With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

ObjectiveIn the realm of forensic science, dust is a valuable type of trace evidence with immense potential for intricate investigations. With the development of DNA sequencing technologies, there is a heightened interest among researchers in unraveling the complex tapestry of microbial communities found within dust samples. Furthermore, striking disparities in the microbial community composition have been noted among dust samples from diverse geographical regions, heralding new possibilities for geographical inference based on microbial DNA analysis. The pivotal role of microbial community data from dust in geographical inference is significant, underscoring its critical importance within the field of forensic science. This study aims to delve deeply into the nuances of fungal community composition across the urban landscapes of Beijing, Fuzhou, Kunming, and Urumqi in China. It evaluates the accuracy of biogeographic inference facilitated by the internal transcribed spacer 2 (ITS2) fungal sequencing while concurrently laying a robust foundation for the operational integration of environmental DNA into geographical inference mechanisms. MethodsITS2 region of the fungal genomes was amplified using universal primers known as 5.8S-Fun/ITS4-Fun, and the resulting DNA fragments were sequenced on the Illumina MiSeq FGx platform. Non-metric multidimensional scaling analysis (NMDS) was employed to visually represent the differences between samples, while analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) were utilized to statistically evaluate the dissimilarities in community composition across samples. Furthermore, using Linear Discriminant Analysis Effect Size (LEfSe) analysis to identify and filter out species that exhibit significant differences between various cities. In addition, we leveraged SourceTracker to predict the geographic origins of the dust samples. ResultsAmong the four cities of Beijing, Fuzhou, Kunming and Urumqi, Beijing has the highest species richness. The results of species annotation showed that there were significant differences in the species composition and relative abundance of fungal communities in the four cities. NMDS analysis revealed distinct clustering patterns of samples based on their biogeographic origins in multidimensional space. Samples from the same city exhibited clear clustering, while samples from different cities showed separation along the first axis. The results from ANOSIM and PERMANOVA confirmed the significant differences in fungal community composition between the four cities, with the most pronounced distinctions observed between Fuzhou and Urumqi. Notably, the biogeographic origins of all known dust samples were successfully predicted. ConclusionSignificant differences are observed in the fungal species composition and relative abundance among the cities of Beijing, Fuzhou, Kunming, and Urumqi. Employing fungal ITS2 sequencing on dust samples from these urban areas enables accurate inference of biogeographical locations. The high feasibility of utilizing fungal community data in dust for biogeographical inferences holds particular promise in the field of forensic science.

Background/Aims: Distal mean nocturnal baseline impedance (MNBI) measuring via pH-impedance may be valuable in diagnosing patients with suspected laryngopharyngeal reflux (LPR). However, its wide adoption is hindered by cost and invasiveness. This study investigates whether baseline impedance measured during high-resolution impedance manometry (HRIM-BI) can predict pathological MNBI. Methods: A cross-sectional study in Taiwan included 74 subjects suspected of LPR, who underwent HRIM (MMS) and pH-impedance testing (Diversatek), after stopping proton pump inhibitors for more than 7 days. Subjects with grade C or D esophagitis or Barrett’s esophagus were excluded. The cohort was divided into 2 groups: those with concomitant typical reflux symptoms (CTRS, n = 28) and those with isolated LPR symptoms (ILPRS, n = 46). HRIM-BI measurements focused on both distal and proximal esophagi. Pathological MNBI was identified as values below 2065 Ω, measured 3 cm above the lower esophageal sphincter. Results: In all subjects, distal HRIM-BI values correlated weakly with distal MNBI(r = 0.34-0.39, P < 0.005). However, in patients with ILPRS, distal HRIM-BI corelated moderately with distal MNBI(r = 0.43-0.48, P < 0.005). The areas under the receiver operating characteristic curve was 0.78 (P = 0.001) with a sensitivity of 0.83 and a specificity of 0.68. No correlation exists between distal HRIM-BI and distal MNBI in patients with CTRS, and between proximal HRIM-BI and proximal MNBI in both groups. Conclusions Distal HRIM-BI from HRIM may potentially predict pathological MNBI in patients with ILPRS, but not in those with CTRS. Future outcome studies linked to the metric are warranted.

Eukaryotic translation initiation factor 5A (eIF5A) is the only known protein in eukaryotes that contains a hydroxyputrescine lysine modification. Only the modified form of eIF5A is biologically active and is widely involved in protein translation, mRNA degradation, autophagy, and other intracellular processes. Epithelial-mesenchymal transition (EMT) is a process in which epithelial cells transform into mesenchymal phenotype cells through a highly regulated program. It plays a key role in embryonic development, tissue regeneration, and wound healing. Based on its biological functions, EMT can be classified into three types: I, II, and III. Type III EMT is the core mechanism underlying malignant tumor cell invasion and metastasis. This EMT mechanism involves the canonical pathway induced by transforming growth factor-β (TGF-β) and is regulated by various growth factors (TRAF6, EGF, IGF, HGF, VEGF), transcription factors (Twist, Slug, NF-κB, E12/E47, SIP1, ZEB1, etc.), and signaling pathways such as Wnt/β-catenin and PEAK1. eIF5A can influence tumor cell proliferation, invasion, and metastasis by regulating EMT-related signaling pathways. The known signaling pathways through which eIF5A regulates EMT include the canonical Smad signaling pathway and non-canonical pathways such as Rho/Rac1, Twist, STAT3, and MAT1. Additionally, certain miRNA family members, such as miR-30b, miR-599, and miR-203, can bind to the 3'-UTR of eIF5A2, inhibiting its expression and subsequently suppressing the EMT process in cancer cells, including gastric cancer and colorectal cancer. GC7, an inhibitor targeting the key enzyme DHPS involved in eIF5A modification, has been shown to reverse the EMT mechanism in oral squamous cell carcinoma, lung cancer, and breast cancer by regulating cytokine-mediated signaling pathways, including HIF-1α, STAT3/c-MYC, and Twist. However, to date, no inhibitors directly targeting eIF5A have been developed. In recent years, the mechanism of eIF5A activation catalyzed by DHPS and DOHH has become increasingly clear. As the only protein involved in lysine deoxyhydroxymethylation, DHPS may play a more critical role than eIF5A in the overall signal transduction process. Through in-depth analysis of the DHPS protein structure and its active site, researchers have shifted their approach to DHPS inhibitor development from substrate analog inhibitors (such as GC7, CNI-1493, DHSI-15, etc.) to allosteric inhibitors (11g, 26d, 8m, GL-1, etc.). GC7 is not suitable for clinical trials due to its lack of specificity and low bioavailability, and the therapeutic potential of novel allosteric inhibitors has yet to be clarified. Therefore, there is a significant gap in the development of covalent drugs targeting DHPS for cancer treatment in clinical settings. This paper reviews the research progress on eIF5A in regulating EMT, focusing on the molecular mechanisms by which eIF5A influences tumor cell invasion and migration. It also discusses the characteristics and current limitations of inhibitors targeting the hypusine pathway, aiming to provide insights for studying tumor metastasis mechanisms and drug discovery.

Objective To investigate the neural basis of glucagon-like peptide-1(GLP-1)in regulating glucose homeostasis and elucidate the molecular mechanisms.Methods Male Glp1r-IRES-Cre,Glp1r-KO,and wild-type mice were used in this study.Fiber photometry was employed to record Ca2+signals of neurons in ventromedial hypothalamus(VMH)and patch-clamp was used to analyze electrophysiological properties of GLP-1 receptor-positive(GLP-1RVMH)neurons.Viral stereotaxic injections,chemogenetics,plasma hormone assays,and routine glucose metabolism assessments were combined to determine the regulatory role of GLP-1RVMH neurons in glucose homeostasis.Tissue and cell mitochondrial respiratory function assays,transmission electron microscopy,and conventional molecular biology methods were used to explore the mechanism by which GLP-1R agonists regulate glucose homeostasis.Results When the glucose concentration decreased from 5.0 mmol/L to 0.5 mmol/L,the action potential frequency of GLP-1RVMH neuron decreased significantly[(4.51±0.80)Hz vs.(1.43±0.51)Hz,P<0.01].Activation of GLP-1RVMH neuron significantly enhanced insulin secretion[(7.60±0.56)μU/mL vs.(11.34±0.93)μU/mL,P<0.01],while inhibition of these neuronal activities impaired the hypoglycemic efficiency of GLP-1 agonists[(32.03%±0.91%)vs.(25.77%±1.09%),P<0.001)].Mechanistically,GLP-1 regulated glucose homeostasis through Drp1 phosphorylation-mediated mitochondrial fission and improved mitochondrial energy metabolism.Conclusion GLP-1RVMH neurons are a class of glucose-excited neurons,and which activated directly promotes secretion of insulin.The hypoglycemic effect of GLP-1R agonists depend on the neuronal activity of GLP-1RVMH.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

The interaction between m⁶A-methylated RNA and chromatin modification remains largely unknown. We found that targeted inhibition of bromodomain-containing protein 4 (BRD4) by siRNA or its inhibitor (JQ1) significantly decreases mRNA m⁶A levels and suppresses the malignancy of breast cancer (BC) cells via increased expression of demethylase AlkB homolog 5 (ALKBH5). Mechanistically, inhibition of BRD4 increases the mRNA stability of ALKBH5 via enhanced binding between its 3' untranslated regions (3'UTRs) with RNA-binding protein RALY. Further, BRD4 serves as a scaffold for ubiquitin enzymes tripartite motif containing-21 (TRIM21) and ALKBH5, resulting in the ubiquitination and degradation of ALKBH5 protein. JQ1-increased ALKBH5 then demethylates mRNA of extra spindle pole bodies like 1 (ESPL1) and reduces binding between ESPL1 mRNA and m⁶A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3), leading to decay of ESPL1 mRNA. Animal and clinical studies confirm a critical role of BRD4/ALKBH5/ESPL1 pathway in BC progression. Further, our study sheds light on the crosstalks between histone modification and RNA methylation.