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 quantify the intraretinal fluid(IRF)volume in patients with diabetic macular edema(DME)using a deep learning-based three-dimensional segmentation model and to investigate the relationship of IRF volume with visual function and retinal biomarkers based on optical coherence tomography(OCT).Methods A total of 37 pa-tients(42 eyes)with DME who received treatment in the Ophthalmology Department of Sichuan Provincial People's Hospi-tal from July 2022 to September 2024 were prospectively included in this study.A three-dimensional segmentation model was used to automatically quantify the IRF volume within a 6 mm × 6 mm OCT scan at baseline and at the last follow-up.The correlation of visual acuity with IRF volume,central subfield thickness(CST),disorganization of the retinal inner lay-ers(DRIL),hyperreflective foci(HRF),external limiting membrane(ELM),ellipsoid zone(EZ),and vitreomacular in-terface abnormality(VMIA)was analyzed at baseline and the last follow-up.Additionally,the correlation of IRF volume with CST,DRIL,HRF,ELM,EZ,and VMIA was analyzed at both time points.Furthermore,the correlation of the visual acuity at the last follow-up with the IRF volume,CST,DRIL,HRF,ELM,EZ,and VMIA at baseline was analyzed.Results Compared with the baseline value,patients exhibited a significant increase in the best corrected visual acuity(BCVA)(logMAR),a significant decrease in the IRF volume and CST,a significant reduction in the HRF,a significant re-covery in the DRIL,ELM,and EZ(all P＜0.05),and no significant changes in the VMIA(P=1.000)at the last follow-up.At baseline,the BCVA was negatively correlated with the IRF volume,CST,DRIL,and HRF,but positively correlated with the ELM and EZ(all P＜0.05);there was no correlation between the BCVA and VMIA(P=0.069).At the last follow-up,the BCVA was negatively correlated with the DRIL,HRF,and VMIA,but positively correlated with the ELM and EZ(all P＜0.01);the BCVA did not correlate with the IRF volume and CST(P=0.419 and 0.994).At baseline,the IRF volume was positively correlated with the CST(P＜0.001)but negatively correlated with the ELM and EZ(P＜0.01);the IRF vol-ume did not correlate with the DRIL,HRF,and VMIA(all P＞0.05).At the last follow-up,the IRF volume was positively correlated with the CST and HRF(all P＜0.01);however,it was not correlated with the DRIL,ELM,EZ,and VMIA(all P＞0.05).The BCVA at the last follow-up was positively correlated with the BCVA,ELM,and EZ at baseline,but negative-ly correlated with the IRF volume,CST,DRIL,HRF,and VMIA at baseline(all P＜0.05).Conclusion The baseline IRF volume in DME patients is an important influencing factor of visual acuity at baseline and the last follow-up.Hence,the IRF volume may serve as a potential biomarker in the management of DME.

This study was aimed at establishing a sensitive and specific sandwich ELISA detection method for Brucella.We screened monoclonal capture antibodies and detection antibodies for Brucella detection,and optimized and determined the opti-mal antibody coating time and concentration,as well as the optimal blocking solution,blocking time,and yin-yang critical val-ue.The specificity of this method was verified by examination of other bacteria prone to cross-reacting with Brucella.The sen-sitivity of the method was verified by detection of a gradient dilution of inactivated Brucella.Moreover,the sandwich ELISA detection results were compared with test tube agglutination and qPCR results.The selected capture antibody was 4A12,and the selected detection antibody was 6C12.Experimental analysis indicated that the optimal coating concentration for the 4A12 capture antibody was 5 μg/mL,and the optimal dilution ratio for the 6C12 detection antibody was 1∶2000.The optimal coating conditions were overnight at 4℃,and blocking with 5％skim milk powder for 2 hours.The established double antibody sand-wich ELISA method reacted with only Brucella but not other bacteria,thus demonstrating the method's good specificity.Inac-tivated Brucella solution was still detectable after dilution to 1 × 105 CFU/mL,thus demonstrating the method's good sensitiv-ity.The intra-and inter batch coefficients of variation were both below 10％,thus indicating the method's good repeatability.Thus,this study successfully established a dual antibody sandwich ELISA method for Brucella detection,which has good spe-cificity and sensitivity,and might provide an effective approach for the precise diagnosis and effective prevention and control of brucellosis.

Objective To compare the differences between swept-source optical coherence tomography angiography(SS-OCTA)and ultra-wide-field fluorescein angiography(UWFA)in detecting non-perfusion areas(NPs)in patients with diabetic retinopathy(DR),to evaluate the accuracy of SS-OCTA in predicting NPs outside its visible range,and to explore the distribution patterns of NPs.Methods A retrospective analysis was made on 69 DR patients(88 eyes)who under-went both UWFA and SS-OCTA examinations at the Ophthalmology Department of Sichuan Provincial People's Hospital from December 2022 to September 2024.Manual NP labeling was conducted to compare the detection rate of NPs between the two imaging techniques.The distribution patterns of NPs and the accuracy of SS-OCTA for predicting NPs outside its visible range were also analyzed.Results In a scanning area of 20 mm x 24 mm,the overall NP detection rate by SS-OCTA was 47.40％,with UWFA taken as the standard.The NP detection rate by SS-OCTA was 51.56％in the superotemporal quad-rant,58.35％in the inferotemporal quadrant,45.50％in the superonasal quadrant,and 43.17％in the inferonasal quad-rant.Most NPs occurred in the inferonasal quadrant,accounting for 41.71％of the total NP.The accuracy of SS-OCTA in predicting NPs was 75.00％in the superonasal quadrant and 78.41％in the inferonasal quadrant.The ischemic indices(ISI)of the two imaging techniques were highly positively correlated(r2=0.74).Conclusion Although SS-OCTA can-not yet fully replace UWFA for NP detection in DR patients due to a small visible range,it is still an effective tool to assess retinal ischemia.SS-OCTA has the ability to predict NPs outside its visible range in its scanning range.The inferonasal quadrant is the region where NPs occur most frequently in DR patients,so it is suggested that special attention should be paid to this region in early diagnosis and follow-up periods.

Objective To quantify the intraretinal fluid(IRF)volume in patients with diabetic macular edema(DME)using a deep learning-based three-dimensional segmentation model and to investigate the relationship of IRF volume with visual function and retinal biomarkers based on optical coherence tomography(OCT).Methods A total of 37 pa-tients(42 eyes)with DME who received treatment in the Ophthalmology Department of Sichuan Provincial People's Hospi-tal from July 2022 to September 2024 were prospectively included in this study.A three-dimensional segmentation model was used to automatically quantify the IRF volume within a 6 mm × 6 mm OCT scan at baseline and at the last follow-up.The correlation of visual acuity with IRF volume,central subfield thickness(CST),disorganization of the retinal inner lay-ers(DRIL),hyperreflective foci(HRF),external limiting membrane(ELM),ellipsoid zone(EZ),and vitreomacular in-terface abnormality(VMIA)was analyzed at baseline and the last follow-up.Additionally,the correlation of IRF volume with CST,DRIL,HRF,ELM,EZ,and VMIA was analyzed at both time points.Furthermore,the correlation of the visual acuity at the last follow-up with the IRF volume,CST,DRIL,HRF,ELM,EZ,and VMIA at baseline was analyzed.Results Compared with the baseline value,patients exhibited a significant increase in the best corrected visual acuity(BCVA)(logMAR),a significant decrease in the IRF volume and CST,a significant reduction in the HRF,a significant re-covery in the DRIL,ELM,and EZ(all P＜0.05),and no significant changes in the VMIA(P=1.000)at the last follow-up.At baseline,the BCVA was negatively correlated with the IRF volume,CST,DRIL,and HRF,but positively correlated with the ELM and EZ(all P＜0.05);there was no correlation between the BCVA and VMIA(P=0.069).At the last follow-up,the BCVA was negatively correlated with the DRIL,HRF,and VMIA,but positively correlated with the ELM and EZ(all P＜0.01);the BCVA did not correlate with the IRF volume and CST(P=0.419 and 0.994).At baseline,the IRF volume was positively correlated with the CST(P＜0.001)but negatively correlated with the ELM and EZ(P＜0.01);the IRF vol-ume did not correlate with the DRIL,HRF,and VMIA(all P＞0.05).At the last follow-up,the IRF volume was positively correlated with the CST and HRF(all P＜0.01);however,it was not correlated with the DRIL,ELM,EZ,and VMIA(all P＞0.05).The BCVA at the last follow-up was positively correlated with the BCVA,ELM,and EZ at baseline,but negative-ly correlated with the IRF volume,CST,DRIL,HRF,and VMIA at baseline(all P＜0.05).Conclusion The baseline IRF volume in DME patients is an important influencing factor of visual acuity at baseline and the last follow-up.Hence,the IRF volume may serve as a potential biomarker in the management of DME.

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.

Aim To explore the therapeutic effect and mechanism of Qingjie Huagong decoction in modulating PI3K/AKT/NF-κB signaling pathway in inflammatory response of acute pancreatitis(AP)mice.Methods Twenty-four mice were randomly divided into Blank group,Model group,Ustekin group,and Qingjie Hua-gong decoction group,with six mice in each group.The AP model was prepared by using rain frogin.Serum α-AMS,PNLP,IL-1β,IL-6,IL-8,IL-18,and TNF-α lev-els were detected by ELISA;the pancreatic pathology was detected by HE staining;the expressions of PI3K,AKT,and NF-κB-related proteins and mRNAs were de-tected by immunohistochemistry,Western blot,and RT-qPCR.Results Compared with the blank group,the model group showed obvious pathological damage to the pancreas,with significantly higher serum α-AMS,PN-LP,IL-1β,IL-6,IL-8,IL-18,and TNF-α levels(P＜0.01),and significantly higher levels of PI3K,AKT,and NF-κB-related proteins and mRNA expression(P＜0.01).Compared with the model group,both the Qingjie Huagong decoction group and the ustekin group improved the histopathological changes in the pancreas of AP mice,decreased the serum α-AMS,PNLP,IL-1β,IL-6,IL-8,IL-18,and TNF-α levels,and down-reg-ulated the expression levels of pancreatic PI3K,AKT,NF-κB-related proteins and mRNA(P＜0.05 or P＜0.01).Conclusion Qingjie Huagong decoction may inhibit the inflammatory response and protect pancreat-ic tissues by regulating the expression of PI3K/AKT/NF-κB signaling pathway.

This study was aimed at establishing a sensitive and specific sandwich ELISA detection method for Brucella.We screened monoclonal capture antibodies and detection antibodies for Brucella detection,and optimized and determined the opti-mal antibody coating time and concentration,as well as the optimal blocking solution,blocking time,and yin-yang critical val-ue.The specificity of this method was verified by examination of other bacteria prone to cross-reacting with Brucella.The sen-sitivity of the method was verified by detection of a gradient dilution of inactivated Brucella.Moreover,the sandwich ELISA detection results were compared with test tube agglutination and qPCR results.The selected capture antibody was 4A12,and the selected detection antibody was 6C12.Experimental analysis indicated that the optimal coating concentration for the 4A12 capture antibody was 5 μg/mL,and the optimal dilution ratio for the 6C12 detection antibody was 1∶2000.The optimal coating conditions were overnight at 4℃,and blocking with 5％skim milk powder for 2 hours.The established double antibody sand-wich ELISA method reacted with only Brucella but not other bacteria,thus demonstrating the method's good specificity.Inac-tivated Brucella solution was still detectable after dilution to 1 × 105 CFU/mL,thus demonstrating the method's good sensitiv-ity.The intra-and inter batch coefficients of variation were both below 10％,thus indicating the method's good repeatability.Thus,this study successfully established a dual antibody sandwich ELISA method for Brucella detection,which has good spe-cificity and sensitivity,and might provide an effective approach for the precise diagnosis and effective prevention and control of brucellosis.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective To compare the differences between swept-source optical coherence tomography angiography(SS-OCTA)and ultra-wide-field fluorescein angiography(UWFA)in detecting non-perfusion areas(NPs)in patients with diabetic retinopathy(DR),to evaluate the accuracy of SS-OCTA in predicting NPs outside its visible range,and to explore the distribution patterns of NPs.Methods A retrospective analysis was made on 69 DR patients(88 eyes)who under-went both UWFA and SS-OCTA examinations at the Ophthalmology Department of Sichuan Provincial People's Hospital from December 2022 to September 2024.Manual NP labeling was conducted to compare the detection rate of NPs between the two imaging techniques.The distribution patterns of NPs and the accuracy of SS-OCTA for predicting NPs outside its visible range were also analyzed.Results In a scanning area of 20 mm x 24 mm,the overall NP detection rate by SS-OCTA was 47.40％,with UWFA taken as the standard.The NP detection rate by SS-OCTA was 51.56％in the superotemporal quad-rant,58.35％in the inferotemporal quadrant,45.50％in the superonasal quadrant,and 43.17％in the inferonasal quad-rant.Most NPs occurred in the inferonasal quadrant,accounting for 41.71％of the total NP.The accuracy of SS-OCTA in predicting NPs was 75.00％in the superonasal quadrant and 78.41％in the inferonasal quadrant.The ischemic indices(ISI)of the two imaging techniques were highly positively correlated(r2=0.74).Conclusion Although SS-OCTA can-not yet fully replace UWFA for NP detection in DR patients due to a small visible range,it is still an effective tool to assess retinal ischemia.SS-OCTA has the ability to predict NPs outside its visible range in its scanning range.The inferonasal quadrant is the region where NPs occur most frequently in DR patients,so it is suggested that special attention should be paid to this region in early diagnosis and follow-up periods.