A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

Uric acid-driven NLRP3 inflammasome activation triggers lens epithelial cell senescence and cataract formation.

Excessive uric acid (UA) is associated with age-related cataract. A previous study showed that a high UA level in the aqueous humor stimulated the senescence of lens epithelial cells (LECs), leading to cataract progression. To better understand the underlying mechanisms, we investigated UA-driven senescence in human lens tissue samples obtained during surgery, rat lens organ cultures, and in vivo experiments, using senescence-associated ß-galactosidase (SA-ß-gal) staining, electronic microscopy, Western blotting, and histological analyses. Initially, we identified markedly higher expressions of NLRP3 and caspase-1 in the lens capsules of hyper-uricemic patients compared to normo-uricemic patients. This increase was accompanied by a significant rise in the SA-ß-gal positive rate. We next built a cataract model in which rat lenses in an organ culture system were treated with an increasing dosage of UA. Notably, opacification was apparent in the lenses treated with 800 µM of UA starting on the fifth day. Mechanistically, UA treatment not only significantly induced the expression of NLRP3, caspase-1, and IL-1ß, but also upregulated the levels of SA-ß-gal and the senescence regulators p53 and p21. These effects were fully reversed, and lens opacification was ameliorated by the addition of MCC950, a selective NLRP3 antagonist. Moreover, an in vivo model showed that intravitreal UA injection rapidly induced cataract phenotypes within 21 days, an effect significantly mitigated by co-injection with MCC950. Together, our findings suggest that targeting the UA-induced NLRP3 inflammasome with MCC950 could be a promising strategy for preventing cataract formation associated with inflammageing.

Bacterial Cytochrome P450 Catalyzed Post-translational Macrocyclization of Ribosomal Peptides.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a fascinating group of natural products that exhibit diverse structural features and bioactivities. P450-catalyzed RiPPs stand out as a unique but underexplored family. Herein, we introduce a rule-based genome mining strategy that harnesses the intrinsic biosynthetic principles of RiPPs, including the co-occurrence and co-conservation of precursors and P450s and interactions between them, successfully facilitating the identification of diverse P450-catalyzed RiPPs. Intensive BGC characterization revealed four new P450s, KstB, ScnB, MciB, and SgrB, that can catalyze the formation of Trp-Trp-Tyr (one C-C and two C-N bonds), Tyr-Trp (C-C bond), Trp-Trp (C-N bond), and His-His (ether bond) crosslinks, respectively, within three or four residues. KstB, ScnB, and MciB could accept non-native precursors, suggesting they could be promising starting templates for bioengineering to construct macrocycles. Our study highlights the potential of P450s to expand the chemical diversity of strained macrocyclic peptides and the range of biocatalytic tools available for peptide macrocyclization.

A multicenter prospective study of comprehensive metagenomic and transcriptomic signatures for predicting outcomes of patients with severe community-acquired pneumonia.

BACKGROUND: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients. METHODS: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study. FINDINGS: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98). INTERPRETATION: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP. FUNDING: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).

Serum levels of IL-6/IL-10/GLDH may be early recognition markers of anti-tuberculosis drugs (ATB) -induced liver injury.

To explore the potential value of serum glutamate dehydrogenase (GLDH) combined with inflammatory cytokines as diagnostic biomarkers for anti-tuberculosis drug -induced liver injury (ATB-DILI). We collected the residual serum from the patients who met the criteria after liver function tests. We have examined these parameters including GLDH which were determined by enzyme-linked immunosorbent assay and cytokines which were determined by cytokine combination detection kit. Multivariate logistics stepwise forward regression was applied to establish regression models. A total of 138 tuberculosis patients were included in the diagnostic markers study of ATB-DILI, including normal liver function group (n = 108) and ATB-DILI group(n = 30). Serum GLDH, IL-6 and IL-10 levels were significantly increased in the ATB-DILI group. Receiver operating characteristic curve (ROC) curve showed that the area under curve (AUC) of serum GLDH, IL-6 and IL-10 for the diagnosis of ATB-DILI were 0.870, 0.714 and 0.811, respectively. In logistic regression modeling, the AUC of GLDH combined with IL-10 as an ATB-DILI marker is 0.912. Serum IL-6、IL-10 and GLDH levels began to rise preceded the increase in ALT by 7 days, with significant differences in IL-6 compared with 7 days. Serum GLDH, IL-6 and IL-10 levels were correlated with the severity of liver injury. In conclusion, we found that GLDH, IL-6 and IL-10 alone as diagnostic markers of ATB-DILI had good diagnostic efficacy. Logistic regression model established by GLDH and IL-10 had better diagnostic efficacy and IL-6 may be an early predictor of liver injury in the setting of ATB poisoning.

Nirmatrelvir plus ritonavir remains effective in vaccinated patients at risk of progression with COVID-19: A systematic review and meta-analysis.

Background: The efficacy of nirmatrelvir plus ritonavir (NMV-r) for vaccinated COVID-19 patients at high risk of progression is not adequately recognised. To address this gap, we conducted a systematic review and meta-analysis of current literature. Methods: We searched PubMed, Web of Science, Embase, Cochrane Library, and medRxiv for articles published up to 8 January 2023 on NMV-r in outpatients. At least two researchers screened articles, extracted data, and assessed the quality of selected studies. We evaluated the results via risk ratios (RRs) with 95% confidence intervals (CIs) and tested for heterogeneity using I2 statistics. Results: We included seven observational cohort studies comprising 224 238 vaccinated patients. According to our meta-analysis, NMV-r reduced 47% incidence of all-cause death or hospitalisation within 30 days for vaccinated patients (RR = 0.53; 95% CI = 0.40-0.70; I2 = 81%). After excluding the most influential result by sensitivity analysis, NMV-r still reduced risk of all-cause death or hospitalisation by 38% (RR = 0.62; 95% CI = 0.56-0.69; I2 = 0%). In our secondary outcome, NMV-r also showed its benefits in reducing all-cause death in vaccinated patients (RR = 0.40; 95% CI = 0.19-0.85; I2 = 23%). Conclusions: We found positive evidence for the use of NMV-r for vaccinated patients at high-risk of progression with mild to moderate COVID-19. However, large-scale RCTs are needed to confirm these findings. Registration: PROSPERO CRD42023391349.

Glutamate dehydrogenase combined with ferrochelatase as a biomarker of liver injury induced by antituberculosis drugs.

AIMS: To explore the potential value of serum glutamate dehydrogenase (GLDH), ferrochelatase (FECH), heme oxygenase-1 (HO-1) and glutathione-S-transferase-α (GST-α) as diagnostic biomarkers for liver injury caused by antituberculosis drugs. METHODS: We established a rat model of isoniazide-induced liver injury and recruited 122 hospitalized tuberculosis patients taking antituberculosis drugs. We detected the concentration of GLDH, FECH, HO-1 and GST-α by enzyme-linked immunosorbent assay. GraphPad Prism8 and SPSS 26.0 were used for statistical analysis. RESULTS: In the rat model, serum GLDH concentration gradually increased during isoniazid (INH) administration, while serum FECH, HO-1 and GST-α concentrations significantly increased after INH administration was stopped. The receiver operating characteristic curve showed that the areas under the curve (AUCs) of serum GLDH and FECH for the diagnosis of anti-tuberculosis (TB) drug-induced liver injury (anti-TB-DILI) were 0.7692 (95% confidence interval [CI] 0.5442-0.9943) and 0.7284 (95% CI 0.4863-0.9705) and the diagnostic accuracies were 81.25% and 78.79%, respectively. In clinical research, the AUCs of GLDH and FECH were 0.9124 (95% CI 0.8380-0.9867) and 0.6634 (95% CI 0.5391-0.7877), and the optimal thresholds were 10.40 mIU/mL and 1.304 ng/mL, respectively. The diagnostic accuracy, specificity and positive predictive value (PPV) of GLDH were 82.61%, 79.38% and 47.22%. We performed a joint diagnostic test for GLDH and FECH. The diagnostic accuracy (90.43%), specificity (91.75%) and PPV (65.21%) of serial tests were better than for GLDH and FECH alone. CONCLUSIONS: GLDH in the diagnosis of liver injury induced by anti-TB drugs has high sensitivity, but low specificity and low PPV. The combination of GLDH and FECH could significantly improve the specificity, PPV and diagnostic accuracy, and reduce the false-positive rate of anti-TB-DILI.

Scale-Spanning Strong Adhesion Using Cellulose-Based Microgels.

Adhesive gels derived from biobased sustainable materials have extremely broad application prospects, such as in flexible smart materials and biomedicine fields. Combining high toughness and strong, persisting repeatable adhesion has always been a daunting challenge for adhesive gels. However, bulk gels based on polysaccharides as the most abundant bio-based compounds usually possess a high toughness but weak interfacial adhesion due to the strong hydration potential. Herein, a novel kind of highly tough microgel membranes with rough surfaces is fabricated using loosely chemically cross-linked dihydroxypropyl cellulose (cDHPC) microgels (average size = 1.25 ± 0.03 µm). Such microgel membranes exhibit strong, instant, and persisting adhesion to various substrates with different surface roughness. Slight chemical cross-linking and multiple physical interactions within microgels and resulting microgel membranes lead to high tensile strength and toughness of 0.23 ± 0.03 MPa and 73.8 ± 9.3 KJ m-3 , respectively. The maximum adhesive strength and debonding work exceed 320 ± 0.50 KPa and 160.97 ± 0.20 J m-2 , respectively. After five cycles (re-lap after detaching), the adhesive strength still remains above 200 KPa. Their adhesive properties outperform most bio-based adhesive gels and even petroleum-based gels, which are based on synergistic molecular and microscaled topological interactions.

High-Flow Nasal Cannula versus Noninvasive Ventilation in AECOPD Patients with Respiratory Acidosis: A Retrospective Propensity Score-Matched Study.

Background: Limited data are available about the clinical outcomes of AECOPD patients with respiratory acidosis treated with HFNC versus NIV. Methods: We conducted a retrospective study to compare the efficacy of HFNC with NIV as initial ventilation support strategy in AECOPD patients with respiratory acidosis. Propensity score matching (PSM) was implemented to increase between-group comparability. Kaplan-Meier analysis was utilized to evaluate differences between the HFNC success, HFNC failure, and NIV groups. Univariate analysis was performed to identify the features that differed significantly between the HFNC success and HFNC failure groups. Results: After screening 2219 hospitalization records, 44 patients from the HFNC group and 44 from the NIV group were successfully matched after PSM. The 30-day mortality (4.5% versus 6.8%, p = 0.645) and 90-day mortality (4.5% versus 11.4%, p = 0.237) did not differ between the HFNC and NIV groups. Length of ICU stay (median: 11 versus 18 days, p = 0.001), length of hospital stay (median: 14 versus 20 days, p = 0.001), and hospital cost (median: 4392 versus 8403 $USD, p = 0.001) were significantly lower in the HFNC group compared with NIV group. The treatment failure rate was much higher in the HFNC group than in the NIV group (38.6% versus 11.4%, p = 0.003). However, patients who experienced HFNC failure and switched to NIV showed similar clinical outcomes to those who first received NIV. Univariate analysis showed that log NT-proBNP was an important factor for HFNC failure (p = 0.007). Conclusions: Compared with NIV, HFNC followed by NIV as rescue therapy may be a viable initial ventilation support strategy for AECOPD patients with respiratory acidosis. NT-proBNP may be an important factor for HFNC failure in these patients. Further well-designed randomized controlled trials are needed for more accurate and reliable results.

A systematically biosynthetic investigation of lactic acid bacteria reveals diverse antagonistic bacteriocins that potentially shape the human microbiome.

BACKGROUND: Lactic acid bacteria (LAB) produce various bioactive secondary metabolites (SMs), which endow LAB with a protective role for the host. However, the biosynthetic potentials of LAB-derived SMs remain elusive, particularly in their diversity, abundance, and distribution in the human microbiome. Thus, it is still unknown to what extent LAB-derived SMs are involved in microbiome homeostasis. RESULTS: Here, we systematically investigate the biosynthetic potential of LAB from 31,977 LAB genomes, identifying 130,051 secondary metabolite biosynthetic gene clusters (BGCs) of 2,849 gene cluster families (GCFs). Most of these GCFs are species-specific or even strain-specific and uncharacterized yet. Analyzing 748 human-associated metagenomes, we gain an insight into the profile of LAB BGCs, which are highly diverse and niche-specific in the human microbiome. We discover that most LAB BGCs may encode bacteriocins with pervasive antagonistic activities predicted by machine learning models, potentially playing protective roles in the human microbiome. Class II bacteriocins, one of the most abundant and diverse LAB SMs, are particularly enriched and predominant in the vaginal microbiome. We utilized metagenomic and metatranscriptomic analyses to guide our discovery of functional class II bacteriocins. Our findings suggest that these antibacterial bacteriocins have the potential to regulate microbial communities in the vagina, thereby contributing to the maintenance of microbiome homeostasis. CONCLUSIONS: Our study systematically investigates LAB biosynthetic potential and their profiles in the human microbiome, linking them to the antagonistic contributions to microbiome homeostasis via omics analysis. These discoveries of the diverse and prevalent antagonistic SMs are expected to stimulate the mechanism study of LAB's protective roles for the microbiome and host, highlighting the potential of LAB and their bacteriocins as therapeutic alternatives. Video Abstract.

Whole transcriptome analysis reveals that immune infiltration- lncRNAs are related to cellular apoptosis in liver transplantation.

Introduction: In most instances, liver transplantation (LT) is the only available treatment for end-stage liver diseases. However, LT could also induce serious liver diseases or injury, and the underlying mechanisms of LT-induced complications remain largely unknown, especially the mechanisms of the dysfunction of the immune system mediated by long noncoding RNAs (lncRNAs). Methods: In this study, we globally analyzed the proportion of immune cells by using the transcriptome sequencing data (RNA-seq) of needle-core liver biopsies from pre- and post-transplantation recipients. Dysregulated lncRNAs were found to be correlated with the altered fractions of immune cells. We finally explored the potential targets of dysregulated lncRNAs and analyzed their functions in LT. Results: We found that in the samples, some immune cells changed significantly after LT, including CD4 T cells, NK cells and mast cells. The proportion of macrophages in different polarization states also changed significantly, with M0 macrophages increasing and M2 macrophages decreasing. Through weighted gene co-expression network analysis (WGCNA), 7 gene expression modules related to LT were identified. These modules were related to changes in the proportion of different immune cells. The functions of these modules represent the response modes of different functional genes after LT. Among these modules, MEtan and MEyellow modules were primarily enriched in apoptosis and inflammatory pathways. Twelve immunity-related lncRNAs were identified for the first time, and the regulatory network co-changing with immune cells was also identified. The co-expressed genes of these lncRNAs were highly enriched in apoptosis-related pathways. Many apoptosis-related genes were found to be up-regulated after LT. Discussion: In summary, we speculated that the expression and regulation of these apoptotic genes may be related to the changes in the proportion of immune cells. Some of these lncRNAs and apoptosis-related genes have been reported to be related to cell proliferation and apoptosis. They are also potential biomarkers or therapeutic targets.

A Effectiveness-and Efficiency-Based Improved Approach for Measuring Ecological Well-Being Performance in China.

Finding solutions to the challenges posed by China's urbanization is an urgent, pressing global concern. An effective approach for evaluating the ecological well-being performance (EWP) is a guideline for improvement. Most previous studies have focused on the evaluation of EWP efficiency without considering the effectiveness of the EWP, which may mislead the practice of improving the EWP. This paper proposed a bi-dimensional effectiveness and efficiency perspective evaluation of the EWP for pursuing sustainable development goals. The Ecological Consumption Index and the Human Development Index are selected to evaluate indicators for the EWP. The entropy method, line-weighted method, and four-quadrant evaluation framework are used to disclose EWP effectiveness. A Super SBM model and the DEA moving split-windows analysis method are applied to calculate the EWP efficiency. Data from 30 provinces in China for the period of 1997 to 2019 have been collected for empirical study to demonstrate the effectiveness of the proposed method. The main findings of the case study are: (1) The ECI and HDI increased during the study period, while the annual average value of the EWP efficiency among 30 provinces in China has decreased with fluctuation; (2) provinces in southern China and Chongqing have a low level of ECI and demonstrate good performance in the HDI; and (3) most developed regions, such as Beijing, Shanghai, and Guangdong, have not presented the best EWPs. The results of this study can provide a basis for understanding the EWP in China so as to formulate targeted sustainable-development strategies.

Expanded Sequence Space of Radical S-Adenosylmethionine-Dependent Enzymes Involved in Post-translational Macrocyclization.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent one of the largest but primarily underexplored natural product families in bacteria. The genetically encoded nature of RiPPs simplifies the prediction and prioritization of their biosynthetic gene clusters (BGCs). We report a small peptide and enzyme co-occurrence analysis workflow (SPECO), which allowed us to identify 32 220 prospective rSAM-catalyzed RiPP BGCs from 161 954 bacterial genomes and prioritize 25 families with new biosynthetic architectures or precursor patterns. We characterized three new enzymes that respectively catalyze cysteine-glycine (BlaB), histidine-aliphatic side chain (ScaB), and tyrosine/histidine-arginine (VguB) cross-links. The cyclophane-forming enzyme ScaB exhibits broad substrate selectivity, allowing it to catalyze diverse triceptide formation. These results demonstrate the strength of the SPECO workflow in discovering new enzymes for peptide macrocyclization.

Development of an Artificial Intelligence Model for Analyzing the Relationship between Imaging Features and Glucocorticoid Sensitivity in Idiopathic Interstitial Pneumonia.

High-resolution CT (HRCT) imaging features of idiopathic interstitial pneumonia (IIP) patients are related to glucocorticoid sensitivity. This study aimed to develop an artificial intelligence model to assess glucocorticoid efficacy according to the HRCT imaging features of IIP. The medical records and chest HRCT images of 150 patients with IIP were analyzed retrospectively. The U-net framework was used to create a model for recognizing different imaging features, including ground glass opacities, reticulations, honeycombing, and consolidations. Then, the area ratio of those imaging features was calculated automatically. Forty-five patients were treated with glucocorticoids, and according to the drug efficacy, they were divided into a glucocorticoid-sensitive group and a glucocorticoid-insensitive group. Models assessing the correlation between imaging features and glucocorticoid sensitivity were established using the k-nearest neighbor (KNN) algorithm. The total accuracy (ACC) and mean intersection over union (mIoU) of the U-net model were 0.9755 and 0.4296, respectively. Out of the 45 patients treated with glucocorticoids, 34 and 11 were placed in the glucocorticoid-sensitive and glucocorticoid-insensitive groups, respectively. The KNN-based model had an accuracy of 0.82. An artificial intelligence model was successfully developed for recognizing different imaging features of IIP and a preliminary model for assessing the correlation between imaging features and glucocorticoid sensitivity in IIP patients was established.

Combustion-Derived Particulate PAHs Associated with Small Airway Dysfunction in Elderly Patients with COPD.

Evidence of the respiratory effects of ambient organic aerosols (e.g., polycyclic aromatic hydrocarbons, PAHs) among patients with chronic diseases is limited. We aimed to assess whether exposure to ambient particle-bound PAHs could worsen small airway functions in patients with chronic obstructive pulmonary disease (COPD) and elucidate the underlying mechanisms involved. Forty-five COPD patients were recruited with four repeated visits in 2014-2015 in Beijing, China. Parameters of pulmonary function and pulmonary/systemic inflammation and oxidative stress were measured at each visit. Linear mixed-effect models were performed to evaluate the associations between PAHs and measurements. In this study, participants experienced an average PAH level of 61.7 ng/m3. Interquartile range increases in exposure to particulate PAHs at prior up to 7 days were associated with reduced small airway functions, namely, decreases of 17.7-35.5% in forced maximal mid-expiratory flow. Higher levels of particulate PAHs were also associated with heightened lung injury and inflammation and oxidative stress. Stronger overall effects were found for PAHs from traffic emissions and coal burning. Exposure to ambient particulate PAHs was capable of impairing small airway functions in elderly patients with COPD, potentially via inflammation and oxidative stress. These findings highlight the importance of control efforts on organic particulate matter from fossil fuel combustion emissions.

KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning.

PURPOSE: We aimed to investigate the usefulness of Zernike coefficients (ZCs) for distinguishing subclinical keratoconus (KC) from normal corneas and to evaluate the goodness of detection of the entire corneal topography and tomography characteristics with ZCs as a screening feature input set of artificial neural networks. METHODS: This retrospective study was conducted at the Affiliated Eye Hospital of Wenzhou Medical University, China. A total of 208 patients (1040 corneal topography images) were evaluated. Data were collected between 2012 and 2018 using the Pentacam system and analyzed from February 2019 to December 2021. An artificial neural network (KeratoScreen) was trained using a data set of ZCs generated from corneal topography and tomography. Each image was previously assigned to 3 groups: normal (70 eyes; average age, 28.7 ± 2.6 years), subclinical KC (48 eyes; average age, 24.6 ± 5.7 years), and KC (90 eyes; average age, 25.9 ± 5.4 years). The data set was randomly split into 70% for training and 30% for testing. We evaluated the precision of screening symptoms and examined the discriminative capability of several combinations of the input set and nodes. RESULTS: The best results were achieved using ZCs generated from corneal thickness as an input parameter, determining the 3 categories of clinical classification for each subject. The sensitivity and precision rates were 93.9% and 96.1% in subclinical KC cases and 97.6% and 95.1% in KC cases, respectively. CONCLUSIONS: Deep learning algorithms based on ZCs could be used to screen for early KC and for other corneal ectasia during preoperative screening for corneal refractive surgery.

Diagnostic Value of Artificial Intelligence Based on CT Image in Benign and Malignant Pulmonary Nodules.

Objective: To evaluate the diagnostic value of artificial intelligence-assisted CT imaging in benign and malignant pulmonary nodules. Methods: The CT scan screening of pulmonary nodules from November 2018 to November 2020 was retrospectively collected. The diagnosis of pulmonary nodules and surgical treatment were performed. A total of 194 nodules in 152 patients with clear pathological results were observed. All patients underwent CT examination to analyze the consistency of the results of artificial intelligence, physician reading according to imaging features, multidisciplinary team work (MDT) diagnosis, and postoperative pathological results; the diagnostic efficacy of different diagnostic methods for solitary pulmonary nodules and the differences of ROC curve and AUC were analyzed. The accuracy, specificity, sensitivity, positive predictive value, negative predictive value, false negative rate, and false positive rate of different diagnostic methods for pulmonary nodules were calculated, and the ROC curves of different diagnostic methods were plotted. Results: The accuracy, sensitivity, specificity, and Youden index of artificial intelligence (AI) were 89.69%, 92.98%, 65.22%, and 58.20%; the accuracy, sensitivity, specificity, and Youden index of physician reading were 85.57%, 88.30%, 65.22%, and 53.52%; the accuracy, sensitivity, specificity, and Youden index of MDT were 96.91%, 98.25%, 86.96%, and 85.21%, respectively. The kappa values of artificial intelligence, physician reading, and MDT were 0.541, 0.437, and 0.852, and the AUC was 0.768, 0.791, and 0.926, respectively (P < 0.001). The average detection time of pulmonary nodules in the AI group, manual reading group, and MAT group was (145 ± 97) s, (534 ± 297) s, and (421 ± 128) s (P < 0.001). Conclusion: Artificial intelligence pulmonary nodule detection system can improve the coincidence rate and accuracy of early diagnosis of lung cancer, shorten the average detection time, and provide more accurate information for clinical decision-making.