Alveolar macrophages (AMs) are specialized tissue-resident macrophages that orchestrate the immune response in allergic inflammation and asthma. However, what signals direct AMs to cross talk with other immune cells remains unclear. Here, we report that autocrine motility factor receptor (AMFR), an endoplasmic reticulum–resident E3 ubiquitin ligase, is upregulated in AMs of asthma and is critical for this condition. AMFR deficiency significantly decreased allergy-induced T helper 2 (Th2) and eosinophilic inflammation, with less granulocyte-macrophage colony-stimulating factor (GM-CSF) production in AMs. Mechanistically, following thymic stromal lymphopoietin (TSLP) stimulation, AMFR associated directly with cytokine-inducible SH2-containing protein (CIS), induced the ubiquitination of Lys48-linked polyubiquitination of CIS, and consequently blocked the inhibitory effect of CIS on signal transducer and activator of transcription 5 (STAT5) phosphorylation and the downstream pathway activation in AMs. In conclusion, our results demonstrate that AMFR serves a crucial role in promoting inflammation in asthma through regulating AM function, and may emerge as a new potential drug target for asthma therapy.

Background: Generative artificial intelligence (GenAI) models are increasingly used for medical information retrieval, due to their accessibility and efficiency. However, the accuracy and readability of their responses, specifically for upper gastrointestinal cancers, remain inadequately evaluated. This gap highlights the need for rigorous assessment to ensure reliable patient education and clinical integration. Objective: This study aimed to assess the accuracy and readability of responses generated by four prominent GenAI models (Kimi, DeepSeek, ChatGPT, and Gemini) when addressing patient-focused questions related to esophageal and gastric cancers. Methods: Twenty-five standardized medical questions about esophageal and gastric cancer covering domains of disease definition, treatment and management were posed to each model. Responses were assessed by four oncologists for accuracy by a 5-point Likert scale and analyzed for readability using Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, and SMOG metrics. High-interest questions for patients were identified via questionnaires. Results: Comparing the accuracy of GenAI-generated responses, DeepSeek achieved the highest overall accuracy score and outperformed other models in questions about definitions and treatments, while ChatGPT excelled in management-related inquiries. In subgroup analysis, GenAI models exhibited higher accuracy in answering definition and management questions, which patients preferred to inquire, compared with questions about cancer therapies. The responses produced by all models required a reading capacity from 11th-grade to college level. Conclusions: This study revealed that in this comparative evaluation application of GenAI models, DeepSeek provides the most accurate responses for upper GI cancer inquiries about definition and treatment, while ChatGPT showed superiority in management-related questions. However, all models generate texts requiring advanced reading levels, highlighting a need for readability optimization without compromising accuracy. GenAI shows promise for patient education but requires rigorous validation for clinical integration.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.

Alveolar epithelial type II (AT2) cells participate in epithelial repair and lung immune defense, while the detailed molecular mechanisms through which AT2

Background: Pancreatic cancer (PCA) is a highly malignant tumor with a 5-year survival rate of < 10%. It is characterized as a cold tumor with an immunosuppressive microenvironment. Liver dysfunction due to biliary obstruction can affect the inflammation index, an indicator of immune status. Adjusting inflammation indices for liver function may enhance their clinical utility for predicting overall survival (OS) in PCA patients. Methods: Resected PCA cases were selected using specific criteria. Liver function indicators identified by Spearman’s analysis were integrated into a covariance analysis to refine inflammation indices, including modified neutrophil-to-lymphocyte ratio (mNLR), modified platelet-to-lymphocyte ratio (mPLR), modified lymphocyte-to-monocyte ratio (mLMR), modified systemic immune-inflammation index (mSII), and modified C-reactive protein (mCRP). These modified indices and clinicopathological factors were analyzed to identify independent OS predictors. A nomogram was developed and compared with a primary inflammation-based model using calibration curves, decision curve analysis (DCA), and the concordance index (C-index). Results: Liver function indicators including direct bilirubin (DBIL), indirect bilirubin (IBIL), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), and albumin (ALB) were integrated to refine inflammation indices. In PCA patients, higher mNLR, mSII, CA19-9, T stage, and N stage were associated with worse OS, while higher mLMR or PNI levels correlated with better OS. Vascular invasion predicted poor OS, whereas chemotherapy improved OS. The nomogram model’s clinical utility surpassed that of the primary inflammation-based model. Conclusion: The nomogram incorporating modified inflammation indices demonstrated superior clinical utility. Adjusting inflammation indices for liver function is recommended for prognostic prediction, especially in PCA patients with biliary obstruction. For patients with advanced T and N staging or poorly differentiated tumors, intraoperative margin nanoknife ablation and timely postoperative adjuvant chemotherapy are recommended to enhance prognosis.