Theoretical Study of the Temperature- and Pressure-Dependent Rate Constants for Nine Reactions between COn (n = 0-4), Om (m = 1-3), C2O, and C3O2 during the Radiolysis of Carbon Dioxide.

We investigate the reaction pathways of nine important CO2-related reactions using the revDSD-PBEP86-D3(BJ)/jun-cc-pV(T+d)Z level and simultaneously employ an accurate composite method (jun-Cheap) based on coupled-cluster (CC) theory. Subsequently, the Rice-Ramsperger-Kassel-Marcus/master equation (RRKM/ME) is solved to calculate the temperature- and pressure-dependent rate constants. This work investigates reactions involving transition states that have been overlooked in previous literature, including the dissociation of singlet-state C3O2, the triple channel formation of C2O + CO to form C3O2, and the formation of O3 + CO. The results show that CO3 is highly prone to dissociation at high temperatures. Finally, the kinetic data show that over a wide temperature range, our calculations are consistent with previous experimental measurements. The majority of the reaction rate constants studied exhibit significant pressure dependence, while the O3 + CO reaction is pressure-independent at low temperatures. These results are instrumental in the development of detailed kinetic models for the CO2 radiolysis reaction network.

[Clinical Multi-features Analysis of Cystic Lung Adenocarcinoma 
and Construction of Invasive Risk Prediction Model].

BACKGROUND: Cystic lung cancer, a special type of lung cancer, has been paid more and more attention. The most common pathological type of cystic lung cancer is adenocarcinoma. The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis. The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma, explore the independent risk factors of its invasiveness, and establish a risk prediction model. METHODS: A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA)] and invasive group [invasive adenocarcinoma (IAC)] according to pathological findings. There were 47 cases in the pre-invasive group, including 19 males and 28 females, with an average age of (51.23±14.96) years. There were 82 cases in the invasive group, including 60 males and 22 females, with an average age of (61.27±11.74) years. Multiple clinical features of the two groups were collected, including baseline data, imaging data and tumor markers. Univariate analysis, LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma, and the risk prediction model was established. RESULTS: In univariate analysis, age, gender, smoking history, history of emphysema, neuron-specific enolase (NSE), number of cystic airspaces, lesion diameter, cystic cavity diameter, nodule diameter, solid components diameter, cyst wall nodule, smoothness of cyst wall, shape of cystic airspace, lobulation, short burr sign, pleural retraction, vascular penetration and bronchial penetration were statistically different between the pre-invasive group and invasive groups (P<0.05). The above variables were processed by LASSO regression dimensionality reduction and screened as follows: age, gender, smoking history, NSE, number of cystic airspaces, lesion diameter, cystic cavity diameter, cyst wall nodule, smoothness of cyst wall and lobulation. Then the above variables were included in multivariate Logistic regression analysis. Cyst wall nodule (P=0.035) and lobulation (P=0.001) were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma (P<0.05). The prediction model was established as follows: P=e^x/(1+e^x), x=-7.927+1.476* cyst wall nodule+2.407* lobulation, and area under the curve (AUC) was 0.950. CONCLUSIONS: Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma, which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma.

The safety and efficacy of the fissure-first approach in lung segmentectomy for patients with incomplete fissures.

Background: Lung segmentectomy has gained much more attention as an important surgical method for treating early-stage lung cancer. However, incomplete fissures increase the difficulty of lung segmentectomy. The aim of this study was to analyze the safety and efficacy of the fissure-first approach in precision resection of lung segments for patients with incomplete fissures. Methods: The clinical data of patients with incomplete fissures who underwent lung segmentectomy were retrospectively analyzed. Date was divided into fissure-first approach in lung segmentectomy group (group A) and fissure-last approach in lung segmentectomy group (group B). The general linear data, operation times, intraoperative adverse events, postoperative recovery dates and complications were compared. Results: A total of 122 patients with complete clinical data were included. Patients in group B had more COPD (p < 0.05), and the lesions in group A were more closely related to the hilum of the lung (p < 0.05). Compared to Group B, Group A achieved better surgical outcomes, such as operation time, postoperative hospital stays, intraoperative bleeding, number of intrapulmonary lymph nodes sampled, counts of resected subsegments (except the upper lobe of the right lung), and rate of conversion to thoracotomy (all p < 0.05). Conclusion: The fissure-first approach is a safe and effective surgical approach in lung segmentectomy for patients with incomplete fissures. This approach can reduce the counts of resected subsegments and improve techniques in lung segmentectomy for patients with lung incomplete fissures.

Bento: a toolkit for subcellular analysis of spatial transcriptomics data.

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

An end-to-end multi-scale airway segmentation framework based on pulmonary CT image.

Objective. Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation.Approach. In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 asa prioriinformation. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs.Main results. We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%.Significance. The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.

Formononetin attenuates psoriasiform inflammation by regulating interferon signaling pathway.

BACKGROUND: Psoriasis is a long-lasting, inflammatory, continuous illness caused through T cells and characterized mainly by abnormal growth and division of keratinocytes. Currently, corticosteroids are the preferred option. However, prolonged use of traditional topical medication can lead to adverse reactions and relapse, presenting a significant therapeutic obstacle. Improved alternative treatment options are urgently required. Formononetin (FMN) is a representative component of isoflavones in Huangqi (HQ) [Astragalus membranaceus (Fisch.) Bge.]. It possesses properties that reduce inflammation, combat oxidation, inhibit tumor growth, and mimic estrogen. Although FMN has been shown to ameliorate skin barrier devastation via regulating keratinocyte apoptosis and proliferation, there are no reports of its effectiveness in treating psoriasis. OBJECTIVE: Through transcriptomics clues and experimental investigation, we aimed to elucidate the fundamental mechanisms underlying FMN's action on psoriasis. MATERIALS AND METHODS: Cell viability was examined using CCK8 assay in this study. The results of analysis of differentially expressed genes (DEGs) between FMN-treated HaCaT cells and normal HaCaT cells using RNA-sequencing (RNA-seq) were presented on volcano plots and heatmap. Enrichment analysis was conducted on DEGs using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), and results were validated through RT-qPCR verification. After 12 days of FMN treatment in psoriasis mouse model, we gauged the PASI score and epidermis thickness. A variety of techniques were used to assess FMN's effectiveness on inhibiting inflammation and proliferation related to psoriasis, including RT-qPCR, HE staining, western blot, and immunohistochemistry (IHC). RESULTS: The findings indicated that FMN could suppress the growth of HaCaT cells using CCK8 assay (with IC50 = 40.64 uM) and 20 uM FMN could reduce the level of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) to the greatest extent. FMN-treated HaCaT cells exhibited 985 up-regulated and 855 down-regulated DEGs compared to normal HaCaT cells. GO analysis revealed that DEGs were linked to interferon (IFN) signaling pathway. Furthermore, FMN improved pathological features, which encompassed decreased erythema, scale, and thickness scores of skin lesions in psoriasis mouse model. In vivo experiments confirmed that FMN down-regulated expression of IFN-α, IFN-ß, IFN-γ, decreased secretion of TNF-α and IL-17 inflammatory factors, inhibited expression of IFN-related chemokines included Cxcl9, Cxcl10, Cxcl11 and Cxcr3 and reduced expression of transcription factors p-STAT1, p-STAT3 and IFN regulatory factor 1 (IRF1) in the imiquimod (IMQ) group. CONCLUSIONS: In summary, these results suggested that FMN played an anti-inflammatory and anti-proliferative role in alleviating psoriasis by inhibiting IFN signaling pathway, and FMN could be used as a potential therapeutic agent.

Feasibility of thoracoscopic monosubsegmentectomy for small ground-glass opacity dominant lung cancer.

BACKGROUND: Monosubsegmentectomy (MSS) involves removal of less lung parenchyma than monosegmentectomy (MS) does. However, the clinical efficacy of MSS in lung cancer treatment remains unclear, with concerns regarding insufficient surgical margins and increased complications. METHODS: Between February 2015 and December 2019, patients who underwent thoracoscopic MSS (n = 126) or MS (n = 678) for small ground-glass opacity (GGO) dominant lung cancer were examined. The primary endpoints were the procedure success rate (defined as resection with a surgical margin ≥2 cm or tumor size) and surgical margin. RESULTS: There were no significant differences in age, sex, smoking history, or comorbidities between the groups. Both groups achieved a success rate of 100%. No significant group differences were observed in the number of lymph nodes removed (p = 0.060), overall complications (p = 0.147), or major complications (p = 0.450). The MSS group had a smaller surgical margin (median, 17 vs. 21mm, p < 0.001) and longer operative time (median, 138 vs. 130 min, p = 0.005) than the MS group did. Propensity score-matched analysis of 122 pairs of patients revealed consistent results. Surgical margins were further compared based on the number of resected subsegments. The 1 subsegment group had the smallest median surgical margin, followed by the 2 and 3 subsegments groups (17, 20, and 23 mm, p < 0.001). CONCLUSIONS: Thoracoscopic MSS is an acceptable option for treating patients with small-sized GGO-dominant lung cancer. However, the close surgical margins and prolonged operative time should be considered.

Spatially organized cellular communities form the developing human heart.

The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.

Airway necrosis and granulation tissue formation caused by Rhizopus oryzae leading to severe upper airway obstruction: a case report.

Pulmonary Mucormycosis is a fatal infectious disease with high mortality rate. The occurrence of Mucormycosis is commonly related to the fungal virulence and the host's immunological defenses against pathogens. Mucormycosis infection and granulation tissue formation occurred in the upper airway was rarely reported. This patient was a 60-year-old male with diabetes mellitus, who was admitted to hospital due to progressive cough, sputum and dyspnea. High-resolution computed tomography (HRCT) and bronchoscopy revealed extensive tracheal mucosal necrosis, granulation tissue proliferation, and severe airway stenosis. The mucosal necrotic tissue was induced by the infection of Rhizopus Oryzae, confirmed by metagenomic next-generation sequencing (mNGS) in tissue biopsy. This patient was treated with the placement of a covered stent and local instillation of amphotericin B via bronchoscope. The tracheal mucosal necrosis was markedly alleviated, the symptoms of cough, shortness of breath, as well as exercise tolerance were significantly improved. The placement of airway stent and transbronchial microtube drip of amphotericin B could conduce to rapidly relieve the severe airway obstruction due to Mucormycosis infection.

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management.

In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity-relation interactions in sets containing potential overlaps, reducing the impact of entity-relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

Anti-CD99 Antibody Therapy Triggers Macrophage-Dependent Ewing Cell Death In Vitro and Myeloid Cell Recruitment In Vivo.

BACKGROUND: Ewing sarcoma is a rare tumor of the bone or soft tissues characterized by diffuse membranous staining for CD99. As this tumor remains incurable in the metastatic, relapsed, and refractory settings, we explored the downstream immune implications of targeting CD99. METHODS: We discovered a human anti-CD99 antibody (NOA2) by phagemid panning and investigated NOA2 immune cell-mediated cytotoxicity in vitro and in vivo focusing on the myeloid cell compartment, given that M2 macrophages are present in human tumors and associated with a poor prognosis. RESULTS: NOA2 is capable of inducing immune effector cell-mediated Ewing death in vitro via engagement of macrophages. Mice with metastatic Ewing tumors, treated with NOA2, experience tumor growth arrest and an associated increase in intratumoral macrophages. Further, incubation of macrophages and Ewing cells with NOA2, in conjunction with anti-PILRα antibody blockade in vitro, results in the reactivation of previously dormant macrophages possibly due to interrupted binding of Ewing CD99 to macrophage PILRα. CONCLUSIONS: These studies are the first to demonstrate the role of human immune effector cells in anti-CD99-mediated Ewing tumor death. We propose that the engagement of CD99 by NOA2 results in the recruitment of intratumoral macrophages. In addition, interruption of the CD99:PILRα checkpoint axis may be a relevant therapeutic approach to activate tumor-associated macrophages.

Metabolomic Analysis Reveals Association between Decreased Ovarian Reserve and In Vitro Fertilization Outcomes.

In vitro fertilization (IVF) is a highly effective treatment for infertility; however, it poses challenges for women with decreased ovarian reserve (DOR). Despite the importance of understanding the impact of DOR on IVF outcomes, limited research has explored this relationship, particularly using omics approaches. Hence, we conducted a study to investigate the association between DOR and IVF outcomes, employing a metabolomic approach. We analyzed serum samples from 207 women undergoing IVF treatment, including 89 with DOR and 118 with normal ovarian reserve (NOR). Our findings revealed that DOR was significantly associated with unfavorable IVF outcomes, characterized by a reduced oocyte count, lower embryo quality, and decreased rates of pregnancy and live births. Furthermore, we identified 82 metabolites that displayed significant alterations in DOR patients, impacting diverse metabolic pathways. Notably, a distinct panel of metabolites, including palmitic acid, stearic acid, LysoPC(9:0(CHO)/0:0), PC(18:0/9:0(CHO)), and PC(16:0/9:0(CHO)), exhibited discriminatory power between the DOR and NOR groups, showcasing a strong correlation with IVF outcomes. These findings emphasize the crucial role of metabolomic disruptions in influencing IVF outcomes among women with DOR.

CCNB1 may as a biomarker for the adipogenic differentiation of adipose-derived stem cells in the postoperative fat transplantation of breast cancer.

Background: Adipose-derived stem cells (ADSCs) are closely associated with the survival rate of transplanted fat in breast reconstruction after breast cancer surgery. Nevertheless, the intrinsic mechanisms regulating ADSCs adipogenic differentiation remain ambiguous. The aim of our study was to explore the relevant genes and pathways to elucidate the potential mechanisms of adipogenic differentiation in ADSCs. Methods: The Gene Expression Omnibus (GEO) dataset GSE61302 was downloaded and analyzed to identify differentially expressed genes (DEGs). Key genes and signaling pathways were obtained through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional and enrichment analysis. Protein-protein interaction (PPI) network and hub gene analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Finally, the transcription levels of hub genes in the adipogenic differentiated group and undifferentiated group of ADSCs were compared via real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 1,091 DEGs were identified through bioinformatics analysis of the adipogenic differentiated group and undifferentiated group. If was then found that the 10 downregulated key genes, CCNB1, NUSAP1, DLGAP5, TTK, CCNB2, KIF23, BUB1B, CDC20, CDCA8, and KIF11 may play important roles in the adipogenic differentiation of ADSCs. Subsequent in vitro experimental verification also revealed that the messenger RNA (mRNA) expression levels of cyclin B1 in adipogenic differentiated cells and undifferentiated cells were significantly different at the early stage (P<0.05), but there was no significant difference at the late stage (P>0.05). Conclusions: As a key gene, CCNB1 might be a potential biomarker in the adipogenic differentiation of ADSCs at the early stage.

Immune-restoring CAR-T cells display antitumor activity and reverse immunosuppressive TME in a humanized ccRCC mouse model.

One of the major barriers that have restricted successful use of chimeric antigen receptor (CAR) T cells in the treatment of solid tumors is an unfavorable tumor microenvironment (TME). We engineered CAR-T cells targeting carbonic anhydrase IX (CAIX) to secrete anti-PD-L1 monoclonal antibody (mAb), termed immune-restoring (IR) CAR G36-PDL1. We tested CAR-T cells in a humanized clear cell renal cell carcinoma (ccRCC) orthotopic mouse model with reconstituted human leukocyte antigen (HLA) partially matched human leukocytes derived from fetal CD34+ hematopoietic stem cells (HSCs) and bearing human ccRCC skrc-59 cells under the kidney capsule. G36-PDL1 CAR-T cells, haploidentical to the tumor cells, had a potent antitumor effect compared to those without immune-restoring effect. Analysis of the TME revealed that G36-PDL1 CAR-T cells restored active antitumor immunity by promoting tumor-killing cytotoxicity, reducing immunosuppressive cell components such as M2 macrophages and exhausted CD8+ T cells, and enhancing T follicular helper (Tfh)-B cell crosstalk.

[Alignment method for metabolite chromatographic peaks using an N-acyl glycine retention index system].

Peak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N-acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.

CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology.

PURPOSE: Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model. METHODS: Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted. RESULTS: The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS. CONCLUSION: CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.

Hierarchical classification of early microscopic lung nodule based on cascade network.

Purpose: Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules. Methods: This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components. Results: In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04%, outperforming the conventional multi-classification approach. Conclusions: Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.