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.

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.

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.

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 investigate the regulatory effect of autophagy on the resistance of human liver cancer cell Huh7 to lenvatinib. METHODS Using human liver cancer cell Huh7 as subject， the lenvatinib-resist cell model （Huh7-LR） was generated by the low-dose gradient method combined with long-term administration. The sensitivity of parental cell Huh7 and drug-resistant cell Huh7-LR to lenvatinib was detected by using CCK-8 assay and flow cytometry. Western blot assay and GFP-mCherry-LC3 plasmid transfection were performed to detect the expression levels of autophagic protein Beclin-1， autophagic adapter protein sequestosome 1 （p62）， microtubule-associated protein 1 light chain 3 （LC3） and autophagic level. Furthermore， an autophagy activation model was constructed by cell starvation， the protein expression of p62 and autophagy level were detected by using Western blot assay and GFP-mCherry-LC3 plasmid transfection， and the effect of autophagy activation on the sensitivity of Huh7-LR cells to lenvatinib was detected by flow cytometry. RESULTS Compared with parental cells， the drug resistance index of Huh7-LR cells was 6.2； protein expression of p62 was increased significantly， while apoptotic rate， protein expression of Beclin-1 and LC3Ⅱ/ LC3Ⅰ ratio were all reduced significantly （P＜0.05 or P＜0.01）； the level of autophagy was decreased to some extent. Autophagy activation could significantly increase the protein expression of p62 in Huh7-LR cells （P＜0.05） and autophagy level， and significantly increase its apoptotic rate （P＜0.05）. CONCLUSIONS Autophagy is involved in lenvatinib resistance， and activating autophagy can reverse the resistance of liver cancer cells to lenvatinib to some extent.

ObjectiveGastric cancer (GC) seriously affects human health and life, and research has shown that it is closely related to the serine/glycine metabolism. The proliferation ability of tumor cells is greatly influenced by the metabolism of serine and glycine. The aim of this study was to investigate the molecular mechanism of serine/glycine metabolism can affect the proliferation of gastric cancer cells. MethodsIn this work, a stable metabolic dynamic model of gastric cancer cells was established via a large-scale metabolic network dynamic modeling method in terms of a potential landscape description of stochastic and non-gradient systems. Based on the regulation of the model, a quantitative analysis was conducted to investigate the dynamic mechanism of serine/glycine metabolism affecting the proliferation of gastric cancer cells. We introduced random noise to the kinetic equations of the general metabolic network, and applied stochastic kinetic decomposition to obtain the Lyapunov function of the metabolic network parameter space. A stable metabolic network was achieved by further reducing the change in the Lyapunov function tied to the stochastic fluctuations. ResultsDespite the unavailability of a large number of dynamic parameters, we were able to successfully construct a dynamic model for the metabolic network in gastric cancer cells. When extracellular serine is available, the model preferentially consumes serine. In addition, when the conversion rate of glycine to serine increases, the model significantly upregulates the steady-state fluxes of S-adenosylmethionine (SAM) and S-adenosyl homocysteine (SAH). ConclusionIn this paper, we provide evidence supporting the preferential uptake of serine by gastric cancer cells and the important role of serine/glycine conversion rate in SAM generation, which may affect the proliferation ability of gastric cancer cells by regulating the cellular methylation process. This provides a new idea and direction for targeted cancer therapy based on serine/glycine metabolism.

Objective To explore characteristics of clinical parameters and cytokines in patients with drug-induced liver injury(DILI)caused by different drugs and their correlation with clinical indicators. Method The study was conducted on patients who were up to Review of Uncertainties in Confidence Assessment for Medical Tests(RUCAM)scoring criteria and clinically diagnosed with DILI.Based on Chinese herbal medicine,cardiovascular drugs,non-steroidal anti-inflammatory drugs(NSAIDs),anti-infective drugs,and other drugs,patients were divided into five groups.Cytokines were measured by Luminex technology.Baseline characteristics of clinical biochemical indicators and cytokines in DILI patients and their correlation were analyzed. Results 73 patients were enrolled.Age among five groups was statistically different(P=0.032).Alanine aminotransferase(ALT)(P=0.033)and aspartate aminotransferase(AST)(P=0.007)in NSAIDs group were higher than those in chinese herbal medicine group.Interleukin-6(IL-6)and tumor necrosis factor alpha(TNF-α)in patients with Chinese herbal medicine(IL-6:P<0.001;TNF-α:P<0.001)and cardiovascular medicine(IL-6:P=0.020;TNF-α:P=0.001)were lower than those in NSAIDs group.There was a positive correlation between ALT(r=0.697,P=0.025),AST(r=0.721,P=0.019),and IL-6 in NSAIDs group. Conclusion Older age may be more prone to DILI.Patients with NSAIDs have more severe liver damage in early stages of DILI,TNF-α and IL-6 may partake the inflammatory process of DILI.

Small for gestational age (SGA) infants are more likely to experience neurocognitive impairments compared to appropriate for gestational age (AGA) infants. This paper reviews recent research on the neurocognitive development of SGA children. SGA can lead to a "brain-sparing effect" due to growth restriction, which may affect cerebral blood flow and brain structure. However, this does not guarantee normal brain development. Restrictive blood flow can result in changes in brain structure, such as reduced total white matter and gray matter volume in various brain regions, including the cerebral cortex, hippocampus and cerebellum, ultimately leading to decreased head circumference. SGA children also exhibit lower scores in all neurocognitive domains, including intelligence, attention, memory, and executive function. This may result in poor academic performance and an increased risk of social, behavioral, and neurological problems, such as cerebral palsy, epilepsy, visual and hearing impairments, as well as comorbidities like attention deficit hyperactivity disorder(ADHD), autism spectrum disorder(ASD), anxiety, depression, and schizophrenia. Several risk factors for SGA-related neurocognitive impairments have been identified, including gestational hypertension, abnormal gestational weight, smoking, and catch-up growth. Studies have shown that the best interventions to improve cognitive dysplasia include nutrient supplementation, continued breastfeeding, high-quality education, and appropriate early intervention (responsive parenting) are effective in improving cognitive outcomes for SGA children.