Mass spectrometry imaging （MSI） is an emerging molecular imaging technique that combines high spatial resolution and the ability to acquire molecular information. This technology can directly obtain spatial distribution maps of metabolites, proteins, lipids and other molecules in tumor tissues, reveal the abnormal molecular changes during the occurrence and development of liver cancer, and make up for the deficiency of information at the molecular level in traditional imaging （such as CT, MRI）. In liver cancer research, MSI has demonstrated unique advantages and application potential in early diagnosis and screening, precise classification, tumor heterogeneity analysis, and exploration of molecular mechanisms. The principle characteristics of MSI technology and its latest progress in the early diagnosis, classification and molecular mechanism research of liver cancer were reviewed, and the current challenges and future development directions were discussed, which aimed to provide a new perspective and theoretical basis for an in-depth understanding of the pathological mechanism of liver cancer and promoted its precise diagnosis and treatment.

The National Institute on Aging-Alzheimer's Association(NIA-AA)released the"Revised criteria for diagnosis and staging of Alzheimer's disease"in 2024,representing a significant update following the 2011 and 2018 frameworks.This new standard aims to advance Alzheimer's disease diagnosis and treatment from a"clinical-pathological"model to a"precision biology"era,improving early diagnosis rates and guiding precise therapeutic interventions.Nuclear medicine molecular imaging plays an irreplaceable role in Alzheimer's disease diagnosis and staging,with key updates in the 2024 standard being the replacement of traditional clinical diagnosis with biological definitions and the hierarchical classification of biomarkers.This article focuses on interpreting the core positioning and clinical application value of multimodal imaging technologies within the new standard framework,particularly the critical roles of amyloid β-protein PET and tau PET in early diagnosis,biological staging and treatment monitoring of Alzheimer's disease,aiming to provide guidance for clinical practice.

In 2025,the Response Assessment in Neuro-Oncology(RANO)working group and the European Association for Neuro-Oncology jointly released the updated guidelines for the clinical use of PET imaging in gliomas.Building upon the methodological framework established in the 2024 PET RANO 1.0 criteria,the new guidelines systematically integrate clinical evidence for amino acid PET and achieve alignment with MRI RANO 2.0 in imaging assessment logic,marking a paradigm shift of amino acid PET from a research tool to a clinical decision-making tool.This article provides a comprehensive analysis of key updates in the guidelines and evaluates the application value and recommendation levels of amino acid PET across critical clinical scenarios.Furthermore,it emphasizes the advantages of integrated PET/MR imaging in multimodal information integration for glioma management.

In 2025,the Response Assessment in Neuro-Oncology(RANO)working group and the European Association for Neuro-Oncology jointly released the updated guidelines for the clinical use of PET imaging in gliomas.Building upon the methodological framework established in the 2024 PET RANO 1.0 criteria,the new guidelines systematically integrate clinical evidence for amino acid PET and achieve alignment with MRI RANO 2.0 in imaging assessment logic,marking a paradigm shift of amino acid PET from a research tool to a clinical decision-making tool.This article provides a comprehensive analysis of key updates in the guidelines and evaluates the application value and recommendation levels of amino acid PET across critical clinical scenarios.Furthermore,it emphasizes the advantages of integrated PET/MR imaging in multimodal information integration for glioma management.

Objective:To establish a highly sensitive indirect competitive ELISA(icELISA)method for detecting ribavirin in chicken.Methods:Based on the obtained monoclonal antibodies against ribavirin,a chessboard test was employed to determine the optimal working concentration of artificial antigen and antibody,and then established an icELISA method.Furthermore,performance of the detection method was evaluated.Results:The established icELISA method has a linear range of 0.44～32.71 ng/ml,IC50 of which was 3.78 ng/ml,and the limit of detection(LOD)was 0.20 ng/ml.Except for specific reaction with ribavirin,there were no cross reactions with other antiviral drugs.Recovery rate of sample spiking was between 91.60%and 100.76%,and coefficient of variation was between 7.29%and 10.63%.Conclusion:A highly sensitive and specific icELISA method for detection of ribavirin has been estab-lished,which can be used to determine the residue of ribavirin in chicken.

Objective To investigate the potential value of a deep learning(DL)model based on non-contrast CT images in predicting contrast medium extravasation in contrast-enhanced CT scans of tumor patients.Methods A total of 298 tumor patients were retrospectively selected,including 90 patients with extravasation and 208 without extravasation,and divided into training set(207 patients),validation set(46 patients),and external test set(45 patients)in a ratio of 7︰1.5︰1.5.U-Net was employed to segment the right common carotid artery/internal jugular vein and right subclavian artery/vein in non-contrast CT images,and ResNet50 was utilized to extract imaging features to construct the DL model,which was subsequently integrated with independent clinical predictors to establish the combined model.The segmentation performance of the DL model was evaluated using Dice similarity coefficient(DSC)and Intersection over Union(IoU),while the area under the curve(AUC),accuracy,sensitivity,and specificity of the model were calculated.Results The DL model demonstrated superior vascular segmentation(DSC 0.81-0.95,IoU 0.79-0.90).The combined model achieved optimal predictive performance,with AUC of 0.961[95%confidence interval(CI)0.924-0.983],0.949(95%CI 0.840-0.992),and 0.891(95%CI 0.762-0.964)in the training,validation,and external test sets,respectively.Its accuracy,sensitivity,and specificity were consistently higher than those of the standalone clinical model.Conclusion The DL model based on non-contrast CT images shows significant potential value in predicting contrast medium extravasation risk in tumor patients,providing an objective and intelligent tool for clinical risk assessment.

Objective:To investigate the differential responses of the endoplasmic reticulum stress-to-apoptosis cascade induced by proton ultra-high dose rate (FLASH) irradiation between lung epithelial and lung cancer cells.Methods:Human lung epithelial cells (KT) and lung adenocarcinoma cells (A549) were irradiated with protons, and divided into Ctrl, CONV and FLASH groups. Survival curves were generated using colony formation assay. Protein and mRNA expressions of the endoplasmic reticulum (ER) stress and apoptosis regulators were assessed via Western blot and RT-qPCR. The concentration of IL-6 secreted into the culture supernatant was determined by enzyme-linked immunosorbent assay(ELISA).Results:In KT cells, compared to the CONV group, FLASH irradiation resulted in a significantly higher survival fraction ( P<0.05), increased GRP78 protein expression ( t= 7.52, P < 0.05) and UPR-related genes PERK, ATF4, and CHOP. In A549, the cell survival rate did not differ significantly between the CONV and FLASH groups ( P > 0.05). UPR pathway was not activated in either group. However, both CONV and FLASH irradiation significantly promoted secretion of IL-6 ( t=4.31, 4.47, P<0.05), while no difference was identified between two groups. In KT, both irradiation promoted secretion of IL-6 ( t=7.43, 3.07, P<0.05) while IL-6 concentration in FLASH group was significantly lower than that in CONV group ( t=7.63, P<0.05). Additionally, a pro-apoptotic propensity in KT cells following FLASH irradiation and in A549 cells following both FLASH and CONV irradiation was identified. Conclusions:In KT cells, FLASH irradiation cleared misfolded proteins through activating UPR pathway, promoted apoptosis of damaged cells, suppressed IL-6 secretion to attenuate inflammatory injury, and ultimately enhanced cell survival. Furthermore, proton FLASH irradiation bypasses ER stress activation in A549 cells, instead directly priming an apoptotic disposition with concomitant IL-6 hypersecretion. This paracrine damage amplification cascade potentiates radiation-induced tumoricidal efficacy through sustained cytotoxic microenvironment remodeling.

Objective:To establish a highly sensitive indirect competitive ELISA(icELISA)method for detecting ribavirin in chicken.Methods:Based on the obtained monoclonal antibodies against ribavirin,a chessboard test was employed to determine the optimal working concentration of artificial antigen and antibody,and then established an icELISA method.Furthermore,performance of the detection method was evaluated.Results:The established icELISA method has a linear range of 0.44～32.71 ng/ml,IC50 of which was 3.78 ng/ml,and the limit of detection(LOD)was 0.20 ng/ml.Except for specific reaction with ribavirin,there were no cross reactions with other antiviral drugs.Recovery rate of sample spiking was between 91.60%and 100.76%,and coefficient of variation was between 7.29%and 10.63%.Conclusion:A highly sensitive and specific icELISA method for detection of ribavirin has been estab-lished,which can be used to determine the residue of ribavirin in chicken.

Objective To investigate the potential value of a deep learning(DL)model based on non-contrast CT images in predicting contrast medium extravasation in contrast-enhanced CT scans of tumor patients.Methods A total of 298 tumor patients were retrospectively selected,including 90 patients with extravasation and 208 without extravasation,and divided into training set(207 patients),validation set(46 patients),and external test set(45 patients)in a ratio of 7︰1.5︰1.5.U-Net was employed to segment the right common carotid artery/internal jugular vein and right subclavian artery/vein in non-contrast CT images,and ResNet50 was utilized to extract imaging features to construct the DL model,which was subsequently integrated with independent clinical predictors to establish the combined model.The segmentation performance of the DL model was evaluated using Dice similarity coefficient(DSC)and Intersection over Union(IoU),while the area under the curve(AUC),accuracy,sensitivity,and specificity of the model were calculated.Results The DL model demonstrated superior vascular segmentation(DSC 0.81-0.95,IoU 0.79-0.90).The combined model achieved optimal predictive performance,with AUC of 0.961[95%confidence interval(CI)0.924-0.983],0.949(95%CI 0.840-0.992),and 0.891(95%CI 0.762-0.964)in the training,validation,and external test sets,respectively.Its accuracy,sensitivity,and specificity were consistently higher than those of the standalone clinical model.Conclusion The DL model based on non-contrast CT images shows significant potential value in predicting contrast medium extravasation risk in tumor patients,providing an objective and intelligent tool for clinical risk assessment.

Surface plasmon resonance （SPR） sensor is an optical detection technique enables real-time and dynamic monitoring of biological samples. SPR-based biosensors have remarkable characteristics such as label-free detection and high sensitivity, making them important tools for studying molecular interactions. The recognition element, which plays a critical role in SPR sensors,which could specifically identify and capture of target analytes, closely influencing the selectivity performance of the sensor. The progress on SPR sensors in pharmaceutical research were reviewed, which focused on the application of recognition elements such as antibodies, aptamers, molecularly imprinted polymers, and metal nanoparticles.