With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

AIM: To investigate the expression of serum growth differentiation factor 11(GDF11)and thrombospondin 1(TSP1)in patients with diabetic retinopathy(DR), and discuss their relationship with microvascular injury.METHODS: Totally 102 DR patients were served as DR group and assigned into non proliferative DR group(NPDR group)and proliferative DR group(PDR group)based on the severity of DR lesions. Meantime, 100 patients with simple diabetes were served as control group. Serum indicators of microvascular injury including vascular endothelial growth factor(VEGF), endothelial cells(ECs), endothelial progenitor cells(EPCs), and levels of GDF11 and TSP1 were measured in each group. Pearson method was used to discuss the correlation between GDF11, TSP1 and microvascular injury indicators. Logistic regression was used to discuss the factors that affected the occurrence of DR. Receiver operating characteristic(ROC)curve was applied to analyze the evaluation value of serum GDF11 and TSP1 for the DR conditions.RESULTS: For the control group, DR group had lower EPCs and GDF11, and higher VEGF, ECs, and TSP1 levels(all P<0.05). The PDR group had lower GDF11 and higher TSP1 than the NPDR group(all P<0.05). Serum GDF11 was negatively related to VEGF and ECs(r=-0.486, -0.511, all P<0.001), and positively related to EPCs(r=0.475, P<0.001). TSP1 was positively related to VEGF and ECs(r=0.579, 0.594, all P<0.001), and negatively related to EPCs(r=-0.505, P<0.001). Moreover, GDF11 and TSP1 were negatively correlated(r=-0.443, P<0.001). The course of T2DM, VEGF, and TSP1 were risk factors for DR, while GDF11 was a protective factor(all P<0.05). The AUC of GDF11, TSP1, and combined diagnosis for PDR conditions was 0.819, 0.822, and 0.915, respectively. The combined diagnosis was better than single diagnosis(Zcombination-GDF11=2.070, P=0.039, Zcombination-TSP1=2.274, P=0.023).CONCLUSION: GDF11 and TSP1 are closely associated with microvascular injury in DR patients and are related to the progression of DR disease, and the combined detection of their serum levels is of clinical value in the assessment of DR disease.

ObjectiveTo monitor the real-time changes in ammonia concentration in the laboratory animal facility environment before, during, and after the air conditioning system stops supplying air, so as to provide a basis and reference for developing emergency plans for the shutdown of the air conditioning system. MethodsThe laboratory animal facilities of the Wuhan Institute of Biological Products were used as the research object. Ammonia concentration detectors were used to monitor ammonia concentration continuously in the environment of conventional rabbit production facility, SPF hamster production facility, and SPF guinea pig experimental facility before and after the passive shutdown due to repairs and active maintenance shutdown of the air conditioning system, as well as the time for the ammonia concentration to return to daily levels after resuming air supply. ResultsUnder both shutdown modes of the air conditioning system, the trend of ammonia concentration changes in different laboratory animal facilities was consistent, showing a rapid increase after shutdown and a rapid decrease after resuming air supply. Under active maintenance shutdown, the maximum ammonia concentrations in the conventional rabbit production facilities, SPF hamster production facilities, and SPF guinea pig experimental facilities were 9.81 mg/m³, 14.27 mg/m³, and 6.98 mg/m³, respectively. Within 12 minutes after resuming air supply, ammonia concentration could return to normal daily levels. Under passive long-term shutdown, ammonia concentration value was positively correlated with the duration of air supply suspension. As the shutdown duration increased, ammonia concentration continued to increase. The maximum ammonia concentration values in the three facilities occurred at 88 minutes (38.06 mg/m³), 40 minutes (18.43 mg/m³), and 34 minutes (15.61 mg/m³) after air supply suspension, respectively.Within 11 minutes after resuming air supply, ammonia concentration could return to normal daily levels. ConclusionShutdown of the air conditioning system causes a rapid increase in ammonia concentration in laboratory animal facilities, and the rise in ammonia concentration is positively correlated with the duration of air supply suspension. Therefore, when an emergency shutdown of the air-conditioning system is required due to maintenance or other reasons, backup fans should be provided in accordance with the requirements of GB 50447-2008 "Architectural and Technical Code for Laboratory Animal Facilities". Older facilities should make adequate preparations and develop a scientifically sound emergency plan.

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.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

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.

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.