OBJECTIVE To evaluate the cost-effectiveness of cefiderocol versus best available therapy （BAT） or standard-of- care （SOC） for the treatment of confirmed or suspected carbapenem-resistant Gram-negative bacterial （CRGNB） serious infections from the perspective of the Chinese healthcare system， and to explore its reasonable pricing. METHODS A decision tree model was constructed based on data from two phase Ⅲ clinical trials （CREDIBLE-CR and GAME CHANGER） to simulate the cost- effectiveness of cefiderocol in two scenarios： salvage therapy for confirmed CRGNB infection （scenario 1） and empirical therapy for suspected CRGNB infection （scenario 2）. The primary outcome measure was the incremental cost-effectiveness ratio （ICER）. The willingness-to-pay （WTP） was set at 1 to 3 times China’s per capita GDP in 2024. To verify the robustness of the results， one- way and probabilistic sensitivity analyses were conducted， and based on these， a reasonable price range for cefiderocol in the Chinese market was explored. RESULTS The results for scenario 1 showed that the clinical cure rate in the cefiderocol group was higher than that in the BAT group （47.50% vs. 34.21%）， but its ICER was 415 065.03 yuan per cured case， exceeding three times China’s GDP per capita. Scenario 2 revealed that the ICER for cefiderocol relative to SOC was as high as 1 362 446.16 yuan per cured case， far exceeding the WTP. Sensitivity analysis indicated that the treatment duration and price of cefiderocol were key factors affecting its cost-effectiveness. In the two scenarios described above， the unit price of cefiderocol must fall below 683.47 and 242.00 yuan/g， respectively， to be considered cost-effective. CONCLUSIONS Based on the current market price， cefiderocol lacks sufficient cost-effectiveness for treating confirmed or suspected CRGNB serious infections within China’s healthcare system. To improve its accessibility， price negotiations or a tiered medical insurance payment strategy are required.

ObjectiveTo explore the value of a multimodal MRI-based radiomics nomogram for differentiating human epidermal growth factor receptor-2 （HER-2） negative breast cancer molecular subtypes.MethodsA retrospective analysis was conducted on 190 patients with HER-2 negative breast cancer who underwent multimodal MRI examination， and the patients were divided into two molecular subtype groups： a HER-2 low expression group （n=108） and a HER-2 zero expression group （n=82）. The cases were randomly stratified and sampled at a ratio of 7∶3 and divided into a training set of 133 cases and a testing set of 57 cases. The clinical and radiological features of the patients were collected， the radiomics features based on T2-weighted imaging （T2WI）， diffusion-weighted imaging （DWI）， and dynamic contrast-enhanced （DCE）-MRI were extracted， and the clinical-radiological model， unimodal radiomics model， multimodal radiomics model， and combined model were constructed respectively. Then the nomogram combined multimodal radiomics signature （radsocre） with clinical-radiological features was used to construct a visualized predictive model， and the area under the curve （AUC） was used to compare the effectiveness of different models in distinguishing HER-2 low expression and zero expression subtypes.ResultsA significant difference in radscore was demonstrated between the HER-2 low and HER-2 zero expression groups in both the training （P<0.000 1） and testing sets （P<0.01）. The AUC of the multimodal radiomics model in the training set and the testing set were 0.914 and 0.836， respectively， which was superior to any unimodal radiomics model. The nomogram demonstrated great diagnostic efficacy （AUC=0.930 in training set； AUC=0.865 in testing set）.ConclusionA multimodal MRI-based nomogram incorporating radsocre and clinical-radiological features can accurately distinguish the subtypes of HER-2 negative breast cancer.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Objective To explore the nutritional status in elderly patients with type 2 diabetes mellitus (T2DM) and analyze its relationship with the risk of sarcopenia. Methods A retrospective analysis was conducted on 312 elderly patients with T2DM admitted to the 904th Hospital of the Joint Logistics Support Force of the People's Liberation Army between January 2023 and December 2025. Based on the presence or absence of sarcopenia, the patients were classified into a sarcopenia group (121 cases) and a non-sarcopenia group (191 cases). The nutritional status and related factors were compared between the two groups. Binary logistic regression analysis was adopted to analyze the correlation and influencing factors. Results The incidence rate of sarcopenia among elderly patients with T2DM was 38.78%. Compared with the non-sarcopenia group, the sarcopenia group had older age and higher proportions of patients with daily activity dependence, coronary heart disease, and gastrointestinal diseases. Conversely, the proportions of patients exercising ≥4 times per week and those paying attention to nutritional knowledge were lower in the sarcopenia group than those in the non-sarcopenia group (P<0.05). The body mass index (BMI), calf circumference (CC), body weight, albumin (ALB), hemoglobin (Hb), and MNA-SF score in the sarcopenia group were lower compared with those in the non-sarcopenia group (P<0.05). After logistic regression analysis, it was found that low BMI, exercising ≥4 times per week, dependence on daily activity, and low ALB and Hb were independent factors influencing sarcopenia in elderly patients with T2DM (P<0.05). Conclusion The incidence rate of sarcopenia is relatively high in elderly patients with T2DM. Low BMI, low ALB, low Hb, reduced weekly exercise frequency, and daily activity dependence are independent related factors. It is necessary to pay clinical attention to nutritional assessment and intervention.

Objective To investigate the independent risk factors affecting the prognosis of chronic active antibody-mediated rejection (caAMR) after kidney transplantation. Methods A retrospective analysis was conducted on 61 patients who underwent renal biopsy and were diagnosed with caAMR. The patients were divided into caAMR group (n=41) and caAMR+TCMR group (n=20) based on the presence or absence of concurrent acute T cell-mediated rejection (TCMR). The patients were followed up for 3 years. The value of 24-hour urinary protein and estimated glomerular filtration rate (eGFR) at the time of biopsy in predicting graft loss was assessed using receiver operating characteristic (ROC) curves. The independent risk factors affecting caAMR prognosis were analyzed using the LASSO-Cox regression model. The correlation between grouping, outcomes, and Banff scores was compared using Spearman rank correlation matrix analysis. Kaplan-Meier analysis was used to evaluate the renal allograft survival rates of each subgroup. Results The 3-year renal allograft survival rates for the caAMR group and the caAMR+TCMR group were 83% and 79%, respectively. The area under the ROC curve (AUC) for predicting 3-year renal allograft loss was 0.83 ［95% confidence interval (CI) 0.70-0.97］ for eGFR and 0.78 (95% CI 0.61-0.96) for 24-hour urinary protein at the time of biopsy. LASSO-Cox regression analysis and Kaplan-Meier analysis showed that eGFR≤25.23 mL/(min·1.73 m²) and the presence of donor-specific antibody (DSA) against human leukocyte antigen (HLA) class I might be independent risk factors affecting renal allograft prognosis, with hazard ratios of 7.67 (95% CI 2.18-27.02) and 5.13 (95% CI 1.33-19.80), respectively. A strong correlation was found between the Banff chronic lesion indicators of renal interstitial fibrosis and tubular atrophy (P<0.05). Conclusions The presence of HLA class I DSA and eGFR≤25.23 mL/(min·1.73 m²) at the time of biopsy may be independent risk factors affecting the prognosis of caAMR.

In recent years, with the development of big data application technology, the real-world data and the corresponding generated real-world evidence have attracted the attention of healthcare regulatory authorities around the world. Regulators recognize that real-world research with specific purposes using real-world data can provide important evidence for regulatory decisions. A total of 90 instances of publicly released on the application of real-world evidence to support regulatory decisions of U. S. Food and Drug Administration are explored, and the positioning and value of real-world evidence in U. S. Food and Drug Administration regulatory decisions are summarized and analyzed, providing references for the use of real-world data and real-world evidence to promote medical devices whole cycle regulation in China.
United States ; United States Food and Drug Administration ; Equipment and Supplies ; Device Approval ; China

Protein arginine methyltransferase 5 (PRMT5) acts as an oncogene in liver cancer, yet its roles and in-depth molecular mechanisms within the liver cancer immune microenvironment remain mostly undefined. Here, we demonstrated that disruption of tumor-intrinsic PRMT5 enhances CD8⁺ T-cell-mediated antitumor immunity both in vivo and in vitro. Further experiments verified that this effect is achieved through downregulation of the inhibitory immune checkpoint molecule, fibrinogen-like protein 1 (FGL1). Mechanistically, PRMT5 catalyzed symmetric dimethylation of transcription factor 12 (TCF12) at arginine 554 (R554), prompting the binding of TCF12 to FGL1 promoter region, which transcriptionally activated FGL1 in tumor cells. Methylation deficiency at TCF12-R554 residue downregulated FGL1 expression, which promoted CD8⁺ T-cell-mediated antitumor immunity. Notably, combining the PRMT5 methyltransferase inhibitor GSK591 with PD-L1 blockade efficiently inhibited liver cancer growth and improved overall survival in mice. Collectively, our findings reveal the immunosuppressive role and mechanism of PRMT5 in liver cancer and highlight that targeting PRMT5 could boost checkpoint immunotherapy efficacy.

Proteolysis-targeting chimeras (PROTACs) have emerged as a promising therapeutic strategy for targeted protein degradation. However, the clinical application of PROTACs may be hindered by off-target toxicity resulting from non-tissue-specific protein degradation and ingenious prodrug strategies may open new avenues to addressing this concern. Herein, we propose a light-induced positive feedback strategy to use photodynamic therapy (PDT) to improve the activation efficiency of PROTAC prodrugs, monitor PROTAC release, and combine PROTAC to induce tumor cell apoptosis. In the hypoxic tumor microenvironment, the azo bond in AZO-PRO selectively cleaves, triggering the release of the potent protein degrader PRO and the multifunctional photosensitizer. Once activated, the fluoresce signal of the photosensitizer dramatically recovers, allowing monitoring of prodrug activation. Additionally, upon irradicating the tumor site using near-infrared (NIR) laser, PDT exacerbates tumor hypoxia, further promoting AZO-PRO activation. Our work introduces a novel approach to efficiently track and activate PROTAC prodrugs, enhance their antitumor efficacy, and mitigate off-target systemic toxicity.

Metabolic reprogramming plays a central role in tumors. However, the key drivers modulating reprogramming of gluconeogenesis/lipogenesis are poorly understood. Here, we try to identify the mechanism by which histone acetyltransferase 1 (HAT1) confers reprogramming of gluconeogenesis/lipogenesis in liver cancer. Diethylnitrosamine (DEN)/carbon tetrachloride (CCl₄)-induced hepatocarcinogenesis was hardly observed in HAT1-knockout mice. Multi-omics identified that HAT1 modulated gluconeogenesis and lipogenesis in liver. Protein phosphatase 2 scaffold subunit alpha (PPP2R1A) promoted gluconeogenesis and inhibited lipogenesis by phosphoenolpyruvate carboxykinase 1 (PCK1) serine 90 dephosphorylation to suppress the tumor growth. HAT1 succinylated PPP2R1A at lysine 541 (K541) to block the assembly of protein phosphatase 2A (PP2A) holoenzyme and interaction with PCK1, resulting in the depression of dephosphorylation of PCK1. HAT1-succinylated PPP2R1A contributed to the remodeling of gluconeogenesis/lipogenesis by PCK1 serine 90 phosphorylation, leading to the inhibition of gluconeogenic enzyme activity and activating sterol regulatory element-binding protein 1 (SREBP1) nuclear accumulation-induced lipogenesis gene expression, which enhanced the tumor growth. In conclusion, succinylation of PPP2R1A lysine 541 by HAT1 converses the role in modulation of gluconeogenesis/lipogenesis remodeling through PCK1 S90 phosphorylation to support liver cancer. Our finding provides new insights into the mechanism by which post-translational modifications (PTMs) confer the conversion of tumor suppressor function to oncogene.

The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.