Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Hypertrophic cardiomyopathy (HCM) is a major contributor to cardiovascular diseases (CVD), the leading cause of death globally. HCM can precipitate heart failure (HF) by causing the cardiac tissue to weaken and stretch, thereby impairing its pumping efficiency. Moreover, HCM increases the risk of atrial fibrillation, which in turn elevates the likelihood of thrombus formation and stroke. Given these significant clinical ramifications, research into the etiology and pathogenesis of HCM is intensifying at multiple levels. In this review, we discuss and synthesize the latest findings on HCM pathogenesis, drawing on key experimental studies conducted both in vitro and in vivo. We also offer our insights and perspectives on these mechanisms, while highlighting the limitations of current research. Advancing fundamental research in this area is essential for developing effective therapeutic interventions and enhancing the clinical management of HCM.
Cardiomyopathy, Hypertrophic/physiopathology* ; Humans ; Animals

OBJECTIVES: To explore the underlying pharmacological mechanisms and its potential effects of Chinese medicine herbal formula Sini Powder (SNP) on hepatocellular carcinoma (HCC). METHODS: The active components of SNP and their in vivo distribution were identified using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Construction of component-target-disease networks, protein-protein interaction network, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and molecular docking were employed to analyze the active components and anti-HCC mechanisms of SNP. Cell viability assay and wound healing assay were utilized to confirm the effect of SNP-containing serum (2.5%, 5.0%, 10%, 20%, and 40%), isoprenaline or propranolol (both 10, 100, and 1,000 µ mol/L) on proliferation and migration of HepG 2 or Huh7 cells. Meanwhile, the effect of isoprenaline or propranolol on the β 2 adrenergic receptor (ADRB2) mRNA expression on HepG2 cells were measured by real-time quantitative reverse transcription (RT-qPCR). Mice with subcutaneous tumors were either subjected to chronic restraint stress (CRS) followed by SNP administration (364 mg/mL) or directly treated with SNP (364 mg/mL). These two parallel experiments were performed to validate the effects of SNP on stress responses. Stress-related proteins and hormones were quantified using RT-qPCR, enzyme-linked immunosorbent assay, and immunohistochemistry. Metagenomic sequencing was performed to confirm the influence of SNP on the gut microbiota in the tumor-bearing CRS mice. RESULTS: The distribution of the 12 active components of SNP was confirmed in various tissues and feces. Network pharmacology analysis confirmed the anti-HCC effects of the 5 active components. The potential anti-HCC mechanisms of SNP may involve the epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and signal transducer and activator of transcription 3 (STAT3) pathways. SNP-containing serum inhibited the proliferation of HepG2 and Huh7 cells at concentrations of 2.5% and 5.0%, respectively, after 24 h of treatment. Furthermore, SNP suppressed tumor progression in tumor-bearing mice exposed to CRS. SNP treatment also downregulated the expressions of stress-related proteins and pro-inflammatory cytokines, primarily by modulating the gut microbiota. Specifically, the abundance of Alistipes and Prevotella, which belong to the phylum Bacteroidetes, increased in the SNP-treated group, whereas Lachnospira, in the phylum Firmicutes, decreased. CONCLUSION SNP can combat HCC by alleviating stress responses through the regulation of gut microbiota.
Animals ; Gastrointestinal Microbiome/drug effects* ; Liver Neoplasms/microbiology* ; Carcinoma, Hepatocellular/microbiology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Powders ; Cell Proliferation/drug effects* ; Mice ; Molecular Docking Simulation ; Cell Line, Tumor ; Hep G2 Cells ; Receptors, Adrenergic, beta-2/genetics* ; Stress, Physiological/drug effects* ; Cell Movement/drug effects* ; Male ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Proto-Oncogene Mas

OBJECTIVE: To investigate the effects of acupuncture on the neurotransmitter levels and gut microbiota in a mouse model of Tourette syndrome (TS). METHODS: Thirty-six male C57/BL6 mice were randomly divided into 4 groups using a random number table method: 3,3'-iminodipropionitrile (IDPN) group, control group, acupuncture group, and tiapride group, with 9 mice in each group. In the IDPN group, acupuncture group, and tiapride group, mice received daily intraperitoneal injections of IDPN (300 mg/kg body weight) for 7 consecutive days to induce stereotyped behaviors. Subsequently, in the acupuncture intervention group, standardized acupuncture treatment was administered for 14 consecutive days to IDPN-induced TS model mice. The selected acupoints included Baihui (DU 20), Yintang (DU 29), Waiguan (SJ 5), and Zulinqi (GB 41). In the tiapride group, mice were administered tiapride (50 mg/kg body weight) via oral gavage daily for 14 consecutive days. The control group, IDPN group, and acupuncture group received the same volume of saline orally for 14 consecutive days. Stereotypic behaviors were quantified through behavioral assessments. Neurotransmitter levels, including dopamine (DA), glutamate (Glu), and aspartate (ASP) in striatal tissue were measured using enzyme-linked immunosorbent assay. Dopamine transporter (DAT) expression levels were additionally quantified through quantitative polymerase chain reaction (qPCR). Gut microbial composition was analyzed through 16S ribosomal RNA gene sequencing, while metabolic profiling was conducted using liquid chromatography-mass spectrometry (LC-MS). RESULTS: Acupuncture administration significantly attenuated stereotypic behaviors, concurrently reducing striatal levels of DA, Glu and ASP concentrations while upregulating DAT expression compared with untreated TS controls (P<0.05 or P<0.01). Comparative analysis identified significant differences in Muribaculaceae (P=0.001), Oscillospiraceae (P=0.049), Desulfovibrionaceae (P=0.001), and Marinifilaceae (P=0.014) following acupuncture intervention. Metabolomic profiling revealed alterations in 7 metabolites and 18 metabolic pathways when compared to the TS mice, which involved various amino acid metabolisms associated with DA, Glu, and ASP. CONCLUSIONS Acupuncture demonstrates significant modulatory effects on both central neurotransmitter systems and gut microbial ecology, thereby highlighting its dual therapeutic potential for TS management through gut-brain axis regulation.
Animals ; Tourette Syndrome/metabolism* ; Gastrointestinal Microbiome ; Neurotransmitter Agents/metabolism* ; Acupuncture Therapy ; Male ; Mice, Inbred C57BL ; Mice

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Background and purpose:The treatment of recurrent,metastatic,and treatment-na?ve advanced cervical cancer remains challenging.Immunotherapy in combination with chemotherapy and targeted therapy has shown preliminary clinical benefits,however,current evidence remains limited.This study aimed to evaluate the impact of camrelizumab combined with chemotherapy and targeted therapy on the prognosis of patients with recurrent,metastatic,and treatment-na?ve advanced cervical cancer.Methods:In this study,we conducted a retrospective analysis of the clinical data from 130 patients with recurrent,metastatic,and treatment-na?ve advanced cervical cancer admitted to Minhang Branch of Fudan University Shanghai Cancer Center from 2019 to 2025.The patients were categorized into the observation group(n=70),which included those who received camrelizumab with or without chemotherapy and targeted therapy,and the control group(n=60),including those who received chemotherapy and targeted therapy.Survival analysis was performed using the log-rank test,and univariate and multivariate Cox regression analyses were conducted to explore prognostic factors.This study was approved by the Ethics Committee of the Minhang Branch of Fudan University Shanghai Cancer Center[Approval number:(2024)Review No.(015)]and all informed consents were exempted.Results:The objective response rate(ORR)in the observation group was 72.9%,and the disease control rate(DCR)was 80.0%,which were significantly higher than those in the control group with an ORR of 20.0%(χ2=36.1,P＜0.001)and a DCR of 40.0%(χ2=21.8,P＜0.001).The median progression-free survival(PFS)in the observation group was not reached,significantly longer than that in the control group of 7.0 months(P＜0.001).Multivariate Cox regression analysis identified camrelizumab treatment as an independent protective factor for PFS(P＜0.001).Age,site of recurrence/metastasis,initial treatment approach,and histopathological type were not significantly associated with PFS.In the observation group,adverse events of grade 3 or higher were reported in 29 patients(41.4%),which primarily included vasculitis,hypothyroidism,hypersensitivity reactions,and diarrhea.Conclusion:The use of camrelizumab significantly improved treatment outcomes and prognosis for patients with recurrent,metastatic,and treatment-na?ve advanced cervical cancer,with significantly improved progression-free survival.Although a certain proportion of patients experienced adverse events of grade 3 or higher,the overall safety profile was acceptable.In clinical practice,immunotherapy offers a more effective treatment option for patients.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.