Prostate cancer is one of the most common malignant tumors of male genitourinary system. Prostate cancer has the following characteristics： insidious onset， early asymptomatic or not obvious symptoms， complex etiology and pathogenesis， long incubation period and so on. Therefore， the realization of its early diagnosis and treatment is of great significance to the prognosis of patients. Prostate-specific membrane antigen （PSMA） is a type 2 transmembrane glycoprotein that is highly expressed on the membrane of almost all primary and metastatic prostate cancer cells， and is an ideal target for prostate cancer imaging and treatment. In recent years， with the approval of urea-containing small molecule PET （positron emission computed tomography） radiopharmaceutical based on PSMA （⁶⁸Ga-PSMA-11， ¹⁸F-PSMA-1007）， PET-CT （positron emission computed tomography/computed tomography） has shown new potential for early diagnosis and accurate staging of prostate cancer patients. This review mainly summarizes the research progress of urea-containing PSMA PET imaging agents and finds that they have defects such as uptake in non-target tissues like the kidneys， lacrimal glands， and salivary glands. Thus， further optimizing their structure to reduce the uptake in non-target tissues， providing provide convenience for the labeling of therapeutic radiopharmaceuticals， thereby achieving the goal of integrated diagnosis and treatment， is an important development direction in this field.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Lung cancer has the highest morbidity and mortality rate among all cancers. Because of the complex pathogenesis， there are limitations in the common Western medicine treatment methods. Clinical and experimental studies have proved that traditional Chinese medicine （TCM） can not only effectively treat lung cancer and alleviate the clinical symptoms of cancer patients but also reduce the adverse reactions and complications caused by surgery， chemotherapy， and radiotherapy to improve the quality of life of the patients. The biological behaviors of lung cancer cells， such as proliferation， invasion， and metastasis， are closely related to their metabolic reprogramming. Metabolic reprogramming in lung cancer involves a series of metabolic changes such as increased glucose uptake and consumption， enhanced glycolysis， increased amino acid uptake and catabolism， and enhanced lipid and protein synthesis. Studies have reported that TCM active components， extracts， and compound prescriptions can effectively inhibit the biological behaviors of lung cancer by regulating metabolic reprogramming. Therefore， this paper reviews the pharmacological mechanisms of TCM active components， extracts， and compound prescriptions in regulating metabolic reprogramming of lung cancer， with the aim of providing a new way of thinking for the treatment of lung cancer by TCM regulation of metabolic reprogramming of lung cancer cells. The available studies suggest that TCM mainly inhibits the extracellular signal-regulated protein kinase （ERK）/c-Myc， phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， and hypoxia-inducible factor-α （HIF-1α） pathways. Furthermore， the expression of monocarboxylate transporter 4 （MCT4）， glucose transporter 1 （GLUT1）， pyruvate dehydrogenase （PDH）， phosphofructokinase 1 （PFK1）， pyruvate dehydrogenase kinase 1 （PDK1）， pyruvate kinase M2 （PKM2）， hexokinase （HK）， lactate dehydrogenase （LDH）， and lactate dehydrogenase A （LDHA） are inhibited. In this way， TCM inhibits the glucose uptake by lung cancer cells and glycolysis in lung cancer cells to reduce the energy metabolism of tumor cells， ultimately achieving the therapeutic effect on lung cancer.

BACKGROUND: Pancreatic cancer is a lethal malignancy prone to gemcitabine resistance. The single-strand selective monofunctional uracil DNA glycosylase (SMUG1), which is responsible for initiating base excision repair, has been reported to predict the outcomes of different cancer types. However, the function of SMUG1 in pancreatic cancer is still unclear. METHODS: Gene and protein expression of SMUG1 as well as survival outcomes were assessed by bioinformatic analysis and verified in a cohort from Fudan University Shanghai Cancer Center. Subsequently, the effect of SMUG1 on proliferation, cell cycle, and migration abilities of SMUG1 cells were detected in vitro . DNA damage repair, apoptosis, and gemcitabine resistance were also tested. RNA sequencing was performed to determine the differentially expressed genes and signaling pathways, followed by quantitative real-time polymerase chain reaction and Western blotting verification. The cancer-promoting effect of forkhead box Q1 (FOXQ1) and SMUG1 on the ubiquitylation of myelocytomatosis oncogene (c-Myc) was also evaluated. Finally, a xenograft model was established to verify the results. RESULTS: SMUG1 was highly expressed in pancreatic tumor tissues and cells, which also predicted a poor prognosis. Downregulation of SMUG1 inhibited the proliferation, G1 to S transition, migration, and DNA damage repair ability against gemcitabine in pancreatic cancer cells. SMUG1 exerted its function by binding with FOXQ1 to activate the Protein Kinase B (AKT)/p21 and p27 pathway. Moreover, SMUG1 also stabilized the c-Myc protein via AKT signaling in pancreatic cancer cells. CONCLUSIONS SMUG1 promotes proliferation, migration, gemcitabine resistance, and c-Myc protein stability in pancreatic cancer via protein kinase B signaling through binding with FOXQ1. Furthermore, SMUG1 may be a new potential prognostic and gemcitabine resistance predictor in pancreatic ductal adenocarcinoma.
Humans ; Pancreatic Neoplasms/pathology* ; Forkhead Transcription Factors/genetics* ; Signal Transduction/genetics* ; Animals ; Cell Line, Tumor ; Proto-Oncogene Proteins c-akt/metabolism* ; Cell Proliferation/physiology* ; Mice ; Uracil-DNA Glycosidase/genetics* ; Female ; Male ; Gemcitabine ; Mice, Nude ; Apoptosis/physiology* ; Deoxycytidine/analogs & derivatives* ; Cell Movement/genetics*

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.