Diabetes mellitus, characterized by glucose metabolism disorders and marked by insulin deficiency or insulin resistance, has seen a continuous rise in prevalence. In recent years, islet transplantation has matured as a therapeutic approach for diabetes, becoming an important method for glycemic control and the reduction of diabetes-related complications. Donor selection directly influences transplant outcomes, and various research institutions worldwide have proposed multiple scoring systems to optimize donor assessment, such as the University of Alberta scoring system and the North American Islet Donor Score. This article explores the impact of key factors such as donor age, body mass index and ischemia time on islet transplantation. Combining practical experience in pancreatic donor selection from Shanghai Changzheng Hospital, it proposes screening criteria for pancreatic donors suitable for China, aiming to provide new evidence for improving the success rate of islet transplantation.

The incidence of delayed chemotherapy-induced nausea and vomiting is relatively high among pediatric cancer patients. Nausea and vomiting symptoms can exacerbate physical and psychological burdens, potentially leading to aversion and reduced treatment adherence. This paper analyzes and summarizes delayed chemotherapy-induced nausea and vomiting in pediatric cancer patients, covering overview, influencing factors, assessment tools, and non-pharmacological interventions, aiming to provide insights for clinical prevention and intervention strategies targeting delayed chemotherapy-induced nausea and vomiting in pediatric patients.

Objective To investigate the regulatory effect and mechanism of Yiqi Jiedu Decoction(YQJD)on ionizing radiation-induced macrophage polarization and its correlation with the Toll-like receptor 4(TLR4)/myeloid differentiation primary response protein 88(MyD88)/nuclear factor kappa-B(NF-κB)signaling pathway.Methods Fifty-five specific-pathogen-free male Sprague-Dawley rats were randomly divided into blank(n=30),anduolin(n=10),and YQJD groups(n=15).They were respectively gavaged with deionized water,anduolin suspension(0.345 6 g/kg),and YQJD high-dose(20.88 g/kg)at a dose of 0.01 mL/g body weight once a day for seven consecutive days.2 hours after the last gavage,blood was collected from the abdominal aorta to prepare the control rat,andolin rat,and YQJD high-dose sera.Appropriate amounts of YQJD high-dose and control sera were mixed in a ratio of 1∶1 and 1∶3,respectively,to obtain YQJD medium-and low-dose rat serum.RAW264.7 cells were divided into blank(10%blank rat serum),model(10%blank rat serum),anduolin(10%anduolin rat serum),and YQJD-L,YQJD-M,YQJD-H groups(10%YQJD low-,medium-,and high-dose rat serum).Except for the blank group,the cells in other groups were irradiated with 12 Gy60 Co γ-rays once to establish the macrophage radiation injury model.At 24 h after irradiation,cell viability was detected using the CCK-8 method,and the cell migration rate was measured using the scratch test.Cell morphology was observed using phalloidin staining,tumor necrosis factor-alpha(TNF-α)and interleukin-10(IL-10)levels in the cell supernatant were quantified using enzyme-linked immunosorbent assay,and the proportion of M1 macrophages was detected using flow cytometry.TLR4,MyD88,and NF-κB protein expression were detected using Western blotting.Results Twenty-four hours after irradiation,compared with the blank group,the model group exhibited significantly reduced cell viability and migration rate(P<0.01),increased cell volume and pseudopodia formation,elevated TNF-α and IL-10 levels,an increased proportion of M1 macrophages,and upregulated TLR4,MyD88,and NF-κB protein expression(P<0.05,P<0.01).Compared with the model group,each drug-treated group showed improved cell viability and migration rate(P<0.05,P<0.01),decreased cell volume,more regular cell shape,reduced TNF-α levels,lower M1-type macrophage proportion,and downregulated TLR4,MyD88,and NF-κB protein expression(P<0.05,P<0.01).IL-10 level showed an upward trend.Conclusion YQJD can partially inhibit M1 macrophage polarization and suppress inflammatory responses,which may be related to the TLR4/MyD88/NF-κB signaling pathway.

The incidence of delayed chemotherapy-induced nausea and vomiting is relatively high among pediatric cancer patients. Nausea and vomiting symptoms can exacerbate physical and psychological burdens, potentially leading to aversion and reduced treatment adherence. This paper analyzes and summarizes delayed chemotherapy-induced nausea and vomiting in pediatric cancer patients, covering overview, influencing factors, assessment tools, and non-pharmacological interventions, aiming to provide insights for clinical prevention and intervention strategies targeting delayed chemotherapy-induced nausea and vomiting in pediatric patients.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that seriously affects the health of the elderly, and the early diagnosis is crucial to slow down the progression of the disease. This review systematically examines the integrative applications of multimodal digital technologies in early AD identification, encompassing cognitive assessment, neuroimaging analysis, biomarker detection, and polygenic risk prediction, with the goal of enhancing diagnostic accuracy and operational efficiency. It was found that artificial intelligence-driven digital tools significantly improved screening efficiency by capturing subtle behavioral patterns and physiological signatures. Machine learning algorithms integrated with multimodal neuroimaging data optimize sensitivity in detecting structural brain abnormalities, while combinatorial analysis of digital biomarkers enables high-precision staging of AD pathology. Recent advancements highlight the critical role of digital technologies in facilitating multimodal biomarker integration and streamlining diagnostic workflows. The convergence of these innovative approaches provides a robust framework for early AD screening, offering patients accessible and efficient diagnostic pathways.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that seriously affects the health of the elderly, and the early diagnosis is crucial to slow down the progression of the disease. This review systematically examines the integrative applications of multimodal digital technologies in early AD identification, encompassing cognitive assessment, neuroimaging analysis, biomarker detection, and polygenic risk prediction, with the goal of enhancing diagnostic accuracy and operational efficiency. It was found that artificial intelligence-driven digital tools significantly improved screening efficiency by capturing subtle behavioral patterns and physiological signatures. Machine learning algorithms integrated with multimodal neuroimaging data optimize sensitivity in detecting structural brain abnormalities, while combinatorial analysis of digital biomarkers enables high-precision staging of AD pathology. Recent advancements highlight the critical role of digital technologies in facilitating multimodal biomarker integration and streamlining diagnostic workflows. The convergence of these innovative approaches provides a robust framework for early AD screening, offering patients accessible and efficient diagnostic pathways.

Objective To investigate the regulatory effect and mechanism of Yiqi Jiedu Decoction(YQJD)on ionizing radiation-induced macrophage polarization and its correlation with the Toll-like receptor 4(TLR4)/myeloid differentiation primary response protein 88(MyD88)/nuclear factor kappa-B(NF-κB)signaling pathway.Methods Fifty-five specific-pathogen-free male Sprague-Dawley rats were randomly divided into blank(n=30),anduolin(n=10),and YQJD groups(n=15).They were respectively gavaged with deionized water,anduolin suspension(0.345 6 g/kg),and YQJD high-dose(20.88 g/kg)at a dose of 0.01 mL/g body weight once a day for seven consecutive days.2 hours after the last gavage,blood was collected from the abdominal aorta to prepare the control rat,andolin rat,and YQJD high-dose sera.Appropriate amounts of YQJD high-dose and control sera were mixed in a ratio of 1∶1 and 1∶3,respectively,to obtain YQJD medium-and low-dose rat serum.RAW264.7 cells were divided into blank(10%blank rat serum),model(10%blank rat serum),anduolin(10%anduolin rat serum),and YQJD-L,YQJD-M,YQJD-H groups(10%YQJD low-,medium-,and high-dose rat serum).Except for the blank group,the cells in other groups were irradiated with 12 Gy60 Co γ-rays once to establish the macrophage radiation injury model.At 24 h after irradiation,cell viability was detected using the CCK-8 method,and the cell migration rate was measured using the scratch test.Cell morphology was observed using phalloidin staining,tumor necrosis factor-alpha(TNF-α)and interleukin-10(IL-10)levels in the cell supernatant were quantified using enzyme-linked immunosorbent assay,and the proportion of M1 macrophages was detected using flow cytometry.TLR4,MyD88,and NF-κB protein expression were detected using Western blotting.Results Twenty-four hours after irradiation,compared with the blank group,the model group exhibited significantly reduced cell viability and migration rate(P<0.01),increased cell volume and pseudopodia formation,elevated TNF-α and IL-10 levels,an increased proportion of M1 macrophages,and upregulated TLR4,MyD88,and NF-κB protein expression(P<0.05,P<0.01).Compared with the model group,each drug-treated group showed improved cell viability and migration rate(P<0.05,P<0.01),decreased cell volume,more regular cell shape,reduced TNF-α levels,lower M1-type macrophage proportion,and downregulated TLR4,MyD88,and NF-κB protein expression(P<0.05,P<0.01).IL-10 level showed an upward trend.Conclusion YQJD can partially inhibit M1 macrophage polarization and suppress inflammatory responses,which may be related to the TLR4/MyD88/NF-κB signaling pathway.

ObjectiveIn order to improve the recognition accuracy of named entities in medical record texts and realize the effective mining and utilization of medical record knowledge， a Bert-Radical-Lexicon（BRL） neural network model is constructed to recognize medical record entities with respect to the characteristics of medical record texts. MethodWe selected 408 medical records related to hypertension from the the Complete Library of Famous Medical Records of Chinese Dynasties and constructed a dataset consisting of 1 672 medical records by manually labeling. Then， we randomly divided the dataset into three subsets， including the training set（1 004 cases）， the testing set （334 cases） and the validation set（334 cases）. Based on this dataset， we built a BRL model that fused various text features of medical records， as well as its variants BRL-B， BRL-L and BRL-R， and a baseline model Base for experiments. During the model training phase， we trained the above models using the training set to reduce the risk of overfitting. We continuously monitored the performance of each model on the validation set during training and saved the model with the best performance. Finally， we evaluated the performance of these models on the testing set. ResultCompared with other models， the BRL model had the best performance in the medical records named entity recognition task， with an overall recognition precision of 90.09%， a recall of 90.61%， and the harmonic mean of the precision and recall（F1） of 90.35% for eight types of entities， including disease， symptom， tongue manifestation， pulse condition， syndrome， method of treatment， prescription and traditional Chinese medicine（TCM）. Compared with the Base model， the BRL model improved the overall F1 value of entity recognition by 5.22%， and the F1 value of pulse condition entity increased by 6.92%， which was the largest increase. ConclusionBy incorporating a variety of medical record text features in the embedding layer， the BRL neural network model has stronger named entity recognition ability， and thus extracts more accurate and reliable TCM clinical information.

With persistent breakthrough and maturity of surgical procedures and postoperative immunosuppressive therapy, the survival rate of liver transplant recipients and grafts has been significantly increased. The shortage of donor liver has become the main obstacle for clinical development of liver transplantation. How to expand the source of donor liver has become an urgent issue. Groundbreaking progresses have been made in the use of common marginal donor livers in clinical liver transplantation, such as elderly donor liver, steatosis donor liver, viral hepatitis donor liver and liver from donation after cardiac death. Nevertheless, multiple restrictions still exist regarding the use of marginal donor liver. Consequently, the definition of marginal donor liver and research progress in the application of common marginal donor livers were reviewed, and the opportunities and challenges of mariginal donoor liver were illustrated, aiming to provide reference for expanding the donor pool for clinical liver transplantation and bringing benefits to more patients with end-stage liver disease.