Driven by the internal pressure of the traditional elderly care model and the external impetus of the new technological revolution， the intelligent elderly care model is emerging. However， the rapid technological development has also triggered a series of ethical controversies. Existing ethical rules struggle to interpret， adapt to， or regulate the risks of ethical norms being out of control， alienated ethical behaviors， and unclear ethical responsibility that have emerged in the intelligent elderly care model. Specifically， these risks manifest in three aspects， including infringement of the rights and interests of the elderly， imbalance in social fairness and justice， and difficulties in defining responsibility. The analysis revealed that the generative factors of ethical risks include both internal system factors and external environmental factors. To prevent and control the ethical risks of the intelligent elderly care model， efforts should focus on three levels. At the technical level， it was necessary to promote the development of responsible intelligent elderly care technologies. In terms of regulatory level， it was essential to innovate the rules and methods of responsibility allocation. Regarding cognitive level， it was vital to strengthen users’ awareness and response capacity to ethical risks. These approaches will continuously promote the upward and virtuous development of technology and foster harmonious coexistence between humans and machines.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

Lipid turbidity disease is a metabolic disease featuring lipid metabolism disorders caused by many factors such as social environment， diet， and lifestyle， which is closely related to many diseases in modern medicine， such as hyperlipidemia， obesity， fatty liver， atherosclerosis， metabolic syndrome， and cardiovascular and cerebrovascular diseases， with a wide range of influence and far-reaching harm. According to the Huangdi Neijing， lipid turbidity disease reflects the pathological change of the body's physiologic grease. Grease is the thick part of body fluids， which has the function of nourishing， and it is the initial state and source of important substances in the human body such as brain， marrow， essence， and blood. Once the grease of the human body is abnormal， it can lead to lipid turbidity disease. The Huangdi Neijing also points out the physiological relationship between the transportation and transformation of body fluids and the rise and fall of the spleen and stomach， which can deduce the pathological relationship between the occurrence of lipid turbidity disease and the abnormal rise and fall of the spleen and stomach functions. Lipid turbidity disease is caused by overconsumption of fatty and sweet foods or insufficient spleen and stomach endowments， leading to disorders of the function of promoting clear and reducing turbidity in the spleen and stomach. This leads to the transformation of thick grease in body fluids into lipid turbidity， which accumulates in the body's meridians， blood vessels， skin pores， and organs， forming various forms of metabolic diseases. The research team believed that the pathological basis of lipid turbidity disease was the abnormal rise and fall of the spleen and stomach and the obstruction of the transfer of grease. According to the different locations where lipid turbidity stays， it was divided into four common pathogenesis types： ''inability to distinguish between the clear and turbid， turbid stagnation in the Ying blood''， ''spleen not rising clear， turbid accumulation in the vessels''， ''spleen dysfunction， lipid retention in the pores''， ''spleen failure to transportation and transformation， and grease accumulation in the liver''. According to the pathogenesis， it could be divided into four common syndromes， namely， turbid stagnation in the Ying blood， turbid accumulation in the vessels， lipid retention in the pores， and grease accumulation in the liver， and the corresponding prescriptions were given for syndrome differentiation and treatment， so as to guide clinical differentiation and treatment of the lipid turbidity disease.

G protein-coupled receptors(GPCRs)are the largest family of membrane proteins in eukaryotes,with nearly 800 genes coding for these proteins.They are involved in many physiological processes,such as light perception,taste and smell,neurotransmitter,metabolism,endocrine and exocrine,cell growth and migration.Importantly,GPCRs and their ligands are the targets of approximately one third of all mar-keted drugs.GPCRs are traditionally known for their role in transmitting signals from the extracellular environment to the cell's interior via the plasma membrane.However,emerging evidence suggests that GPCRs are also localized on mitochondria,where they play critical roles in modulating mitochondrial functions.These mitochondrial GPCRs(mGPCRs)can influence processes such as mitochondrial respi-ration,apoptosis,and reactive oxygen species(ROS)production.By interacting with mitochondrial signaling pathways,mGPCRs contribute to the regulation of energy metabolism and cell survival.Their presence on mitochondria adds a new layer of complexity to the understanding of cellular signaling,highlighting the organelle's role as not just an energy powerhouse but also a crucial hub for signal transduction.This expanding understanding of mGPCR function on mitochondria opens new avenues for research,particularly in the context of diseases where mitochondrial dysfunction plays a key role.Ab-normalities in the phase conductance pathway of GPCRs located on mitochondria are closely associated with the development of systemic diseases such as cardiovascular disease,diabetes,obesity and Alz-heimer's disease.In this review,we examined the various types of GPCRs identified on mitochondrial membranes and analyzed the complex relationships between mGPCRs and the pathogenesis of various diseases.We aim to provide a clearer understanding of the emerging significance of mGPCRs in health and disease,and to underscore their potential as therapeutic targets in the treatment of these conditions.

Protozoan infections(e.g.,malaria,trypanosomiasis,and toxoplasmosis)pose a considerable global burden on public health and socioeconomic problems,leading to high rates of morbidity and mortality.Due to the limited arsenal of effective drugs for these diseases,which are associated with devastating side effects and escalating drug resistance,there is an urgent need for innovative antiprotozoal drugs.The emergence of drug repurposing offers a low-cost approach to discovering new therapies for pro-tozoan diseases.In this review,we summarize recent advances in drug repurposing for various human protozoan diseases and explore cost-effective strategies to identify viable new treatments.We highlight the cross-applicability of repurposed drugs across diverse diseases and harness common chemical motifs to provide new insights into drug design,facilitating the discovery of new antiprotozoal drugs.Chal-lenges and opportunities in the field are discussed,delineating novel directions for ongoing and future research.

Objective To investigate the correlation between serum levels of macrophage inflammatory protein-1α(MIP-1α),apolipoprotein C1(APOC1),cysteine leukotrienes(CysLTs)and prognosis in children with severe Mycoplasma pneumoniae pneumonia.Methods The children with severe M.pneumoniae pneumonia admitted to the Second Hospital of Handan City from January 2022 to December 2023 were included as case group.The children in case group were assigned to a good prognosis group(n=69)or poor prognosis group(n=24)according to patient outcome.Additionally,93 healthy children who underwent physical examination were included as control group.Enzyme linked immunosorbent assay(ELISA)was applied to determine the serum levels of MIP-1α,APOC1,and CysLTs.Pearson correlation was used to analyze the correlation between serum levels of MIP-1α,APOC1,CysLTs and other biomarkers.Receiver operating characteristic(ROC)curve was plotted to analyze the value of MIP-1α,APOC1,and CysLTs levels for predicting the outcome of children with severe M.pneumoniae pneumonia.Results The serum levels of MIP-1α,APOC1,and CysLTs in case group were higher than those in the control group(P＜0.05).The serum levels of MIP-1α,APOC1,and CysLTs were higher in poor prognosis group compared with good prognosis group(P＜0.05).The levels of MIP-1α,APOC1,and CysLTs in the serum of case group were positively correlated with platelet count,mean platelet volume,white blood cell,neutrophil count,lactate dehydrogenase,erythrocyte sedimentation rate,C-reactive protein,and D-dimer(P＜0.05),and negatively correlated with AT-Ⅲ(P＜0.05).ROC analyses showed that the area under the curve(AUC)of MIP-1α,APOC1,and CysLTs combined was 0.881 in predicting prognosis,significantly higher than MIP-1α(Z=2.096,P=0.036),APOC1(Z=2.236,P=0.025),and CysLTs(Z=2.058,P=0.040)alone,with a sensitivity of 70.80％and specificity of 89.90％.Conclusions The levels of serum MIP-1α,APOC1,and CysLTs are elevated in children with severe M.pneumoniae pneumonia.Serum MIP-1α,APOC1,and CysLTs combined can provide higher value for predicting the prognosis of children with severe M.pneumoniae pneumonia.

Objective To explore the biological characteristics associated with different subtypes and the response to immunotherapy by integrating multiomics analysis and artificial neural networks(ANN)to delineate the precise molecular subtypes of adrenocortical carcinoma(ACC)and establish a prognostic prediction model,in order to provide reference for the accurate prognosis assessment and individualized treatment of ACC.Methods The multiomics data of 44 Chinese ACC patients admitted to the Department of Urology,West China Hospital of Sichuan University during Jan.1,2012 and Dec.31,2022 were integrated,including genomic,transcriptomic and clinical features.Ten different clustering algorithms were employed for consensus clustering to identify robust molecular subtypes.The results were then incorporated into an ANN model to construct an ANN-driven prognostic index(ANPI)for patient stratification and survival prediction.Results Three distinct molecular subtypes(cancer subtypes,CS1-3)with significantly different prognoses were identified,among which CS1 exhibited the poorest survival outcomes.A set of 20 core genes was selected to form the basis of the ANPI model.ANPI effectively stratified patients into high-and low-risk groups:patients in the low-ANPI group had significantly better overall survival and exhibited"hot tumor"immune phenotypes,suggesting greater benefits from immunotherapy.In contrast,high-ANPI patients had worse prognoses and displayed"cold tumor"characteristics with weaker immunotherapy responses.Conclusion Our integrative multiomics analysis illustrated the molecular landscape of ACC in the Chinese population and uncovered the key immune-related features linked to clinical outcomes.The ANPI model demonstrated strong performance in prognostic prediction and immunotherapy response assessment,offering a valuable tool for precision oncology and clinical decision-making.

BACKGROUND:D1-3-n-butylphthalide has antioxidant and anti-inflammatory effects and has been explored to have protective role in Parkinson's disease,but the underlying mechanisms are unknown.OBJECTIVE:To investigate the protective effect of D1-3-n-butylphthalide by the approach of network pharmacology,molecular docking,and cellular experimental validation.METHODS:(1)Network pharmacology and molecular docking:The database was used to screen the targets of D1-3-n-butylphthalide and Parkinson's disease.The intersection was taken from the construction of the target protein interaction network,and then screen the core targets.The GO and KEGG pathway enrichment was used to further analyze the core targets.The interaction between the target proteins and D1-3-n-butylphthalide was verified by molecular docking.(2)Cell validation:The passage 6 PC12 cells were divided into six groups for culture.The control group was cultured with conventional culture medium.The model group was cultured with N-methyl-4-phenylpyridinium iodide to induce Parkinson's disease model.The ML385 inhibitor group was added with nuclear factor E2-related factor 2 inhibitor ML385 on the basis of inducing Parkinson's disease model.The D1-3-n-butylphthalide treatment group was added with butylphthalide on the basis of inducing Parkinson's disease model.The D1-3-n-butylphthalide combined with ML385 treatment group was added with D1-3-n-butylphthalide and ML385 on the basis of inducing Parkinson's disease model.The D1-3-n-butylphthalide group was cultured with conventional culture medium containing butylphthalide alone.Cell proliferation,intracellular reduced glutathione and malondialdehyde levels,and protein expression of protein kinase B/glycogen synthase kinase 3β/nuclear factor E2-related factor 2(AKT/GSK-3β/Nrf2)signaling pathway were detected.RESULTS AND CONCLUSION:(1)A total of 52 targets were screened for the intersection of drugs and disease targets,and the core targets including the matrix metalloproteinase 9 and GSK-3β were involved the phosphatidylinositol 3-kinase(PI3K)/AKT and oxidative stress-related signaling pathways.The molecular docking binding energy of D1-3-n-butylphthalide and GSK-3β was-18.27 kJ/mol,which indicated that D1-3-n-butylphthalide had a good binding ability with GSK-3β.(2)Compared with the model group,the PC12 cell activity and reduced glutathione level in the D1-3-n-butylphthalide treatment group were increased(P＜0.05),the malondialdehyde level was decreased(P＜0.05),and the expression of p-AKT,p-GSK-3β,Nu-Nrf2,and T-Nrf2 proteins was increased(P＜0.05).Compared with the D1-3-n-butylphthalide group,the PC12 cell activity and reduced glutathione level in the D1-3-n-butylphthalide combined with ML385 treatment group were decreased(P＜0.05),the malondialdehyde level was increased(P＜0.05),and the expression of Nu-Nrf2 and T-Nrf2 proteins was decreased(P＜0.05).(3)These results demonstrate that D1-3-n-butylphthalide can inhibit oxidative stress and improve cell activity through the AKT/GSK-3β/Nrf2 signaling pathway,and has a protective effect on the Parkinson's cell model induced by N-methyl-4-phenylpyridinium iodide.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Vacuum compression molding (VCM) is a novel technology supporting the research and development of pharmaceutical solid dispersions. It is widely applied due to its precision and convenience in sample preparation. This technology integrates the principles of heating, melting, cooling, and vacuum compression to transform solid powders into shaped solids directly. By selecting different molds, temperatures, and pressures, researchers can prepare samples with diverse characteristics. This paper presents an overview of the equipment composition and working principles of VCM technology, demonstrating its distinct advantages in the formulation screening process of amorphous solid dispersions through comparative analysis with hot melt extrusion using case studies, and introduces its applications in the development of drug delivery systems and rheological characterization analysis, with a perspective on the future development of its functions.