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 assess the professional knowledge proficiency of mainstream large language models (LLMs) in medical laboratory education and to explore their potential as educational aids for medical laboratory technology students.Methods:A comprehensive evaluation was conducted using 400 authentic questions from the 2023 Chinese National Clinical Medical Laboratory Technician Qualification Examination. Five LLMs (Copilot, Grok, Yuanbao, Doubao, and Kimi) were tested through two-round interactions using zero-shot prompting and interaction-optimized prompting strategies. The accuracy of answers and the quality of generated content were evaluated. Performance disparities were analyzed using Cochran's Q test. Content quality was scored through the CLEAR framework (completeness, lack of false information, evidence-based reasoning, appropriateness, relevance).Results:In the first-round test, Doubao achieved the highest overall accuracy rate (375/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot and Kimi ( P<0.001). After the second-round interactive optimization, the accuracy rate of Kimi significantly improved ( P<0.05), whereas other LLMs showed slight improvements ( P>0.05). Doubao still had the highest overall accuracy rate (380/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot ( P<0.005). Evaluation based on the CLEAR framework revealed that Yuanbao, Doubao, and Kimi significantly outperformed foreign models in the dimensions of evidence-based reasoning ( P<0.003) and completeness ( P<0.05), demonstrating standardized citation of authoritative evidence and superior content quality. Conclusions:The tested LLMs possess extensive medical laboratory knowledge. The accuracy of their answers and the quality of the generated content can be improved through single-question input, specifying evidence requirements, and enabling advanced reasoning functions. Domestic LLMs are comparable to foreign LLMs in terms of accuracy, and have significant advantages in the dimensions of evidence-based reasoning and completeness. LLMs can serve as auxiliary tools for learning professional knowledge in medical laboratory technology.

BACKGROUND:Myocardial cell hypoxic injury is closely associated with oxidative stress and ferroptosis.Previous studies have shown that aloin has various effects such as antioxidant,anti-inflammatory,and anti-tumor activities.OBJECTIVE:To investigate the effects of aloin on oxidative stress and ferroptosis in hypoxia-induced H9C2 cells.METHODS:A hypoxia model was established using H9C2 myocardial cells.Firstly,cell viability was determined to confirm the lack of cytotoxicity of aloin and to determine its optimal therapeutic concentration.Subsequently,the effects of aloin on hypoxia-induced lactate dehydrogenase release,reactive oxygen species production,and mitochondrial oxidative stress in H9C2 cells were evaluated using assay kits,dihydroethidium fluorescent probes,and MitoSOX?Red fluorescent probes,respectively.To verify the effect of aloin on ferroptosis,intracellular Fe2+content and lipid peroxidation level were detected using fluorescence staining and flow cytometry,respectively.Then,the expression levels of ferroptosis regulatory factors glutathione peroxidase 4,acyl-CoA synthetase long-chain family member 4,and nuclear factor E2-related factor 2 were detected using western blot assay and real-time fluorescence quantitative PCR techniques.Finally,the role of ferroptosis in aloin-mediated myocardial protection was further confirmed by using the ferroptosis inducer Erastin.RESULTS AND CONCLUSION:(1)Compared with the control group,the viability of H9C2 cells in the hypoxia group was significantly decreased,lactate dehydrogenase release,reactive oxygen species level,mitochondrial oxidative stress degree,Fe2+content,and lipid peroxidation degree were significantly increased,while glutathione peroxidase 4 and nuclear factor E2-related factor 2 mRNA and protein expression levels were significantly decreased,and acyl-CoA synthetase long-chain family member 4 mRNA and protein expression were significantly increased(all P＜0.05).(2)Compared with the hypoxia group,both low and high doses of aloin reversed the changes in above indicators(all P＜0.05).(3)Compared with the hypoxia+aloin group,the hypoxia+aloin+Erastin group showed a significant decrease in H9C2 cell viability and a significant increase in lactate dehydrogenase release(both P＜0.01).The results indicate that aloin has a protective effect on hypoxia-treated H9C2 cells in a dose-dependent manner,mainly achieved by inhibiting oxidative stress and ferroptosis.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

OBJECTIVES: The diagnostic value of ultrasonographic quantitative indicators of umbilical cord coiling, such as the umbilical coiling index (UCI) and pitch value, in identifying hypercoiling and predicting adverse pregnancy outcomes remains controversial. This study aims to evaluate the predictive value of UCI, pitch value, and the cerebroplacental ratio in pregnancies complicated by umbilical cord hypercoiling. METHODS: Pregnant women with densely coiled umbilical cords identified by routine obstetric ultrasound at Changsha Maternal and Child Health Hospital between November 2022 and November 2024 were enrolled. Complete clinical data, including UCI, pitch value, and cerebroplacental ratio (CPR), were collected. Pregnancy outcome scores were calculated, and newborns were categorized into the normal outcome group (n=177) and adverse outcome group (n=85), with the latter further subdivided into mild (n=51), moderate (n=19), and severe (n=15) subgroups. Differences in baseline data, UCI, pitch value, and incidence of CRP<1 were compared between groups and among subgroups. Correlations between UCI, pitch value, and adverse pregnancy outcomes were analyzed. Receiver operating characteristic (ROC) curve were used to assess the predictive performance of UCI, pitch value, CPR<1, and their combinations. RESULTS: Compared with the normal outcome group, the adverse outcome group had higher age, parity, parity, incidence of CPR<1, and UCI, while gestational age at delivery and pitch values were lower (all P<0.05). The incidence of obesity, gestational diabetes mellitus, and hypertensive disorders of pregnancy did not differ significantly between the 2 groups (all P>0.05). The normal outcome group showed lower UCI and higher pitch values than all 3 adverse outcome subgroups (all P<0.05), while differences among the 3 adverse subgroups were not significant (all P>0.05). UCI was positively correlated with adverse pregnancy outcomes (r_s=0.350, P<0.05), whereas pitch value was negatively correlated (r_s=-0.286, P<0.05). ROC curve analysis showed that the area under the curve (AUC) values for predicting adverse outcomes were 0.837 for UCI, 0.886 for pitch value, and 0.610 for CPR<1, with sensitivities of 77.6%, 82.4%, and 27.1% and specificities of 78.5%, 83.6%, and 94.9%, respectively. The combined UCI+CPR<1 and pitch value+CPR<1 models yielded AUCs of 0.841 and 0.886, with sensitivities of 78.8% and 81.2% and specificities of 78.5% and 84.2%, respectively. No significant differences were found between the AUCs of UCI and pitch value (P>0.05), but both outperformed CPR<1 alone (both P<0.001). The combined models showed no significant improvement over UCI or pitch value alone (both P>0.05), though both were superior to CPR<1 alone (both P<0.001). CONCLUSIONS Most umbilical cord hypercoiling cases had favorable outcomes, with UCI, pitch value, CPR<1 and their combinations demonstrating significant predictive value for adverse pregnancy outcomes.
Humans ; Female ; Pregnancy ; Pregnancy Outcome ; Adult ; Ultrasonography, Prenatal/methods* ; Umbilical Cord/diagnostic imaging* ; Hemodynamics ; Predictive Value of Tests ; Infant, Newborn ; ROC Curve

BACKGROUND:Myocardial cell hypoxic injury is closely associated with oxidative stress and ferroptosis.Previous studies have shown that aloin has various effects such as antioxidant,anti-inflammatory,and anti-tumor activities.OBJECTIVE:To investigate the effects of aloin on oxidative stress and ferroptosis in hypoxia-induced H9C2 cells.METHODS:A hypoxia model was established using H9C2 myocardial cells.Firstly,cell viability was determined to confirm the lack of cytotoxicity of aloin and to determine its optimal therapeutic concentration.Subsequently,the effects of aloin on hypoxia-induced lactate dehydrogenase release,reactive oxygen species production,and mitochondrial oxidative stress in H9C2 cells were evaluated using assay kits,dihydroethidium fluorescent probes,and MitoSOX?Red fluorescent probes,respectively.To verify the effect of aloin on ferroptosis,intracellular Fe2+content and lipid peroxidation level were detected using fluorescence staining and flow cytometry,respectively.Then,the expression levels of ferroptosis regulatory factors glutathione peroxidase 4,acyl-CoA synthetase long-chain family member 4,and nuclear factor E2-related factor 2 were detected using western blot assay and real-time fluorescence quantitative PCR techniques.Finally,the role of ferroptosis in aloin-mediated myocardial protection was further confirmed by using the ferroptosis inducer Erastin.RESULTS AND CONCLUSION:(1)Compared with the control group,the viability of H9C2 cells in the hypoxia group was significantly decreased,lactate dehydrogenase release,reactive oxygen species level,mitochondrial oxidative stress degree,Fe2+content,and lipid peroxidation degree were significantly increased,while glutathione peroxidase 4 and nuclear factor E2-related factor 2 mRNA and protein expression levels were significantly decreased,and acyl-CoA synthetase long-chain family member 4 mRNA and protein expression were significantly increased(all P＜0.05).(2)Compared with the hypoxia group,both low and high doses of aloin reversed the changes in above indicators(all P＜0.05).(3)Compared with the hypoxia+aloin group,the hypoxia+aloin+Erastin group showed a significant decrease in H9C2 cell viability and a significant increase in lactate dehydrogenase release(both P＜0.01).The results indicate that aloin has a protective effect on hypoxia-treated H9C2 cells in a dose-dependent manner,mainly achieved by inhibiting oxidative stress and ferroptosis.

Objective:To assess the professional knowledge proficiency of mainstream large language models (LLMs) in medical laboratory education and to explore their potential as educational aids for medical laboratory technology students.Methods:A comprehensive evaluation was conducted using 400 authentic questions from the 2023 Chinese National Clinical Medical Laboratory Technician Qualification Examination. Five LLMs (Copilot, Grok, Yuanbao, Doubao, and Kimi) were tested through two-round interactions using zero-shot prompting and interaction-optimized prompting strategies. The accuracy of answers and the quality of generated content were evaluated. Performance disparities were analyzed using Cochran's Q test. Content quality was scored through the CLEAR framework (completeness, lack of false information, evidence-based reasoning, appropriateness, relevance).Results:In the first-round test, Doubao achieved the highest overall accuracy rate (375/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot and Kimi ( P<0.001). After the second-round interactive optimization, the accuracy rate of Kimi significantly improved ( P<0.05), whereas other LLMs showed slight improvements ( P>0.05). Doubao still had the highest overall accuracy rate (380/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot ( P<0.005). Evaluation based on the CLEAR framework revealed that Yuanbao, Doubao, and Kimi significantly outperformed foreign models in the dimensions of evidence-based reasoning ( P<0.003) and completeness ( P<0.05), demonstrating standardized citation of authoritative evidence and superior content quality. Conclusions:The tested LLMs possess extensive medical laboratory knowledge. The accuracy of their answers and the quality of the generated content can be improved through single-question input, specifying evidence requirements, and enabling advanced reasoning functions. Domestic LLMs are comparable to foreign LLMs in terms of accuracy, and have significant advantages in the dimensions of evidence-based reasoning and completeness. LLMs can serve as auxiliary tools for learning professional knowledge in medical laboratory technology.

Objective:To investigate the relationship between positive expression of multiple myeloma oncogene 1 (MUM1) and changes of endometrial endometrium in patients with repeated implantation failure (RIF).Methods:A retrospective cohort study of 62 RIF patients who attended the Center for Reproductive Medicine of the Second Affiliated Hospital of Zhengzhou University between March 2022 and May 2023 were selected. All patients were examined by perimetry, endometrial tissue was collected, and the positive expression of MUM1, vascular endothelial growth factor (VEGF), and microvessel density (MVD) were detected by immunohistochemical staining. The endometrial hemodynamics were examined by three-dimensional Doppler ultrasound, and according to the results of MUM1 expression, the patients were divided into MUM1-positive group (29 cases) and MUM1-negative group (33 cases). The endometrial thickness, endometrial blood flow vascularization index (VI), flow index (FI), vascularized blood flow index (VFI), and bilateral uterine arterial blood flow resistance index (RI), pulsatility index (PI), peak systolic velocity/peak diastolic velocity (S/D) were compared between the two groups. The expression levels of endometrial VEGF and MVD were compared between the two groups. Pearson correlation was used to explore the correlation between the expression of endometrial VEGF, MVD and endometrial blood flow VI, FI, VFI in the two groups.Results:In RIF patients, the endometrial VEGF positivity rate [31.03% (9/29)], MVD expression (16.97±3.28), and endometrial VI (1.45±0.96), FI (21.13±5.33), and VFI (0.39±0.17) in MUM1-positive group were significantly lower than those in the MUM1-negative group [the VEGF-positive rate: 36.36% (12/33), P<0.001; MVD: 33.42±7.97, P<0.001; VI: 1.91±0.56, P=0.025; FI: 23.91±5.37, P=0.045; VFI: 0.54±0.14, P=0.004]. Bilateral uterine arterial blood flow RI (0.69±0.14), PI (1.49±0.61), S/D (2.40±0.62) in MUM1-positive group was significantly higher than those in MUM1-negative group (0.54±0.15, 0.92±0.34, 1.45±0.40; all P<0.001). There was a significant positive correlation between the endometrial MVD expression level in the MUM1-positive group and endometrial blood flow of VI, FI, and VFI ( r=0.931, P<0.001; r=0.968 , P<0.001; r=0.914, P<0.001), and there was also a significant positive correlation between endometrial VEGF expression and endometrial blood flow VI, FI, and VFI ( r=0.965 , P<0.001; r=0.954, P<0.001; r=0.935, P<0.001). Conclusion:In RIF patients, the positive expression of MUM1 affects endometrial blood flow and the expression levels of VEGF and MVD, and there is a significant positive correlation between endometrial blood flow VI, FI, VFI and the expression of VEGF and MVD in the endometrium.

Objective A neuroblastoma(NB)liver transplantation model was established and compared with the adrenal orthotopic transplantation model to explore its characteristics.Methods 5× 105 SK-N-SH cells were implanted along the long axis of the left lobe of mouse livers with a micro-injection needle.The growth,metastasis,expression of related genes,and histopathological changes of tumors were detected after the modeling.Results The tumor formation rate in mice inoculated with tumor cells reached 100％after 21 days,and tumor growth,metastasis,related gene expression changes,and pathological characteristics were apparent.Conclusions In this study,a neuroblastoma liver transplantation model was successfully constructed via a relatively simple surgical method to provide a more suitable choice for future scientific NB experiments.