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.

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 explore the optimal warning threshold of motor evoked potentials(MEP)in decompression surgery for ossification of the posterior longitudinal ligament(OPLL)at cervical and thoracic segments,and the predictive role of different MEP parameters on postoperative lower extremity motor function.Methods A retrospective analysis was conducted on the clinical data of 227 patients diagnosed with cervical or thoracic OPLL and underwent decompression surgery from January 2022 to January 2024 in the hospital.There were 131 males and 96 females,with an average age of(60±10)years.All patients underwent continuous neuro-physiological monitoring during the operation,and the minimum ratio of MEP amplitude change to the baseline at the beginning of the operation(Dmax)and the ratio of MEP terminal amplitude change to the baseline at the end of the operation(Dend)were recorded.The correlations between these two ratios and the lower extremity motor func-tion immediately after the operation and at 1 year were compared.According to the Medical Research Council muscle strength score(MRC)standard,a postoperative score increase of≥1 point compared to preoperative was defined as postoperative motor dysfunction.Pearson correlation coefficients were used to evaluate the correlations between Dmax and Dend and the lower extremity motor function immediately after the operation and at 1 year.Receiver operating characteristic(ROC)curves were drawn to predict postoperative lower extremity motor dysfunc-tion using Dmax and Dend.Results Among the 227 patients,186 had cervical OPLL and 41 had thoracic OPLL.The incidence of lower extremity motor dysfunction immediately after the operation and at 1 year was 7 cases(3.76%)and 2 cases(1.08%)in the cervical group,and 9 cases(21.95%)and 3 cases(7.32%)in the thoracic group,respectively.The incidence of lower extremity motor dysfunction in the thoracic group was higher than that in the cervical group(P<0.001).The baseline induction rate of bilateral lower extremity MEPs was 98.92%(368/372)in the cervical group and 96.34%(79/82)in the thoracic group.The Pearson correlation coefficients of Dend with the bilateral lower extremity motor function immediately after the operation in the cervical and thoracic groups were both greater than those of Dmax,and the differences were statistically significant(cervical group:r=0.669,0.517,P=0.001 2;thoracic group:r=0.882,0.727,P=0.003 6),while the differences in the Pearson corre-lation coefficients of Dend and Dmax with the bilateral lower extremity motor function at 1 year were not statistically significant(cervical group:r=0.457,0.352,P=0.088;thoracic group:r=0.760,0.625,P=0.098).The cut-off values of Dend for the cervical group were 0.853 immediately after the operation and at 1 year,and the cut-off values of Dmax were 0.881 and 0.978,respectively.For the thoracic group,the cut-off values of Dend were 0.532 immediately after the operation and 0.639 at 1 year,and the cut-off values of Dmax were 0.532 and 0.640,respec-tively.Conclusions In OPLL surgery,the MEP monitoring strategy should be adjusted according to the surgical segment.For the cervical segment,Dmax should be emphasized to balance high sensitivity and specificity,while for the thoracic segment,Dmax or Dend can be flexibly selected.Higher MEP warning thresholds are required for cervical OPLL surgery(Dmax:0.881 immediately after the operation and 0.978 at 1 year;Dend:0.853),while significantly lower thresholds are needed for thoracic OPLL(Dmax/Dend:0.532 immediately after the operation and 0.640 at 1 year).

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

AIM To investigate the mechanism of Jiedu Yizhi Formula on cognitive function and neuroinflammation in a mouse model of Alzheimer's disease(AD).METHODS 50 APP/PS1 double transgenic mice were randomly divided into the model group,the donepezil group,and the low-dose,moderate-dose,and high-dose Jiedu Yizhi Formula group(1.78,3.56 and 7.12 g/kg),with 10 mice in each group,in contrast to the 10 WT mice of the blank group.Following anesthesia administration and 8-week oral gavage regimen with respective drugs,all mice underwent final tissue sample collection.The mice had their learning and memory ability assessed by Morris water maze and nesting behavior scores;their pathology of brain tissue and Aβ expression observed using HE,Nissl and IHC staining;their polarization of microglia and the expression of inflammatory factors in hippocampal tissue detected by IF and ELISA;their hippocampal expression of PI3K/Akt/mTOR signaling pathway detected by RT-qPCR and Western blot.RESULTS Compared with the blank group,the model group had lower scores in total swimming distance,frequency in crossing the platform,residence time in the target quadrant,and nesting behavior scores(P＜0.05,P＜0.01);prolonged evasion latency(P＜0.01);more disorganized arrangement of pyramidal cells,solidification and deep staining,unclear demarcation,irregular cell shapes,reduction of Nyctinidia,and increased Aβ deposition in the brain tissue(P＜0.01);elevated expression of hippocampal microglia M1-type markers CD16/32 and lba-1(P＜0.01);decreased levels of M2-type marker CD206(P＜0.05);elevated levels of TNF-α and IL-1β(P＜0.01);decreased expressions of IL-13 and IL-4(P＜0.01);and decreased levels of PI3K,Akt and mTOR mRNA,and reduced p-PI3K,p-Akt and p-mTOR protein expressions(P＜0.01).Compared to the model group,the donepezil group and the Jiedu Yizhi Formula groups showed statistically significant improvements in the aforementioned indexes(P＜0.05,P＜0.01),with the magnitude of improvement being higher in the high-dose Jiedu Yizhi Formula group.CONCLUSION Jiedu Yizhi Formula suppresses microglia Ml-type polarization while enhancing M2-type polarization via activation the PI3K/Akt/mTOR signaling pathway,which subsequently reduces inflammatory cytokine secretion.This mechanism attenuates Aβ deposition in brain tissues and ameliorates cognitive dysfunction in AD mouse models.

AIM To investigate the mechanism of Jiedu Yizhi Formula on cognitive function and neuroinflammation in a mouse model of Alzheimer's disease(AD).METHODS 50 APP/PS1 double transgenic mice were randomly divided into the model group,the donepezil group,and the low-dose,moderate-dose,and high-dose Jiedu Yizhi Formula group(1.78,3.56 and 7.12 g/kg),with 10 mice in each group,in contrast to the 10 WT mice of the blank group.Following anesthesia administration and 8-week oral gavage regimen with respective drugs,all mice underwent final tissue sample collection.The mice had their learning and memory ability assessed by Morris water maze and nesting behavior scores;their pathology of brain tissue and Aβ expression observed using HE,Nissl and IHC staining;their polarization of microglia and the expression of inflammatory factors in hippocampal tissue detected by IF and ELISA;their hippocampal expression of PI3K/Akt/mTOR signaling pathway detected by RT-qPCR and Western blot.RESULTS Compared with the blank group,the model group had lower scores in total swimming distance,frequency in crossing the platform,residence time in the target quadrant,and nesting behavior scores(P＜0.05,P＜0.01);prolonged evasion latency(P＜0.01);more disorganized arrangement of pyramidal cells,solidification and deep staining,unclear demarcation,irregular cell shapes,reduction of Nyctinidia,and increased Aβ deposition in the brain tissue(P＜0.01);elevated expression of hippocampal microglia M1-type markers CD16/32 and lba-1(P＜0.01);decreased levels of M2-type marker CD206(P＜0.05);elevated levels of TNF-α and IL-1β(P＜0.01);decreased expressions of IL-13 and IL-4(P＜0.01);and decreased levels of PI3K,Akt and mTOR mRNA,and reduced p-PI3K,p-Akt and p-mTOR protein expressions(P＜0.01).Compared to the model group,the donepezil group and the Jiedu Yizhi Formula groups showed statistically significant improvements in the aforementioned indexes(P＜0.05,P＜0.01),with the magnitude of improvement being higher in the high-dose Jiedu Yizhi Formula group.CONCLUSION Jiedu Yizhi Formula suppresses microglia Ml-type polarization while enhancing M2-type polarization via activation the PI3K/Akt/mTOR signaling pathway,which subsequently reduces inflammatory cytokine secretion.This mechanism attenuates Aβ deposition in brain tissues and ameliorates cognitive dysfunction in AD mouse models.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

This study aims to reveal the effect and mechanism of Gentianella turkestanorum total extract(GTI) in ameliorating non-alcoholic steatohepatitis(NASH). UPLC-Q-TOF-MS was employed to identify the chemical components in GTI. SwissTarget-Prediction, GeneCards, OMIM, and TTD were utilized to screen the targets of GTI components and NASH. The common targets shared by GTI components and NASH were filtered through the STRING database and Cytoscape 3.9.0 to identify core targets, followed by GO and KEGG enrichment analysis. AutoDock was used for molecular docking of key components with core targets. A mouse model of NASH was established with a methionine-choline-deficient high-fat diet. A 4-week drug intervention was conducted, during which mouse weight was monitored, and the liver-to-brain ratio was measured at the end. Hematoxylin-eosin staining, Sirius red staining, and oil red O staining were employed to observe the pathological changes in the liver tissue. The levels of various biomarkers, including aspartate aminotransferase(AST), alanine aminotransferase(ALT), hydroxyproline(HYP), total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol(HDL-C), malondialdehyde(MDA), superoxide dismutase(SOD), and glutathione(GSH), in the serum and liver tissue were determined. RT-qPCR was conducted to measure the mRNA levels of interleukin 1β(IL-1β), interleukin 6(IL-6), tumor necrosis factor α(TNF-α), collagen type I α1 chain(COL1A1), and α-smooth muscle actin(α-SMA). Western blotting was conducted to determine the protein levels of IL-1β, IL-6, TNF-α, and potential drug targets identified through network pharmacology. UPLC-Q-TOF/MS identified 581 chemical components of GTI, and 534 targets of GTI and 1 157 targets of NASH were screened out. The topological analysis of the common targets shared by GTI and NASH identified core targets such as IL-1β, IL-6, protein kinase B(AKT), TNF, and peroxisome proliferator activated receptor gamma(PPARG). GO and KEGG analyses indicated that the ameliorating effect of GTI on NASH was related to inflammatory responses and the phosphoinositide 3-kinase(PI3K)/AKT pathway. The staining results demonstrated that GTI ameliorated hepatocyte vacuolation, swelling, ballooning, and lipid accumulation in NASH mice. Compared with the model group, high doses of GTI reduced the AST, ALT, HYP, TC, and TG levels(P<0.01) while increasing the HDL-C, SOD, and GSH levels(P<0.01). RT-qPCR results showed that GTI down-regulated the mRNA levels of IL-1β, IL-6, TNF-α, COL1A1, and α-SMA(P<0.01). Western blot results indicated that GTI down-regulated the protein levels of IL-1β, IL-6, TNF-α, phosphorylated PI3K(p-PI3K), phosphorylated AKT(p-AKT), phosphorylated inhibitor of nuclear factor kappa B alpha(p-IκBα), and nuclear factor kappa B(NF-κB)(P<0.01). In summary, GTI ameliorates inflammation, dyslipidemia, and oxidative stress associated with NASH by regulating the PI3K/AKT/NF-κB signaling pathway.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Mice ; Network Pharmacology ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Chromatography, High Pressure Liquid ; Liver/metabolism* ; Mice, Inbred C57BL ; Humans ; Mass Spectrometry ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans