Background Regular assessment of exercise tolerance is essential for managing COPD, nevertheless, the standard 6-Minute Walk Test (6MWT) is difficult to perform outside clinical settings. This study aimed to develop and validate a smartphone-based digital 1-Minute Sit-to-Stand Test (1MSTST) to estimate 6-Minute Walking Distance (6MWD) by integrating data from the phone's Inertial Motion Unit (IMU) with advanced machine learning algorithms, offering a convenient alternative for remote functional assessment. Methods The enrolled COPD patients completed the smartphone-based digital 1MSTST and 6MWT with a minimum 15-minute rest period between the two tests. Accelerometer and gyroscope data were recorded by a smartphone throughout the 1MSTST. Systolic and Diastolic Blood Pressure (SBP, DBP), Heart Rate (HR) and Pulse Oxygen Saturation (SpO2) were measured before and after the 1MSTST and 6MWT. The authors used a smartphone-based dataset of 66 subjects and algorithms such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Linear Regression (LR) and Random Forest (RF) to estimate the prediction of digital 1MSTST to 6MWD. The correlation between the features extracted from digital 1MSTST and the 6MWD was analyzed. Results A total of 66 patients with stable COPD were enrolled to build the predictive model for 6MWD. The change of HR and SBP after 1MSTST was higher than that of 6MWT (paired t-test, ΔHR: p < 0.0001, ΔSBP: p < 0.0001) with no significant difference in the change of DBP and SpO2 (paired t-test, ΔDBP: p = 0.974, ΔSpO2: p = 0.072). Pearson correlation analysis identified the top seven features that were strongly correlated with 6MWD. The RF machine learning model had the best performance to predict the 6MWD (R-square = 0.86, MAE = 24.3). Bland-Altman analysis indicates that the RF model provides a bias of -0.61 ± 31.04 and that the limits of agreement (95%, 1.96 SD) range from -60.22 to 61.45. Conclusions The smartphone-based digital 1MSTST, combined with machine learning, can accurately estimate 6MWD. The significance of this study lies in proposing a novel assessment paradigm that may serve as a practical tool for remote monitoring of exercise capacity in COPD management.

Immunotherapy has showcased remarkable progress in the management of gastric cancer (GC), prompting the need to proactively identify and classify patients suitable for immunotherapy. Here, 30 patients were enrolled and stratified into three groups (PR, partial response; SD, stable disease; PD, progressive disease) based on efficacy assessment. 16S rRNA sequencing were performed to analyze the gut microbiome signature of patients at three timepoints. We found that immunotherapy interventions perturbed the gut microbiota of patients. Additionally, although differences at the enterotype level did not distinguish patients’ immunotherapy response, we identified 6, 7, and 19 species that were significantly enriched in PR, SD, and PD, respectively. Functional analysis showed that betalain biosynthesis and indole alkaloid biosynthesis were significantly different between the responders and non-responders. Furthermore, machine learning model utilizing only bacterial biomarkers accurately predicted immunotherapy efficacy with an Area Under the Curve (AUC) of 0.941. Notably, Akkermansia muciniphila and Dorea formicigenerans played a significant role in the classification of immunotherapy efficacy. In conclusion, our study reveals that gut microbiome signatures can be utilized as effective biomarkers for predicting the immunotherapy efficacy for GC.

Background:Elderly patients are more susceptible to pulmonary aspergillosis(PA) and exhibit worse clinical outcomes. This study aims to compare the clinical characteristics, pathogen distribution, co-infection patterns, and outcomes of elderly and non-elderly patients with PA diagnosed via targeted next-generation sequencing (tNGS). Methods:This retrospective study included 107 patients with tNGS-confirmed pulmonary aspergillosis admitted to Ruijin Hospital between March 2023 and January 2025. Demographic, clinical, radiological, and laboratory data were extracted from electronic medical records to compare the clinical and microbiological profiles between the elderly (≥65 years) and non-elderly (<65 years) cohorts. Results:A total of 107 patients including elderly (n=64) and non-elderly (n=43) were included retrospectively. The prevalence of invasive pulmonary aspergillosis was significantly higher in the elderly group compared to the non-elderly group (93.8% vs 74.4%). Similarly, the elderly group had significantly higher proportions of overall co-infection (79.7% vs 51.2%), bacterial co-infection (68.8% vs 39.5%), and atypical imaging features such as patchy infiltrates/consolidation (89.1% vs 62.8%) (all P < 0.05). Furthermore, the elderly exhibited significantly lower lymphocyte and albumin levels, and higher D-dimer levels (P < 0.05). Mortality was also significantly higher among elderly patients (18.8% vs 4.7%; P = 0.042). Conclusion:Within our study scope, pulmonary aspergillosis in the elderly is characterized by higher mortality, atypical imaging, prevalent bacterial co-infections, and specific laboratory deviations-most notably, diminished lymphocytes and albumin alongside elevated D-dimer and LDH levels. Furthermore, tNGS serves as a valuable adjunctive tool to identify complex mixed pathogens and assist in clinical management.

Recent advances in cough sound analysis using deep learning techniques enable smartphone-based respiratory disease screening suitable for self-management care in a home setting, yet their utility is limited by device heterogeneity, population diversity, and challenges in multimodal integration. We propose a device-invariant, multimodal deep learning framework that jointly models cough acoustics, demographic data, and symptom descriptions for multi-label classification of adult respiratory diseases. To address the issues of device effect, an adversarial branch is embedded in the audio encoder to enforce device-invariant feature learning, while an invariant risk minimization-augmented loss enhances robustness to non-structural shifts. To evaluate the effectiveness of our proposed method, a real-world, multi-center dataset containing over 10,000 cases spanning seven major respiratory conditions was curated. On the tasks of individual respiratory disease identification for chronic obstructive pulmonary disease (COPD), lower respiratory tract infection (LRTI) and pulmonary shadows (PS), our method achieves superior performance with the area under the receiver operating characteristic curve (AUROC) of 0.9698, 0.8483 and 0.8720, respectively. It also shows promising results in identifying the presence of comorbidities for 7 respiratory diseases with an overall AUROC of 0.8907. More importantly, extensive experimental results demonstrate our method mitigates the issues of device effect and facilitates the cross-device generalization for cough-based respiratory disease diagnoses. This work demonstrates a scalable and transferable AI-based approach for cough-driven respiratory screening, emphasizing the importance of multimodal fusion and robust representation learning in advancing clinical applicability.

BACKGROUND:Prompt identification of rapidly progressive interstitial lung disease (RP-ILD) in dermatomyositis (DM) is crucial; however, reliable biomarkers remain lacking. OBJECTIVES:To evaluate the association between serum amyloid A (SAA) and RP-ILD in DM. METHODS:SAA levels were quantified via scattering turbidimetry. Spearman's correlation analyzed associations with serologic markers. Diagnostic thresholds were determined through ROC curve analysis, and independent markers associated with mortality were identified using Cox proportional hazards models. RESULTS:SAA levels were significantly higher in DM patients than in healthy controls (37.71 ± 6.93 vs 5.42 ± 0.30 mg/L; P < 0.0001) and were markedly elevated in those with RP-ILD (103.60 ± 18.02 mg/L). Elevated SAA was independently linked to RP-ILD. The combination of SAA and anti-MDA5 antibody provided the highest diagnostic accuracy (AUC = 0.950; 95% CI: 0.906-0.995). Patients with SAA >21.98 mg/L had significantly worse survival (P < 0.0001). SAA >21.98 mg/L independently implied mortality in multivariable analysis (HR = 14.12; 95% CI: 1.16-171.33; P = 0.038). LIMITATIONS:This was a retrospective, single-center cohort with a modest sample size. CONCLUSION:Elevated SAA is associated with increased frequency of RP-ILD and mortality. Combining SAA with anti-MDA5 antibody optimizes risk stratification and concurrent disease severity evaluation.

Bcakground: Hepatocellular carcinoma (HCC) is a prevalent malignancy with limited therapeutic options. Drug repurposing offers an attractive strategy to accelerate anticancer discovery. The pleuromutilin class of antibiotics, including the human-approved agent lefamulin and the veterinary drug tiamulin, has shown preliminary anticancer potential, but its efficacy and mechanism in HCC remain unexplored. Methods: The anti-tumor effects of lefamulin and tiamulin were evaluated in HCC cell lines, patient-derived organoids, and a C57BL/6 mouse subcutaneous tumor model. Safety was assessed in a human normal hepatocyte cell line and by histopathological examination of major organs in treated mice. Mechanistic investigations were performed using RNA-sequencing, RT-qPCR, immunohistochemistry (IHC), filipin staining, pharmacological rescue assays, and shRNA-mediated gene silencing. Results: In this study, we found that both lefamulin and tiamulin markedly inhibited HCC cell proliferation in vitro and significantly suppressed tumor growth in vivo (lefamulin vs. control, p = 0.014; tiamulin vs. control, p = 0.021), without causing significant toxicity. RNA-sequencing analysis revealed consistent downregulation of the cholesterol transporter Abca1 (ATP-binding cassette transporter A1) and alterations in cell adhesion molecule pathways. Functional studies confirmed that treatment reduced ABCA1 protein levels, leading to intracellular cholesterol accumulation and aberrant distribution. Furthermore, treated tumors exhibited a significant increase in CD8+ T-cell infiltration, with CD4+ T cells and macrophage infiltration remained unchanged, indicating a specific modulation of the tumor immune microenvironment. Conclusions: These findings suggest that lefamulin and tiamulin are promising therapeutic candidates for HCC.

Glypican-3 (GPC3)-targeted chimeric antigen receptor T (CAR-T) cell therapy is a promising approach for hepatocellular carcinoma (HCC), but marked interpatient variability and antigen heterogeneity limit its broader application. Here, we established a patient-derived organoid (PDO)-based platform to functionally evaluate autologous GPC3-targeted CAR-T cell activity in HCC. HCC PDOs preserved key histologic features and heterogeneous GPC3 expression patterns of the original tumors. In co-culture assays, CAR-T cell cytotoxicity was associated with GPC3 expression levels and was accompanied by IFN-γ and IL-2 release, supporting the feasibility of using PDOs for functional assessment of CAR-T cell sensitivity. We further found that matrix conditions strongly influenced organoid architecture, viral transduction, CAR-T cell infiltration, and killing efficiency, with lower Matrigel concentrations providing a more permissive setting for functional assessment. Importantly, in GPC3-low PDOs, pretreatment with the DNA methyltransferase inhibitor 5-azacytidine (5-AZA) reduced DNA methyltransferase 3 alpha (DNMT3A) expression, increased surface GPC3 expression, and significantly enhanced CAR-T-mediated cytotoxicity. Together, these findings provide proof-of-concept evidence supporting the use of HCC PDOs as a patient-derived platform for modeling selected determinants of GPC3-targeted CAR-T cell activity and for exploring combination strategies to improve therapeutic efficacy.

Early COPD diagnosis is vital for effective management, yet conventional tools such as professional spirometers are often inaccessible in resource-limited settings. We present Cough Search, a smartphone-based deep learning algorithm that uses voluntary cough sounds to detect COPD, offering a cost-efficient and accessible diagnostic approach. The presented COPD detection algorithm (Cough Search) employs a transformer-based neural network model. It was trained on a training cohort (406 COPD and 1631 non-COPD) with hyperparameters tuned on the balanced internal validation cohort (151 COPD and 225 non-COPD participants). The algorithm was finally validated on the external validation cohort (105 COPD and 617 non-COPD participants from four hospitals). Participants were classified as COPD or non-COPD based on spirometry and clinical diagnoses. Cough Search achieved an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.94 in the internal and external validation cohorts, respectively. In the external validation cohort study, the model demonstrated high sensitivity (92%) and specificity (86%) in distinguishing COPD from non-COPD cases. Performance remained robust across all COPD stages, with a sensitivity exceeding 93% for severe stages (GOLD 3-4) and above 91% for moderate stages (GOLD 1-2). The algorithm maintained its accuracy across non-COPD respiratory conditions and smartphone models. Cough Search shows promise as a scalable, accessible tool for COPD detection, particularly in underserved areas, potentially transforming early COPD diagnosis and management. Trial registration: ClinicalTrials.gov Identifier: NCT06082791.

Heterogeneous response to anti-programmed cell death protein 1 (PD-1) immunotherapy in lung cancer necessitates reliable biomarkers for monitoring systemic CD8+ T-cell dynamics. This study used [68Ga]Ga-NODAGA-SNA006, a CD8-targeted PET tracer, to evaluate CD8+ T-cell distribution in relation to disease progression and treatment response. Methods: Fourteen patients with stage II-IV lung cancer underwent baseline [68Ga]Ga-NODAGA-SNA006 PET/CT and blood CD8+ T-cell quantification. Eight patients receiving anti-PD-1-based chemoimmunotherapy underwent PET/CT and blood analyses before treatment and after 2 treatment cycles. SUVmax was measured in tumors (along with the tumor-to-background ratio), the spleen, the liver, and axial (sternum, T12, pelvis) and appendicular (femur) bone marrow. Correlations with disease stage, peripheral blood CD8+ T-cell quantification, programmed death ligand 1 (PD-L1) expression, and tumor reduction were assessed. Results: Patients with stage IV disease patients exhibited a lower peripheral blood CD8+ T-cell count and percentage (P < 0.05), whereas the SUVmax in the spleen (P < 0.05) and axial bone marrow (sternum, T12, pelvis; P < 0.01) was higher than that in patients with earlier-stage disease. Peripheral blood CD8+ T-cell percentage was correlated with SUVmax in the spleen (r 2 = 0.38, P < 0.05) and axial bone marrow sites (most significant in sternum: r 2 = 0.68, P < 0.001). After anti-PD-1 therapy, analysis of combined "axial skeleton SUV" (sternum, T12, pelvis) revealed a significant interaction between time and PD-L1 group (P < 0.001): SUV increased in the low PD-L1 (<10%) group but decreased in the high PD-L1 (≥10%) group. Changes in peripheral blood CD8+ T-cell count and percentage were not significant. Tumor shrinkage was not significantly correlated with peripheral blood CD8+ T-cell percentage or T-cell dynamics. For all target lesions, tumor shrinkage showed moderate positive correlations with baseline lesion SUVmax (r 2 = 0.30, P < 0.01) and baseline tumor-to-background ratio (r 2 = 0.34, P < 0.001) and a strong positive correlation with the reduction in lesion SUVmax after treatment (r 2 = 0.44, P < 0.01). Conclusion: [68Ga]Ga-NODAGA-SNA006 PET/CT revealed systemic CD8 + T-cell depletion and splenic/axial marrow sequestration in advanced lung cancer. During anti-PD-1 therapy, CD8 + T cells redistribute on the basis of PD-L1 status. Baseline intratumoral CD8 + T-cell density and early on-treatment SUVmax reduction are robust imaging biomarkers of response, outperforming peripheral blood monitoring.

Background Diabetes mellitus (DM) predisposes patients to severe pneumonia (SP) and multidrug-resistant (MDR) infections, yet interactions between the disruption of the respiratory microbiome and host immunity remain poorly understood. Methods In this multicenter prospective cohort study, 216 pneumonia patients were stratified by DM status and disease severity. Bronchoalveolar lavage fluid (BALF) was analyzed using metagenomic and transcriptomic sequencing to profile both the microbial community and host gene expression, followed by integrative multi‑omics network analysis. Findings Patients with both DM and SP had the worst outcomes—higher ICU admission, mechanical ventilation, and in‑hospital mortality. At the microbiome level, these same patients manifested reduced alpha diversity, with MDR K. pneumoniae enrichment and protective commensal depletion. MDR K. pneumoniae independently predicted ICU admission and mechanical ventilation, and correlated with BALF neutrophil fraction and CXCL8 expression. Host transcriptomic profiling revealed that SP with diabetics was characterized by the activation of innate inflammatory pathways (e.g., TNF, NF-κB) and concurrent suppression of adaptive immunity. Neutrophil fraction was associated with SP only in diabetic patients, whereas microbiome community structure (PCoA1) remained independently protective regardless of DM status. Intriguingly, blood glucose showed a DM-specific inverted U-shaped relationship with microbiome diversity, peaking around 8 mmol/L. Multi-omics network analysis identified glucose as a central hub orchestrating a hyperglycemia‑inflammation‑MDR pathogen axis in the diabetic cohort. Interpretation Overall, DM precipitated a pathological process of MDR K. pneumoniae dominance, microbiome collapse, and neutrophil-driven immune dysregulation in SP—providing a multi-omics framework for risk stratification and targeted therapeutic intervention.

Background: In idiopathic pulmonary fibrosis (IPF) patients, alveolar architectures are lost and gas transfer function would decline, which cannot be rescued by conventional anti-fibrotic therapy. P63+ lung basal progenitor cells are reported to have potential to repair damaged lung epithelium in animal models, which need further investigation in clinical trials. Methods: We cloned and expanded P63+ progenitor cells from IPF patients to manufacture cell product REGEND001, which were further characterized by morphology and single-cell transcriptomic analysis. Subsequently, an open-label, dose-escalation autologous progenitor cell transplantation clinical trial was conducted. We treated 12 patients with ascending doses of cells: 0.6x, 1x, 2x and 3.3x106 cells/kg bodyweight. The primary outcome was the incidence and severity of cell therapy-related adverse events (AEs); secondary outcome included other safety and efficacy evaluations. Results: P63+ basal progenitor cell was safe and tolerated at all doses, with no dose-limiting toxicity or cell therapy-related severe adverse events observed. Patients in three higher dose groups showed significant improvement of lung gas transfer function as well as exercise ability. Resolution of honeycomb lesion was observed in patients of higher dose groups. Conclusions: REGEND001 has high safety profile and meanwhile encourages further efficacy exploration in IPF patients. Funding: National High Level Hospital Clinical Research Funding (2022-PUMCH-B-108), National Key Research and Development Plan (2024YFA1108900, 2024YFA1108500), Jiangsu Province Science and Technology Special Project Funding (BE2023727), National Biopharmaceutical Technology Research Project Funding (NCTIB2023XB01011), Non-profit Central Research Institute Fund of Chinese Academy of Medical Science (2020-PT320-005), and Regend Therapeutics. Clinical trial number: Chinese clinical trial registry: CTR20210349.

Chimeric antigen receptor (CAR) gene-modified T-cell therapy has achieved significant success in the treatment of hematological malignancies. However, this therapy has not yet made breakthroughs in the treatment of solid tumors and still faces issues of resistance and relapse in hematological cancers. A major reason for these problems is the antigenic heterogeneity of tumor tissues. This review outlines the antigenic heterogeneity encountered in CAR-T cell therapy and the corresponding strategies to address it. These strategies include using combination therapy to increase the abundance of target antigens, optimizing the structure of CARs to enhance sensitivity to low-density antigens, developing multi-targeted CAR-T cells, and reprogramming the TME to activate endogenous immunity. These approaches offer new directions for overcoming tumor antigenic heterogeneity in CAR-T cell therapy.

We review current studies on the anti-inflammatory and antifibrotic effects of herbal medicines and their active ingredients. To explore how these active ingredients target long noncoding RNA to exert their effects, we searched PubMed and Chinese National Knowledge Infrastructure databases for preclinical and clinical studies of long noncoding RNAs (lncRNA), herbal medicine, inflammation, and fibrosis. The active ingredients of herbal medicines were able to target lncRNAs. These interactions can have anti-inflammatory and antifibrotic effects on various diseases. The current studies provide preliminary insights but are not comprehensive. Targeting lncRNAs with herbal medicine ingredients is a promising direction for further research. This approach could lead to new alternative treatments for inflammation and fibrosis-related diseases.

Background: Previous research has established that chronic kidney disease (CKD) and heart failure with preserved ejection fraction (HFpEF) often coexist. Although we have a preliminary understanding of the potential correlation between HFpEF and CKD, the underlying pathophysiological mechanisms remain unclear. This study aimed to elucidate the molecular mechanisms associated with CKD and HFpEF through bioinformatics analysis. Methods: Datasets for HFpEF and CKD were obtained from the Gene Expression Omnibus (GEO) database. The R software package “limma” was employed to conduct differential expression analysis. Functional annotation was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We conducted weighted gene co-expression network analysis (WGCNA), correlation analysis with autophagy, ferroptosis, and immune-related processes, as well as transcriptional regulation analysis, immune infiltration analysis, and diagnostic performance evaluation. Finally, the diagnostic potential of the identified hub genes for CKD and HFpEF was assessed using ROC curve analysis (GSE37171). Results: Differential expression analysis revealed 58 overlapping genes, comprised of 40 up-regulated and 18 down-regulated genes. Both GO and KEGG analyses indicated enriched pathways relevant to both disorders. WGCNA identified 4086 genes associated with CKD. Further comparison with differentially expressed genes (DEGs) identified three hub genes (KLF4, SCD, and SEL1L3) that were linked to autophagy, ferroptosis, and immune processes in both conditions. Additionally, a miRNA-mRNA regulatory network involving 376 miRNAs and 12 transcription factors (TFs) was constructed. ROC curve analysis was performed to evaluate the diagnostic utility of the hub genes for CKD and HFpEF. Conclusion: This study elucidated shared pathogenic mechanisms and identified diagnostic markers common to both HFpEF and CKD. The identified hub genes show promise as potential tools for early diagnosis and treatment strategies for these conditions.

IntroductionThis study evaluated the distribution characteristics, influencing factors, and future trends of non-Hodgkin lymphoma (NHL) burden in children and adolescents globally from 1990 to 2021.MethodsData were obtained from the Global Burden of Disease Study database. Multiple analytical methods were used, including Joinpoint regression, age-period-cohort analysis, decomposition analysis, frontier analysis, health equity analysis, and Bayesian age-period-cohort (BAPC) model.ResultsIn 2021, the global age-standardized prevalence rate was 3.177/100,000, with a disability adjusted life year (DALY) rate of 13.535/100,000. The prevalence demonstrated a fluctuating downward trend during 1990-2021. Age, period, and cohort effects significantly influenced disease patterns. While population growth drove prevalence increase, population aging and epidemiological factors had negative impacts. Disease burden showed a non-linear negative correlation with Socio-demographic Index (SDI). Over the past nearly 30 years, health inequality has intensified, as some African regions have shown relatively low prevalence rates due to limited resource Settings, which have restricted disease diagnosis and reporting, compared with the developed areas with high prevalence. The BAPC model predicted further decrease from 2022-2036.DiscussionDespite overall decline, significant regional differences and health inequalities persist, suggesting future focus on targeted prevention, optimized resource allocation, and improved treatment.

Background:Patients with diabetes mellitus (DM) are at increased risk for Clostridioides difficile (C. difficile) infection (CDI), in part due to frequent exposure to antibiotics-particularly broad-spectrum agents-which represents the most important modifiable risk factor for CDI. Objective:To systematically evaluate the impact of DM on the incidence and recurrence risk of CDI, explore underlying mechanisms, and provide evidence-based guidance for prevention and control in high-risk populations. Methods:A systematic search was conducted in PubMed, Embase, and Web of Science to identify cohort and case-control studies reporting on the association between diabetes and the risk or outcomes of CDI. The ROBINS-I tool was used for risk of bias assessment. Random-effects models were applied to pool odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup analyses, sensitivity analyses, and cumulative meta-analyses were performed. The quality of evidence for the primary outcomes was graded according to the GRADE approach. The study protocol was registered in PROSPERO (registration number: CRD420251128182). Results:A total of 12 international studies (including 8 reporting recurrence outcomes) and covering more than 3. 5 million participants from North America, Europe, and East Asia were included. Meta-analysis showed that diabetes significantly increased the risk of CDI (OR=1. 46, 95% CI: 1. 20-1. 77), as well as the risk of recurrence (OR=3. 11, 95% CI: 1. 98-4. 87). Subgroup and sensitivity analyses yielded consistent results, and cumulative meta-analysis indicated that effect sizes became stable over time. Mechanistic analyses suggested that immune dysfunction, gut microbiota imbalance, and exposure to high-risk medications were key contributing factors. Based on GRADE assessment, the quality of evidence for the primary outcomes was moderate, with a low risk of publication bias. Conclusion:Diabetes is an independent risk factor for both CDI and its recurrence. It is recommended that clinicians strengthen CDI risk assessment and integrated prevention strategies for patients with diabetes, with a focus on optimizing antibiotic stewardship, reducing unnecessary broad-spectrum antibiotic use, and microbiota-targeted interventions. High-quality prospective studies are needed to further improve prevention strategies and elucidate underlying mechanisms.

Cell size is an important component of cell morphological characteristics. It reflects the characteristics of the cell type, nutritional status, growth stage and physiological function. The cell size of cells of the same type tends to be homogeneous and stable. However, in tumour cells, mutations in cell cycle genes and cytoskeletal genes and overexpression of the corresponding signalling pathways often lead to large variations in tumour cell size. Tumour cells regulate cell size and growth and proliferation through multiple signalling pathways, such as PI3K/Akt/mTOR, Myc and Hippo pathways, which work together to regulate cell size and proliferation. This allows tumour cells to adapt to different survival environments. Alterations in cell size also cause tumours to perform different functions, leading to alterations in tumour stemness, invasive migration and anti-tumour immunity by affecting immune cells in the tumour immune microenvironment. In this review, we describe the endogenous and exogenous factors affecting tumour cell size, analyse the mechanisms by which tumour cells regulate cell size and the effects of cell size on tumour malignancy and tumour immunity, summarise the potential therapeutic targets for cell size, and look forward to possible future research directions and clinical applications.

Background:Although advancements in cancer therapies have substantially improved the survival of cancer patients, these treatments may also result in acute or chronic lung injury. Cancer treatment-related lung injury (CTLI) presents with a diverse array of clinical manifestations and can involve multiple sites. Due to the lack of specific diagnostic protocols, CTLI can deteriorate rapidly and may be life-threatening if not promptly addressed. Unfortunately, there is no universally accepted consensus document on the diagnosis and management of CTLI. Methods:A multidisciplinary panel comprising experts from respiratory and critical care medicine, oncology, radiation oncology, thoracic surgery, radiology, pathology, infectious diseases, pharmacy, and rehabilitation medicine participated in this consensus development. Through a systematic literature review and detailed panel discussions, the team formulated nine key recommendations. Results:This consensus document addresses the concept, epidemiology, pathogenesis, risk factors, diagnostic approach, evaluation workflow, management strategies, differential diagnosis, type-specific management and clinical staging of CTLI. Emphasis is placed on raising awareness among clinicians and therapeutic practices through comprehensive guidelines. Conclusions:The consensus provides a detailed diagnostic protocol for CTLI and introduces a structured management framework based on grading, typing, and staging. It highlights the critical role of multidisciplinary team (MDT) collaboration and emphasizes the need for individualized, whole-process patient care strategies to optimize clinical outcomes.