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.

Objective To understand the distribution, species composition, seasonal fluctuations, and presence of Dabie bandavirus in ticks in Tai'an City, providing a basis for assessing tick-borne disease risks and formulating prevention strategies. Methods Free-living ticks were monitored using the flagging method, whereas parasitic ticks were monitored through host surface examination. Reverse transcription-polymerase chain reaction (RT-qPCR) was used to detect the presence of Bandavirus dabieense in tick specimens. Results The density of free-living ticks in Tai'an City showed a continuous decreasing trend over the 3 years from 2021 to 2023, with an average tick density of 0.43 ticks/(flag·100 m). The dominant habitats were scenic areas and forests, and the seasonal fluctuations exhibited a bimodal pattern, peaking in March and August. The only free-living tick species captured was Haemaphysalis longicornis. Over the 3 years, the average tick index for parasitic ticks was 1.85, with sheep as the primary host. Seasonal fluctuations also followed a bimodal pattern, peaking in March and August. The primary parasitic tick species captured were H. longicornis and Rhipicephalus sanguineus. No Dabie bandavirus was detected in the tick specimens. Conclusions Tick density in Tai'an City remains relatively high, necessitating continued efforts to enhance tick surveillance and implement targeted prevention measures in rural environments to further reduce the health risks posed by tick-borne diseases.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

This study aims to analyze the screening results and epidemiological characteristics of prostate cancer (PCa) among elderly males in the rural areas of Songjiang, Shanghai City, through the implementation of a preliminary prostate-specific antigen (PSA) screening based on a community-linkage model, and to explore an effective screening approach. A retrospective observational study design was employed to collect data from residents who underwent PSA screening at Songjiang Hospital affiliated to Shanghai Jiao Tong University School of Medicine, in collaboration with multiple community health service centers in Songjiang District, Shanghai City, between June 2022 and June 2024, through free clinics and annual health examinations. Prostate biopsy was recommended for individuals with total PSA (tPSA) levels >10 ng/ml and those with 4 ng/ml≤tPSA≤10 ng/ml and abnormal free-to-total PSA (f/tPSA) ratios. Clinical characteristics of detected PCa patients were analyzed. Follow-up was conducted through phone calls and home visits by family doctors, coupled with enhanced health education. The results indicated that a total of 17 198 residents participated in the screening, among which 2 234 (12.99%) had tPSA levels between 4 ng/ml and 10 ng/ml, and 257 (1.49%) had tPSA levels >10 ng/ml. Ultimately, 417 residents underwent prostate biopsy, with 171 being diagnosed with PCa, yielding a positive biopsy rate of 41.00% and a PCa detection rate of 0.99%. The predominant pathological subtype among PCa patients was adenocarcinoma (168 cases, 98.24%). Of the 146 PCa patients who received treatment, the majority were classified as intermediate or high-risk (124 cases, 84.93%). Furthermore, with the optimization of the screening model, there was a significant increase in the proportion of subsequent outpatient visits. In conclusion, the community-linkage-based PSA screening model demonstrated high effectiveness in screening for PCa among elderly males in the rural areas of Songjiang, Shanghai City. Epidemiological findings revealed that PCa patients in this region are primarily composed of intermediate and high-risk groups, highlighting the need for intensified early screening and health education.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

This research aims to construct a stable epithelial cell senescence model for screening and e-valuation of senolytics.We explored the optimal conditions for doxorubicin-induced senescence of non-transformed epithelial cells MCF 10A,including the optimal induction concentration,the optimal inter-vention time,and the optimal senescence duration,and confirmed the feasibility of MCF 10A as an epi-thelial senescence model by multiple ways.The optimal condition for Doxorubicin-induced senescence of MCF 10A cells was treatment with 0.6 μmol/L Doxorubicin for 16 h to achieve the best senescence state on the 8th day.Under the optimal induction conditions,the positive rate of senescence-associated β-gal-actosidase(SA-β-gal)staining in the treated group reached 97％.At the same time,biochemical results of detecting the expression of mRNA,proteins,and immunofluorescence demonstrated that the expression levels of senescence-associated secretory phenotype(SASP),p16,p21 and p53 in the treated group were significantly higher than those in the control cells,and Lamin B1 was significantly decreased(P＜0.001),which were consistent with the specific characteristics of senescence.In summary,an epithelial senescence model was successfully induced in MCF 10A cells by Doxorubicin in this study,which will promote the screening of senolytics for senescent epithelial cells.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective:To investigate the effects of combination therapy with aqueous extract of corn silk(CS)and training on exercise capacity and glycolipid metabolism in mice with metabolic syndrome(MS).Methods:In this study,db/db mice were used as the animal model of MS.The mice were administered aqueous extract of CS via gavage and subjected to different intensities of training for 12 weeks(3 months).The specific experimental design was as follows:24 db/db mice were randomly divided into four groups on average:negative control group(NC),aqueous extract of CS group(CS),aqueous extract of CS+moderate-intensity training group(CS+MT),and CS aqueous extract of CS+high-intensity training group(CS+HT).The maximum running speed,forelimb grip strength,body weight and fasting blood glucose of mice were measured before and after treatment.After the intervention,oral glucose tolerance test(OGTT)and insulin tolerance test(ITT)were conducted to assess glucose metabolism,while serum triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)levels were measured to evaluate lipid metabolism.Results:After 3 months of intervention,there were significant differences in the maximum running speed and forelimb grip strength among the four groups(P＜0.05).The maximum running speed and forelimb grip strength of CS group,CS+MT group and CS+HT group were higher than those of NC group(P＜0.05).The CS+MT group exhibited higher forelimb grip strength,and the CS+HT group showed higher maximum running speed and forelimb grip strength compared to the CS group(P＜0.05),while no significant difference was found between the CS+MT and CS+HT groups(P＞0.05).Significant differences in body weight were observed among the four groups after 3 months of intervention(P＜0.05).Specifically,the CS+MT and CS+HT groups exhibited significantly lower body weight compared to both the NC and CS groups(P＜0.05),with the CS+MT group having the lowest body weight(P＜0.05).Fasting blood glucose levels also differed significantly among the groups after 2 and 3 months of intervention(P＜0.05).The CS,CS+MT,and CS+HT groups had lower fasting blood glucose levels compared to the NC group(P＜0.05),with the CS+MT and CS+HT groups showing the lowest levels(P＜0.05).No significant difference was found between the CS+MT and CS+HT groups(P＞0.05).After 3 months of intervention,significant differences in the area under the curve(AUC)of OGTT and ITT were observed among the four groups(P＜0.05).The AUC of OGTT and ITT were significantly lower in the CS,CS+MT,and CS+HT groups compared to the NC group(P＜0.05).The CS+MT and CS+HT groups exhibited the lowest AUC values for both OGTT and ITT(P＜0.05),with the CS+MT group showing the lowest AUC for OGTT(P＜0.05).Significant differences in serum lipid levels were observed among the four groups after 3 months of intervention(P＜0.05).TG,TC,and LDL-C levels were significantly lower,while HDL-C levels were higher in the CS,CS+MT,and CS+HT groups compared to the NC group(P＜0.05).The CS+MT group had the lowest TG levels and the highest HDL-C levels compared to the CS+HT group(P＜0.05),with no significant differences in TC and LDL-C levels between these two groups(P＞0.05).Conclusion:Aqueous extract of CS combined with different intensity training can significantly improve the exercise capacity and glycolipid metabolism of MS mice and reduce body weight,especially CS combined with MT treatment is more effective in improving lipid metabolism.In addition,when combined with HT,aqueous extract of CS can also play an auxiliary role in reducing the side effects of high-intensity exercise and improving the therapeutic effect.

Objective:Systematically review the release status and characteristics of policies related to cross-regional medical insurance settlement policies in China,providing reference for promoting the convenience of medical insurance settlement services and improving management efficiency.Methods:Using content analysis,a two-dimensional framework of"policy objectives-policy tools"was constructed.A total of 127 policy documents were coded and analyzed.Results:Demand-based tools were applied most frequently(43.6%),followed by environmental and supply-based tools.Policy objectives mainly focused on improving service convenience,supported by expanding coverage and ensuring regulatory security.Conclusions:China's cross-regional medical insurance settlement policies have improved service accessibility and strengthened institutional supervision while gradually expanding coverage.However,deficiencies remain in assessment refinement and emergency mechanisms.Greater interdepartmental coordination is needed to enhance regulatory linkage and ensure the safe operation of medical insurance funds.