Objective To evaluate the effect of low-dose ¹⁸F-fluorodeoxyglucose (FDG) injection on image quality in pediatric patients, calculate the whole-body effective dose and absorbed doses to critical organs, and provide a reference for clinical protection in pediatric low-dose positron emission tomography/computed tomography (PET/CT) examination. Methods A total of 67 pediatric patients aged 4-14 years who underwent ¹⁸F-FDG whole-body PET/CT imaging in the Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University between January 2024 and December 2025 were enrolled in a retrospective study. According to the injected ¹⁸F-FDG dose, patients were divided into a full-dose group (37 cases, 2.96-3.7 MBq/kg, PET acquisition duration of 10 min) and a low-dose group (30 cases, 1.85-2.4 MBq/kg, PET acquisition duration of 15 min). All scans were performed using a uEXPLORER PET/CT scanner. The standardized uptake value, standard deviation, and signal-to-noise ratio were measured for the liver and mediastinal blood pool. Image quality was compared between the two groups. The PET effective dose was calculated based on body mass index, and the total effective dose was obtained by combining PET effective dose with CT effective dose. The doses to critical organs were calculated according to ICRP Publication 128. Statistical analysis was performed using SPSS 26.0 and Prism 9.0 software. Differences were compared between groups. Results There were no significant differences in age, sex, weight, height, or body mass index between the two groups (P>0.05), while the injected dose showed a significant difference (P<0.001). In terms of image quality, there were no significant differences in standardized uptake value, standard deviation, and signal-to-noise ratio between the two groups. In terms of radiation dose, compared with the full-dose group, the low-dose group showed a 35.13% reduction in PET effective dose and a 15.22% reduction in total effective dose (P<0.05), whereas the CT effective dose was reduced by 2.4% and the difference was not significant (P=0.0892). The doses to critical organs (gonads, breasts, thyroid, red bone marrow, lungs) in the low-dose group were significantly lower than those in the full-dose group (P<0.01). Conclusion Under the premise of meeting clinical diagnostic requirements, low-dose ¹⁸F-FDG whole-body PET/CT imaging in pediatric patients can reduce the overall radiation exposure by 15%, and decrease the doses to critical organs, including the gonads, by more than 20%.

Objective To evaluate the effect of low-dose ¹⁸F-fluorodeoxyglucose (FDG) injection on image quality in pediatric patients, calculate the whole-body effective dose and absorbed doses to critical organs, and provide a reference for clinical protection in pediatric low-dose positron emission tomography/computed tomography (PET/CT) examination. Methods A total of 67 pediatric patients aged 4-14 years who underwent ¹⁸F-FDG whole-body PET/CT imaging in the Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University between January 2024 and December 2025 were enrolled in a retrospective study. According to the injected ¹⁸F-FDG dose, patients were divided into a full-dose group (37 cases, 2.96-3.7 MBq/kg, PET acquisition duration of 10 min) and a low-dose group (30 cases, 1.85-2.4 MBq/kg, PET acquisition duration of 15 min). All scans were performed using a uEXPLORER PET/CT scanner. The standardized uptake value, standard deviation, and signal-to-noise ratio were measured for the liver and mediastinal blood pool. Image quality was compared between the two groups. The PET effective dose was calculated based on body mass index, and the total effective dose was obtained by combining PET effective dose with CT effective dose. The doses to critical organs were calculated according to ICRP Publication 128. Statistical analysis was performed using SPSS 26.0 and Prism 9.0 software. Differences were compared between groups. Results There were no significant differences in age, sex, weight, height, or body mass index between the two groups (P>0.05), while the injected dose showed a significant difference (P<0.001). In terms of image quality, there were no significant differences in standardized uptake value, standard deviation, and signal-to-noise ratio between the two groups. In terms of radiation dose, compared with the full-dose group, the low-dose group showed a 35.13% reduction in PET effective dose and a 15.22% reduction in total effective dose (P<0.05), whereas the CT effective dose was reduced by 2.4% and the difference was not significant (P=0.0892). The doses to critical organs (gonads, breasts, thyroid, red bone marrow, lungs) in the low-dose group were significantly lower than those in the full-dose group (P<0.01). Conclusion Under the premise of meeting clinical diagnostic requirements, low-dose ¹⁸F-FDG whole-body PET/CT imaging in pediatric patients can reduce the overall radiation exposure by 15%, and decrease the doses to critical organs, including the gonads, by more than 20%.

Objective To evaluate the effect of low-dose ¹⁸F-fluorodeoxyglucose (FDG) injection on image quality in pediatric patients, calculate the whole-body effective dose and absorbed doses to critical organs, and provide a reference for clinical protection in pediatric low-dose positron emission tomography/computed tomography (PET/CT) examination. Methods A total of 67 pediatric patients aged 4-14 years who underwent ¹⁸F-FDG whole-body PET/CT imaging in the Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University between January 2024 and December 2025 were enrolled in a retrospective study. According to the injected ¹⁸F-FDG dose, patients were divided into a full-dose group (37 cases, 2.96-3.7 MBq/kg, PET acquisition duration of 10 min) and a low-dose group (30 cases, 1.85-2.4 MBq/kg, PET acquisition duration of 15 min). All scans were performed using a uEXPLORER PET/CT scanner. The standardized uptake value, standard deviation, and signal-to-noise ratio were measured for the liver and mediastinal blood pool. Image quality was compared between the two groups. The PET effective dose was calculated based on body mass index, and the total effective dose was obtained by combining PET effective dose with CT effective dose. The doses to critical organs were calculated according to ICRP Publication 128. Statistical analysis was performed using SPSS 26.0 and Prism 9.0 software. Differences were compared between groups. Results There were no significant differences in age, sex, weight, height, or body mass index between the two groups (P>0.05), while the injected dose showed a significant difference (P<0.001). In terms of image quality, there were no significant differences in standardized uptake value, standard deviation, and signal-to-noise ratio between the two groups. In terms of radiation dose, compared with the full-dose group, the low-dose group showed a 35.13% reduction in PET effective dose and a 15.22% reduction in total effective dose (P<0.05), whereas the CT effective dose was reduced by 2.4% and the difference was not significant (P=0.0892). The doses to critical organs (gonads, breasts, thyroid, red bone marrow, lungs) in the low-dose group were significantly lower than those in the full-dose group (P<0.01). Conclusion Under the premise of meeting clinical diagnostic requirements, low-dose ¹⁸F-FDG whole-body PET/CT imaging in pediatric patients can reduce the overall radiation exposure by 15%, and decrease the doses to critical organs, including the gonads, by more than 20%.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Background:Bushen Zhuanggu Formula(BZF),derived from the classic Yougui Pills,has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head(NONFH),particularly by promoting bone repair.However,its underlying mechanisms remain unclear.Objective:This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH.Materials and methods:A total of 518 potential BZF targets were identified from the ETCM v2.0 database.Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls.A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis.In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH.Results:Network analysis identified key pathways associated with blood circulation obstruction,immune-inflammatory imbalance,and abnormal bone metabolism.Protein kinase C alpha(PKCA),Ras proto-oncogene(RAS),mitogen-activated protein kinase 3(ERK),ETS proto-oncogene 1(ETS1),and receptor activator of nuclear factor-κB ligand(RANKL)formed a signaling axis implicated in NONFH pathogenesis.BZF treatment alleviated joint inflammation,preserved trabecular bone morphology,reduced bone loss,and promoted bone repair.Mechanistically,BZF significantly downregulated the expression of PKCA,RAS,ERK,ETS1,and RANKL,improved blood circulation,and inhibited osteoclast activation while promoting osteoblast activation.Conclusion:BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis,thereby reversing dysregulated bone metabolism.

Autoantibody immunoassays are critical in the diagnosis, disease stratification and prognosis of autoimmune disease (AID). Many important autoantibodies have been included in the diagnostic criteria for the classification and stratification of AID. However, the poor comparability among different methodologies may lead to significant differences in the interpretation of autoantibody test results, which poses a great challenge for the clinical application of autoantibody testing. The aim of this article is to discuss the main factors affecting the comparability of autoantibody test results, including the heterogeneity of autoantibodies, the variability of antigens, the setting of the positive judgment value for qualitative tests, and the traceability of calibrators for quantitative tests. In addition, this article also discusses the role of external quality assessment in improving the comparability of autoantibody test results.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Background: Bushen Zhuanggu Formula (BZF), derived from the classic Yougui Pills, has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head (NONFH), particularly by promoting bone repair. However, its underlying mechanisms remain unclear. Objective: This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH. Materials and methods: A total of 518 potential BZF targets were identified from the ETCM v2.0 database. Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls. A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis. In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH. Results: Network analysis identified key pathways associated with blood circulation obstruction, immune-inflammatory imbalance, and abnormal bone metabolism. Protein kinase C alpha (PKCA), Ras proto-oncogene (RAS), mitogen-activated protein kinase 3(ERK), ETS proto-oncogene 1 (ETS1), and receptor activator of nuclear factor-κB ligand (RANKL) formed a signaling axis implicated in NONFH pathogenesis. BZF treatment alleviated joint inflammation, preserved trabecular bone morphology, reduced bone loss, and promoted bone repair. Mechanistically, BZF significantly downregulated the expression of PKCA, RAS, ERK, ETS1, and RANKL, improved blood circulation, and inhibited osteoclast activation while promoting osteoblast activation. Conclusion: BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis, thereby reversing dysregulated bone metabolism.

The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network's focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
Humans ; Attention/physiology* ; Speech Perception/physiology* ; Neural Networks, Computer ; Speech ; Noise ; Feedback