OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Early neurological deterioration(END)occurs in patients with acute cerebral infarction after recombinant tissue-plasminogen activator(rt-PA)intravenous thrombolysis,which seriously affects the prognosis of patients and even causes death.However,there is no effective treatment.This report describes two patients with acute ischemic stroke presenting with right-sided limb weakness within 4.5 hours of onset.Brain CT ruled out cerebral hemorrhage and both patients were diagnosed with acute ischemic stroke.END occurred within 24 hours after intravenous thrombolysis with rt-PA.Reexamine Brain CT showed no evidence of cerebral hemorrhage.The patients were treated with continuous tirofiban infusion for 48 hours,overlapping with oral antiplatelet therapy for 4 hours and 6 hours,respectively.After discontinuation of tirofiban,no further neurological deterioration was observed.At the 3-month follow-up,there was no recurrence,and the modified Rankin scale(mRS)scores were 1 and 3,respectively.This study delineates the diagnostic and therapeutic trajectories of two paradigmatic cases to inform clinical decision-making for analogous presentations,thereby contributing to evidence-based management strategies.

Objective To investigate the low-dose CT image denoising and generalization performance of the existing mainstream deep learning based denoising networks.Methods The public AAPM Mayo challenge dataset was used to train the denoising network using 3 image-domain methods(REDCNN,WGAN-VGG,CTformer)and 2 projection-image dual-domain methods(VVBP-UNet,CLEAR),separately.The denoising networks were evaluated quantitatively for peak signal-to-noise ratio(PSNR),structural similarity index,root mean square error,number of network parameters and floating point operations,and their generalization performance was analyzed on the AbdomenCT-1K Dataset.Results Image-domain denoising networks effectively suppressed low-dose CT image noise,with REDCNN demonstrating the best denoising performance and achieving a PSNR of 42.0988 dB.The dual-domain denoising networks were better at preserving tiny tissue structures while removing image noise,with VVBP-UNet performing the best and increasing PSNR to 42.150 9 dB.Conclusion The projection-image dual-domain method exhibits superior denoising and generalization performances than the image-domain method,despite requiring a relatively large amount of network parameters and computations.When computing resources are sufficient,the denoising results obtained by dual-domain method better fulfill the requirements for clinical diagnosis.

Early neurological deterioration(END)occurs in patients with acute cerebral infarction after recombinant tissue-plasminogen activator(rt-PA)intravenous thrombolysis,which seriously affects the prognosis of patients and even causes death.However,there is no effective treatment.This report describes two patients with acute ischemic stroke presenting with right-sided limb weakness within 4.5 hours of onset.Brain CT ruled out cerebral hemorrhage and both patients were diagnosed with acute ischemic stroke.END occurred within 24 hours after intravenous thrombolysis with rt-PA.Reexamine Brain CT showed no evidence of cerebral hemorrhage.The patients were treated with continuous tirofiban infusion for 48 hours,overlapping with oral antiplatelet therapy for 4 hours and 6 hours,respectively.After discontinuation of tirofiban,no further neurological deterioration was observed.At the 3-month follow-up,there was no recurrence,and the modified Rankin scale(mRS)scores were 1 and 3,respectively.This study delineates the diagnostic and therapeutic trajectories of two paradigmatic cases to inform clinical decision-making for analogous presentations,thereby contributing to evidence-based management strategies.

Objective To investigate the low-dose CT image denoising and generalization performance of the existing mainstream deep learning based denoising networks.Methods The public AAPM Mayo challenge dataset was used to train the denoising network using 3 image-domain methods(REDCNN,WGAN-VGG,CTformer)and 2 projection-image dual-domain methods(VVBP-UNet,CLEAR),separately.The denoising networks were evaluated quantitatively for peak signal-to-noise ratio(PSNR),structural similarity index,root mean square error,number of network parameters and floating point operations,and their generalization performance was analyzed on the AbdomenCT-1K Dataset.Results Image-domain denoising networks effectively suppressed low-dose CT image noise,with REDCNN demonstrating the best denoising performance and achieving a PSNR of 42.0988 dB.The dual-domain denoising networks were better at preserving tiny tissue structures while removing image noise,with VVBP-UNet performing the best and increasing PSNR to 42.150 9 dB.Conclusion The projection-image dual-domain method exhibits superior denoising and generalization performances than the image-domain method,despite requiring a relatively large amount of network parameters and computations.When computing resources are sufficient,the denoising results obtained by dual-domain method better fulfill the requirements for clinical diagnosis.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

Objective To evaluate the performance of reconstruction networks with different sparse views under the condition of keeping the same number of model parameters.Methods The number of network channels and network layers were adjusted to make the parameter quantity of each network similar when keeping the structure of each image-domain network and dual-domain network unchanged.The reconstruction performance of each network at different sparsity levels was compared.The AAPM Low-Dose CT Grand Challenge datasets were used in the experiment,including 10 976 images for training,979 images for validation,and 4 256 images for testing.The performance of each model was evaluated visually in combination with objective metrics such as peak signal-to-noise ratio,structural similarity and root mean square error.Results Before adjusting the model parameters,the hybrid domain network Tensor-Net obtained the best visual evaluation and objective evaluation metrics.After parament adjustment,with a similar number of parameters,Tensor-Net outperformed the other models at various projection angles in image anatomical detail recovery,but its structural similarity was slightly lower than that of RED-CNN.The parameters of the hybrid domain model Dual-FBPConvNet were all worse than those of FBPConvNet.Conclusion The hybrid domain model is advantageous in sparse-view CT reconstruction,but it faces more serious overfitting problems.Using a larger image domain model can achieve results similar to those of hybrid domain model.