Radiopharmaceuticals play an important role in the diagnosis and treatment of cardiovascular and cerebrovascular diseases， malignant tumors， central nervous system diseases， and other diseases. Under the urgent need for clinical diagnosis and treatment as well as medical development， the clinical research of radiopharmaceuticals has become a hotspot in international research. By analyzing the current situation of clinical research on radiopharmaceuticals in Europe， America， and China， the ethical issues of clinical research on radiopharmaceuticals were elaborated from four aspects， including lack of relevant laws and regulations， a higher risk of radiopharmaceuticals， dilemmas in ethical review， and insufficient radiation protection. Response principles and measures were proposed from four aspects， including improving regulations and policies， enhancing radiological protection for all parties involved in the research， strengthening ethical review， and reinforcing the training of relevant personnel， to enhance the quality and level of clinical research on radiopharmaceuticals.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective Based on the radiomics features extracted from the unenhanced CT images of the lower abdomen, a variety of machine learning models were constructed to explore their application value in the assessment of split renal function. Methods A retrospective analysis was conducted on the unenhanced CT images from 240 single kidneys in patients with clinically suspected renal dysfunction. Based on the results of single-photon emission computed tomography renal dynamic imaging, the cases were classified into the normal glomerular filtration rate group (n=118) and the decreased glomerular filtration rate group (n=122). The region of interest was outlined on the unenhanced CT images and the radiomics features were extracted. The features were selected by correlation analysis and least absolute shrinkage and selection operator, and the machine learning models were constructed based on the algorithms of decision tree, support vector machine, random forest, logistic regression, and extreme gradient boosting. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated to compare the performance of different models. Results Sixteen radiomics features were selected for constructing the machine learning models. The support vector machine model showed relatively high performance for the assessment of split renal function on the test set, with an area under the receiver operating characteristic curve value of 0.883 (95% confidence interval: 0.804-0.961), an accuracy of 0.778, a sensitivity of 0.811, and a specificity of 0.743. Conclusion The machine learning models constructed based on unenhanced CT radiomics can be used to preliminarily assess split renal function, which provides an innovative, convenient, and safe method for clinical diagnosis and has positive significance for treatment.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

ObjectiveThis study aimed to explore the effects of aerobic exercise on cognitive function in aging mice and to elucidate the underlying molecular mechanisms by which aerobic exercise ameliorates cognitive decline through the regulation of gut microbiota-metabolite network. By providing novel insights into the interplay between exercise, gut microbiota, and cognitive health, this research seeks to offer a robust theoretical foundation for developing anti-aging strategies and personalized exercise interventions targeting aging-related cognitive dysfunction. MethodsUsing naturally aged C57BL/6 mice as the experimental model, this study employed a multi-omics approach combining 16S rRNA sequencing and wide-targeted metabolomics analysis. A total of 18 mice were divided into 3 groups: young control (YC, 4-month-old), old control (OC, 21-month-old), and old+exercise (OE, 21-month-old with 12 weeks of moderate-intensity treadmill training) groups. Behavioral assessments, including the Morris water maze (MWM) test, were conducted to evaluate cognitive function. Histopathological examinations of brain tissue sections provided morphological evidence of neuronal changes. Fecal samples were collected for gut microbiota and metabolite profiling via 16S rRNA sequencing and ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS). Data were analyzed using a combination of statistical and bioinformatics tools to identify differentially abundant microbial taxa and metabolites and to construct interaction networks between them. ResultsBehavioral tests revealed that 12 weeks of aerobic exercise significantly improved spatial learning and memory capacity of aged mice, as evidenced by reduced escape latency and increased target area exploration and platform crossings in the MWM. Histopathological analysis demonstrated that exercise mitigated aging-related neuronal damage in the hippocampus, enhancing neuronal density and morphology. 16S rRNA sequencing indicated that exercise increased gut microbiota α‑diversity and enriched beneficial bacterial genera, including Bifidobacterium, Parabacteroides, and Rikenella. Metabolomics analysis identified 32 differentially regulated metabolites between OC and OE groups, with 94 up-regulated and 30 down-regulated in the OE group when compared with OC group. These metabolites were primarily involved in energy metabolism reprogramming (e.g., L-homocitrulline), antioxidant defense (e.g., L-carnosine), neuroprotection (e.g., lithocholic acid), and DNA repair (e.g., ADP-ribose). Network analysis further revealed strong positive correlations between specific bacteria and metabolites, such as Parabacteroides with ADP-ribose and Bifidobacterium with lithocholic acid, suggesting potential neuroprotective pathways mediated by the gut microbiota-metabolite axis. ConclusionThis study provides comprehensive evidence that aerobic exercise elicits cognitive benefits in aging mice by modulating the gut microbiota-metabolite network. These findings highlight three key mechanisms: (1) the proliferation of beneficial gut bacteria enhances metabolic reprogramming to boost DNA repair pathways; (2) elevated neuroinflammation-inhibiting factors reduce neurodegenerative changes; and (3) enhanced antioxidant defenses maintain neuronal homeostasis. These results underscore the critical role of the “microbiota-metabolite-brain” axis in mediating the cognitive benefits of aerobic exercise. This study not only advances our understanding of the gut-brain axis in aging but also offers a scientific basis for developing personalized exercise and probiotic-based interventions targeting aging-related cognitive decline. Future research should further validate these mechanisms in non-human primates and human clinical trials to establish the translational potential of exercise-induced gut microbiota-metabolite modulation for combating neurodegenerative diseases.

Objective To investigate dynamic changes in myocardial protein phosphorylation during the acute phase of myocardial infarction (MI) in mice. Methods Six 8-week-old C57BL/6J mice were randomly assigned to MI model (n＝3) or sham-operated control (n＝3) groups. Cardiac tissues were harvested 72 hours post-intervention for proteomic analysis. Phosphorylation modifications were systematically characterized using liquid chromatography-mass spectrometry (LC-MS). Bioinformatics analyses included differential phosphorylation screening, functional enrichment, hierarchical clustering, and protein-protein interaction network. Results LC-MS identified 1 921 differentially phosphorylated sites (20 tyrosine and 1 901 serine/threonine sites) across 851 proteins. Compared with controls, MI hearts exhibited significant phosphorylation upregulation at 1 545 sites and downregulation at 376 sites (P＜0.05). Conclusions This study delineates MI-associated phosphorylation dynamics, providing mechanistic insights and potential therapeutic targets for acute MI intervention.

Objective To understand the application effect of early screening scale and behavioral intervention effect in children with autism spectrum disorder (ASD). Methods A total of 348 children with suspected ASD were selected and evaluated using the Modified Checklist for Autism in Toddlers (M-CHAT) and Autism Behavior Checklist (ABC). The evaluation results were compared with those from the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). Children enrolled were given Early start Denver model (ESDM) intervention. The evaluation results of Gesell Developmental Scale and Autism Treatment Evaluation Checklist (ATEC) scores were compared before and after intervention. Results The sensitivity, specificity, accuracy and Kappa value of M-CHAT for evaluating ASD in children aged 1-3 years were 89.53%, 90.70%, 89.92% and 0.78. The corresponding values of ABC were 78.49%, 81.40%, 79.46% and 0.56. The sensitivity, specificity, accuracy and Kappa value of M-CHAT for evaluating children aged >3-6 years were 87.30%, 77.78%, 84.44% and 0.64. The corresponding values of ABC were 85.71%, 77.78%, 83.33% and 0.62. The sensitivity and accuracy of M-CHAT were higher than ABC for evaluating ASD in children aged 1-3 years (P<0.05). There were no significant differences in sensitivity, specificity and accuracy between M-CHAT and ABC for evaluating ASD in children aged 3-6 years (P>0.05). After intervention, development quotients (DQ) of personal-social aspects, adaptability, language, gross motor, and fine motor of children with ASD were higher than those before intervention (P<0.05). ATEC scores for language, behavior, sensation, and social contact of children with ASD were lower than those before intervention (P<0.05). Conclusion M-CHAT and ABC both can be used for early screening of ASD in children, especially M-CHAT. Early behavioral intervention can effectively improve the condition and developmental level of children with ASD.