follow-up. In the discovery cohort (CHARLS), CircS was independently associated with an increased risk of incident CLD ( HR = 1.16, 95% CI = 1.01–1.33). This was robustly confirmed in the validation cohort (ELSA) with a stronger effect size ( HR = 1.53, 95% CI = 1.20–1.95). Furthermore, subgroup analyses revealed that this risk was significantly amplified by alcohol consumption in the British population ( P < 0.05), suggesting an interactive effect between circadian misalignment and lifestyle stressors. In conclusion, CircS serves as a robust, independent predictor of respiratory decline. Evaluating circadian integrity offers a reliable indicator of respiratory vulnerability, and holistic chronomedicine approaches targeting sleep hygiene and moderate alcohol consumption may help mitigate the global CLD burden.

Radiomics holds great potential for the noninvasive evaluation of lung cancer mutations; however, challenges related to data imbalance and model robustness limit its current efficacy and safety. This multicenter study aimed to develop and validate a model utilizing the Synthetic Minority Over-sampling Technique (SMOTE) and clustering methods to address the challenges associated with small sample sizes and imbalanced radiomics features between negative and positive samples. A total of 230 patients with lung adenocarcinoma who underwent positron emission tomography/computed tomography and genetic testing for anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations were included. Multi-layer perceptron (MLP) models were developed for each genetic mutation group based on 2 different approaches for processing radiomics features. The MLP model incorporating SMOTE and clustering demonstrated potentially improved performance, with AUC values of 0.591 for ALK, 0.750 for EGFR, and 0.789 for KRAS in the external test set. These findings suggest that the MLP model combined with SMOTE and clustering has potential for predicting ALK, EGFR, and KRAS mutations in lung adenocarcinoma.

Objective: This study aims to characterize the GM in LUAD patients and develop and validate a GM-based diagnostic model for LUAD. Methods: In this prospective, randomized, multi-center study, the GM was characterized, and an LUAD classifier was developed using a training cohort of 175 early-stage LUAD patients and 107 healthy controls. The model was further validated in a test cohort, two independent external cohorts from Jiangsu and Hainan, and an advanced LUAD cohort. Additional ML models were also developed and compared to assess their predictive performance. Results: LUAD patients exhibited reduced microbial diversity and significantly altered microbial composition compared to healthy controls. The phylum Verrucomicrobia and 13 genera, including Enterococcus and Akkermansia , were more abundant in the LUAD group, while 5 phyla, such as Fusobacteria and Cyanobacteria, and 17 genera, including Lactobacillus and Weissella , were enriched in the control group. Using random forest (RF), eight operational taxonomic units were identified as the optimal subset, achieving an area under the curve (AUC) of 0.998 in the training cohort and maintaining high accuracy in the test cohort (AUC = 96.9%). The model also demonstrated robust performance in two independent cohorts from Jiangsu (AUC = 97.6%) and Hainan (AUC = 82.9%), with strong diagnostic potential for advanced LUAD. Among five common models, the RF model exhibited the highest diagnostic accuracy. Conclusions: This study provides a comprehensive characterization of the gut microbiome in LUAD and develops a diagnostic model based on microbial biomarkers, which is validated across regionally diverse cohorts, highlighting its potential as a reliable and non-invasive screening tool for LUAD.

Bone fracture healing requires coordinated regulation of osteogenesis, angiogenesis, and immune homeostasis within a dynamically evolving microenvironment.

The refinement of risk stratification in lung adenocarcinoma (LUAD) plays a pivotal role in advancing precision medicine; however, the current staging classification falls short of comprehensiveness, particularly in the case of stage IA patients. We aimed to molecularly stratify LUAD patients especially for stage IA. We analysed tumour heterogeneity and identified highly proliferating cancer cells (HPCs) in LUAD by performing single-cell RNA sequencing (scRNA-seq) analysis, immunohistochemical (IHC) staining using a tissue microarray, flow cytometry and biological experiments. Then, we quantified the content of HPCs in nine LUAD datasets by single-sample gene set enrichment analysis and evaluated the relationship between the percentage of HPCs and overall survival (OS). Next, we analysed the OS predictive effect of HPCs at different LUAD stages, especially for stage I risk stratification. Furthermore, we established a prognostic prediction model based on HPC-associated genes for clinical application. The above findings were validated in another five LUAD datasets. Finally, we explored the relationship between HPCs and the progressive pathological evolution of early-stage LUAD and the driving mutations by scRNA-seq, bulk RNA-seq and IHC staining. LUAD tissues carry a small proportion of HPCs, which show potential for malignant proliferation and intense interactions with the microenvironment. A high HPC content is an independent risk factor for OS in LUAD patients, even in stage IA patients. HPCs can be used to establish a cut-off point for the prognosis of stage IA disease, with patients with a higher risk showing a prognosis similar to that of patients with stage IB disease. We built an R package (HSurADs) based on HPC-associated genes, which exhibited good efficacy for the prognostic prediction of LUAD. HPCs gradually increase with the pathological evolution of early-stage LUAD, which may be affected by TP53 mutations. The HPC content can be used as a novel prognostic factor for LUAD, especially for stage IA risk stratification.

Background: Robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) are new modes for the surgical treatment of mediastinal tumors. The differences in perioperative efficacy and safety among RATS, VATS, and conventional open surgery (COS) are a major concern for surgeons. Most of the previous studies have not paid sufficient attention to mediastinal roof tumors. This study comprehensively analyzed the efficacy and safety of RATS, VATS, and COS for the surgical treatment of mediastinal roof tumors. Methods: Patients who underwent resection of mediastinal roof tumors at our center from 2013 to 2023 were selected as the study cohort. Their perioperative and postoperative indexes were collected and cases were stratified according to tumor location for stratified analysis. The efficacy and safety of the three surgical methods were compared. Results: A total of 213 patients were enrolled in this study: 57 RATS (26.8%), 115 VATS (54.0%), and 41 COS (19.2%). RATS and VATS were shown to be superior to COS in intraoperative bleeding, thoracic drainage time, and postoperative hospital stay. RATS and VATS had similar advantages over COS in the treatment of anterior region tumors. RATS could shorten the postoperative hospital stay and reduce the incidence of postoperative composite adverse outcomes for posterior region tumors. When the maximum tumor diameter was ≥30 mm, the RATS group had obvious advantages. Conclusions: Our study suggests that RATS and VATS are safe and feasible for the resection of mediastinal roof tumors. VATS may be more appropriate for anterior region tumors, and RATS has advantages for posterior region tumors and large tumors.

Background: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. Objective: This study evaluated the diagnostic efficacy of a CRDL model in differentiating pulmonary metastases from NCPH. Methods: The authors retrospectively analyzed the clinical and imaging data of 256 patients from the First Medical Centre of the General Hospital of the People’s Liberation Army (PLA) and 85 patients from Shanghai Changhai Hospital, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, the authors extracted radiomic features and deep learning (DL) attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. Results: Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred), and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and DL. Of the 1130 radiomics features and 512 DL features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. Conclusions: The comprehensive model incorporating clinical features, radiomics, and DL shows promise for aiding in the differentiation between pulmonary metastases and hamartomas.

Here we examined whether Osimertinib (OSI) resistance which due to induced autophagy could be reversed by inhibiting autophagy with Pantoprazole. The effects of OSI ± pantoprazole were examined in four different non-small-cell lung cancer (NSCLC) cell lines, along with two human xenograft models. The effects of OSI ± pantoprazole on autophagy and drug effects were evaluated. Alterations in PI3K/Akt/mTOR pathway activation were also evaluated. Combination of OSI and pantoprazole resulted in decreased proliferation and tumor inhibition in NSCLC cells compared to single agent therapy. Treatment with OSI alone resulted in up-regulation of autophagy in NSCLC cells. Pantoprazole inhibited the induction of autophagy and sensitized NSCLC cells to OSI. When used together, OSI and pantoprazole increased apoptosis and reactive oxygen species (ROS) generation in NSCLC cells. Pantoprazole effectively reversed OSI-mediated suppression of the PI3K/Akt/mTOR pathway in NSCLC cells. Pharmacological autophagy inhibition sensitized NSCLC cells to OSI and enhanced the efficacy of OSI.

Objective: We aim to molecularly stratify stage IA lung adenocarcinoma (LUAD) for precision medicine. Methods: Twelve multi-institution datasets (837 cases of IA) were used to classify the high- and low-risk types (based on survival status within 5 years), and the biological differences were compared. Then, a gene-based classifying score (IA score) was trained, tested and validated by several machine learning methods. Furthermore, we estimated the significance of the IA score in the prognostic assessment, chemotherapy prediction and risk stratification of stage IA LUAD. We also developed an R package for the clinical application. The SEER database (15708 IA samples) and TCGA Pan-Cancer (1881 stage I samples) database were used to verify clinical significance. Results: Compared with the low-risk group, the high-risk group of stage IA LUAD has obvious enrichment of the malignant pathway and more driver mutations and copy number variations. The effect of the IA score on the classification of high- and low-risk stage IA LUAD was much better than that of classical clinicopathological factors (training set: AUC = 0.9, validation set: AUC = 0.7). The IA score can significantly predict the prognosis of stage IA LUAD and has a prognostic effect for stage I pancancer. The IA score can effectively predict chemotherapy sensitivity and occult metastasis or invasion in stage IA LUAD. The R package IAExpSuv has a good risk probability prediction effect for both groups and single stages of IA LUAD. Conclusions: The IA score can effectively stratify the risk of stage IA LUAD, offering good assistance in precision medicine.