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.

Background: Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury. Objective: To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed. Methods: First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues. Results: IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10 ⁻¹⁰; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10 ⁻⁷). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets. Conclusion: Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Lung cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, and its treatment continues to face major challenges such as therapeutic resistance and tumor recurrence. Pyroptosis, a newly characterized form of programmed cell death, induces tumor cell death through gasdermin-mediated membrane pore formation and is accompanied by the release of inflammatory mediators, thereby playing complex roles in lung cancer initiation, progression, and modulation of the tumor microenvironment. Active components and herbal formulas derived from traditional Chinese medicine can modulate pyroptosis-related signaling pathways through multi-target mechanisms, showing potential advantages in inducing lung cancer cell death, inhibiting proliferation and migration, and reversing chemoresistance. This review systematically summarizes relevant studies from domestic and international sources, focusing on the molecular mechanisms of pyroptosis, its roles in lung cancer development and tumor microenvironment remodeling, and the current research progress on traditional Chinese medicine-based interventions targeting pyroptosis, with the aim of providing references for the prevention and treatment of lung cancer using traditional Chinese medicine.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

Objective: To identify the latent profile characteristics and associated factors of family intergenerational transmission of proactive health behaviors in overweight and obesity junior high school students, so as to provide a reference for promoting active health in adolescents. Methods: From October to December 2024, a stratified cluster random sampling method was employed to select 4 932 junior high school students with overweight and obesity from 18 junior high schools of three cities representing the eastern (Yantai), central (Zibo), and western (Zaozhuang) regions of Shandong Province. Latent profile analysis was used to explore the heterogeneity of family intergenerational transmission among those students. Univariate analysis and multiple Logistic regression analysis were conducted to identify factors associated with different profiles and to examine the influence of profile membership on junior high school students proactive health levels. Results: The intergenerational transmission of health behaviors in junior high school students could be classified into 3 latent profiles:the moderate modeling dysfunctional group (5.88%, n =290), the low modeling advantage deficient group (41.79%, n =2 061), and the high modeling functional harmonious group ( 52.33 % n =2 581). Univariate analysis showed that gender, urban/rural area, region location, paternal/maternal education level, family economic status, and only child status were significantly associated with the classification of latent profiles of intergenerational transmission in families of overweight and obesity junior high school students ( χ 2=7.63-165.22, all P <0.05). Multiple Logistic regression analysis revealed that, using the moderate modeling dysfunctional group as the reference, male students ( OR =0.71) and those residing in coastal cities ( OR =0.74) were less likely to belong to the low modeling advantage deficient group (both P <0.05); students with moderate family economic status were more likely to belong to the low modeling advantage deficient group ( OR =1.72， P <0.05). Conversely, students living in urban areas ( OR =1.43), those with fathers ( OR =2.53) or mothers ( OR =2.48) having a college degree or above, and those with moderate ( OR =3.89) or good ( OR =2.42) family economic status were more likely to be classified into the high modeling functional harmonious group (all P < 0.05 ). Using the low modeling advantage deficient group as the reference, residing in coastal cities ( OR =1.26), living in urban residence ( OR =1.14), having parents (father or mother) with a college education or above ( OR =1.91, 2.33), moderate or good family economic status ( OR = 2.27 , 3.17), and being an only child ( OR =1.19) were associated with a higher probability of belonging to the high modeling functional harmonious group (all P < 0.05 ). There was a statistically significant difference in active health behaviors among students across different latent profiles of family intergenerational transmission ( F =494.79, P <0.01). Conclusions The heterogeneity of family intergenerational transmission is associated the level of proactive health behaviors among junior high school students. Therefore, precision interventions should be implemented based on different categories of family intergenerational transmission, so as to strengthen the efficacy of intergenerational transmission of health behaviors and promote proactive health among junior high school students.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To evaluate the safety and efficacy of the probiotic Bifidobacterium longum subsp. longum BL21 (BL21) in preventing radiation-induced diarrhea (RID) in cervical cancer patients during radiotherapy (RT) and to investigate the intestinal microbiota in the patients. Methods:This study was a prospective, single-arm, phase Ⅱ clinical trial, involving cervical cancer patients treated with radical and adjuvant RT. From the first day of RT, participants took one pack of BL21 powder (containing 20 billion colony-forming unit(CFU) of Bifidobacterium longum subsp. longum BL21) orally every day until the end of RT. The occurrence of adverse events and RID during RT were assessed as per Common Terminology Criteria for Adverse Events ( CTCAE) v5.0. In this way, the safety and efficacy of BL21 in preventing RID were evaluated. Additionally, the intestinal microbiota in fecal samples collected from the patients before and after RT were analyzed using 16S rRNA sequencing. Results:A total of 35 cervical cancer patients were enrolled in this study, with 29 cases incorporated for the final analysis. No serious adverse event related to the administration of BL21 was observed. The patients exhibited slight RID, with the majority (22/29) developing no or grade 1 RID during RT. The microbiota in the fecal samples showed decreased alpha diversity after RT, as indicated by the Chao1 ( P = 0.002) and Shannon ( P = 0.005) indices. Furthermore, these samples exhibited a notably higher abundance of genus Clostridium (LDA score = 3.98). The fecal samples from patients with grade 1 RID or no RID post-RT exhibited higher alpha diversity than those from patients with grade 2 RID or above post-RT (Chao1: P = 0.07, Shannon: P = 0.28), as well as a high abundance of genera Gemmiger (LDA score = 4.48) and Dorea (LDA score = 3.83). Conclusions:The administration of BL21 to cervical cancer patients during RT is simple, convenient, safe, and effective in preventing RID, thus warranting further investigation.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective:To design a novel model for experiments on in vitro irradiation with radioactive seeds using a treatment planning system (TPS) and 3D printing technology and to preliminarily validate the design scientific rigor of the model via experiments on isodose brachytherapy (BT) and external beam radiotherapy (EBRT) on glioma cells in mice. Methods:The TPS was employed to design the model′s shape and calculate the number and positions of radioactive seeds, and 3D printing technology was utilized to fabricate the experimental model. The GL261 cell line was selected for in vitro irradiation experiments, with the mice divided into the control, EBRT, and BT groups. Mice in the EBRT and BT groups were treated with EBRT and BT, respectively, at doses of 2, 4, and 6 Gy. Then, changes in their cell viability, proliferation, and the level of intracellular reactive oxygen species (ROS) were assessed. Results:The model for in vitro irradiation with radioactive seeds was successfully designed and fabricated. The single photon emission computed tomography (SPECT) verified a uniform radioactive distribution within the model, with no significant cold spots. The BT and EBRT groups displayed decreased cell viability with an increase in the radiation dose. Compared to the EBRT group, the BT group exhibited significantly reduced cell viability (51.33% vs. 22.00%, t = 10.94, P < 0.05) and clone counts (172.67 ± 13.11 vs. 53.67 ± 10.22, t = 8.73, P < 0.05), but a significantly increased level of ROS (102.52 ± 6.87 vs. 144.81 ± 6.01, t = -5.26, P < 0.05) at a dose of 6 Gy. Conclusions:An effective model of in vitro irradiation with radioactive seeds is designed based on TPS and 3D printing technology. This provides an experimental model tool and target for research on the BT and EBRT mechanisms.

Objective:To employ single-photon emission computed tomography (SPECT)/CT for isodose assignment in 125I brachytherapy, assess the correlation between photon counts and dose values, and develop a clinical γ-ray visualization model for 125I brachytherapy. Methods:125I radioactive seeds were filled into a self-made 3D printed stereotactic template to build a stereotactic model. The model was scanned by SPECT/CT for photon counts at 0.5, 1.0, 1.5, 2.0 cm from the outermost peripheral seeds, and the corresponding dose values were measured using the Treatment Planning System (TPS). The fitting curve for the photon counts and the dose values was plotted using SPSS 27.0 software. Results:The photon counts of γ rays at distances of 0.5, 1.0, 1.5, and 2.0 cm from the peripheral particles were 7 603.57±1 806.35, 4 018.26±1 315.72, 2 074.04±791.53, and 1 080.34±424.79, respectively, showing a significant difference ( F=743.72, P<0.01). The dose values (in Gy) in the TPS at distances of 0.5, 1.0, 1.5, and 2.0 cm from the peripheral particles were 208.05±37.57, 125.43±17.74, 86.76±17.67, and 61.55±14.39, respectively, which were significantly different ( F=930.46, P<0.01). The photon counts were linearly correlated with the dose values ( y=0.02 x+ 46.45, R2=81.2%, P<0.01). Conclusions:SPECT/CT-based γ-ray photon count detection can be used to assign doses for 125I brachytherapy, enabling the visualization of γ rays in 125I brachytherapy. This approach has a distinct advantage over TPS, laying the foundation for the establishment of an alternative system to TPS.