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.

Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.

Digestive system tumors are the leading cause of cancer incidence and mortality globally， and their occurrence and development generally follow the key pathological process of inflammation-cancer transformation. Therefore， intervening in the precancerous lesion stage is an important strategy to block malignant transformation of the disease and reduce the incidence rate of tumors. Banxia Xiexin Tang-related prescriptions （including Banxia Xiexin Tang， Shengjiang Xiexin Tang， and Gancao Xiexin Tang） originated from the Shanghan Zabinglun They follow the principle of pungent dispersing and bitter descending， combination of cold and warm medicinals， and tonifying deficiency and purging excess， which aligns with the core pathogenesis of digestive system precancerous lesions characterized by cold-heat intermingling and disorder of ascending and descending. These prescriptions are widely used in the clinical treatment of various digestive system inflammatory disorders such as atrophic gastritis， ulcerative colitis， and reflux esophagitis. This review systematically summarizes the theoretical basis， clinical evidence， and therapeutic mechanisms of such prescriptions in preventing and treating the inflammation-cancer transformation process in the digestive system. Clinical studies have shown that whether used alone or in combination with modern therapies， Banxia Xiexin Tang can effectively alleviate symptoms， repair histopathological changes， and improve patients' quality of life. Basic research further reveals that their efficacy stems from multi-target systemic regulatory effects： ameliorating the chronic inflammatory microenvironment， antagonizing oxidative stress， reshaping the gut microbiota， restoring the immune balance， regulating cell proliferation and apoptosis， etc. On the basis of the convergence of traditional Chinese and Western medicine understanding of inflammation-cancer transformation， this article constructs a research framework centered on regulating the inflammatory microenvironment homeostasis to systematically elucidate the mechanism of treating different diseases with the same method （Banxia Xiexin Tang）. In view of the limitations of current research in terms of evidence level， disease-syndrome combination models， and overall mechanism analysis of compound prescriptions， this article proposes future research directions of integrating high-quality clinical research， systems biology， and cutting-edge technologies， providing a new theoretical basis and translational ideas for the precise prevention and control of digestive system tumors with traditional Chinese medicine.

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.

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.

Objective To develop the Children's Screen Interaction Quality Questionnaire(CSIQ)suit-able for measuring Chinese children aged 0 to 4 years,and to test its reliability and validity.Methods The purposive sampling method was used,and the guardians of 30 normal children aged 0 to 4 years undergoing physical examinations in the Department of Child Health Care of Guiyang Maternal and Child Health Care Hospital from February to April 2023 were selected as the interview objects.25 initial items were constructed through literature review,semi-structured interviews,and the Delphi expert consultation method.With the convenience sampling method,2 242 guardians of children aged 0 to 4 years old in the small and middle classes of 9 kindergartens in Guiyang City,Zunyi City,and Renhuai City were surveyed for item analysis,exploratory factor analysis,confirmatory factor analysis,and reliability and validity analysis.Results Exploratory factor a-nalysis extracted three factors,namely screen content interaction,reality interaction,and media interaction,with a total of 12 items.The cumulative variance explained rate of the 3-factor model was 69.829％.Confirma-tory factor analysis supported the three-factor model of CSIQ:x2/df=4.424,root mean square error of ap-proximation(RMSEA)=0.066,normed fit index(NFI)=0.955,comparative fit index(CFI)=0.965,incre-mental fit index(IFI)=0.965,Tucker-Lewis index(TLI)=0.955,goodness-of-fit index(GFI)=0.955,and the CSIQ had good convergent validity and discriminant validity.Conclusion The CSIQ has good reliability and validity.

Compared with matrix-assisted laser desorption ionization mass spectrometry(MALDI-MS)using organic small molecule matrix,surface-assisted laser desorption ionization mass spectrometry(SALDI-MS)based on nanomaterial matrix is more effective in analysis of small molecule compounds.Ion sputtering instruments have obvious advantages for applying inorganic nanomatrix.In this work,the carbon-platinum material was sputtered onto a glass cover slip using an ion sputtering instrument to form a carbon-platinum(C-Pt)composite nanomatrix,and an SALDI-MS analytical method was thus established based on the prefabricated C-Pt composite matrix.The experimental results showed that the C-Pt composite nanomatrix could significantly improve the signal intensity and signal-to-noise ratio of the mass spectrum peaks of the components to be measured.The ratio of carbon to platinum,the duration of ion sputtering,and the laser power in mass spectrometer were investigated to select the optimal C-Pt matrix prefabricated conditions and SALDI-MS experimental conditions.Using the prefabricated C-Pt composite matrix,the melittriose and daidzein sample solution were applied as sample to examine repeatability.The results showed that the intra-point repeatability(RSD)was≤4.8%and the inter-point repeatability(RSD)was≤6.4%.The quercetin and melitriose were applied as model samples,and a linearity between MS peak intensity and respective concentration in the range of 0.05-1.0 mg/mL was found,with linear correlation coefficients(R2)greater than 0.994,showing good potential for quantitative and imaging analysis.Then,the prefabricated C-Pt composite matrix was applied to SALDI-MS analysis of the 50%ethanol extract of soybean,and 15 kinds of compounds including oligosaccharides and triglycerides were identified from the mass spectra.Furthermore,the C-Pt matrix was employed in SALDI-MS imaging for the compositions in corn tissue section,and the results showed that diglycerides and triglycerides were mainly distributed in the corn embryo and around the embryo,and the distribution of oligosaccharides was relatively even.

Objective:To explore the clinical characteristics of neonatal necrotizing enterocolitis（NEC）and analyze the risk factors for early-onset NEC.Methods:A total of 220 children with NEC admitted to the Department of Pediatrics，Tianjin Medical University General Hospital from January 1st，2018 to February 29th，2024 were retrospectively selected as the research objects. According to the time of onset，the early-onset group（ n=120）and the late-onset group（ n=100）were established，and the clinical characteristics of the two groups were compared. Another 150 cases of normal healthy newborns born in this hospital in the same period were selected as the control group，and the clinical data of the control group were collected. The clinical characteristics of the early-onset group and the control group were compared，and the risk factors of early-onset NEC were analyzed by multivariate Logistic regression. Results:Compared with the late-onset group，the early-onset group had fever［50.0%（60/120）vs. 40%（40/100）， χ2=7.333， P=0.007］，apnea［39.17%（47/120）vs. 28%（28/100）， χ2=7.568， P=0.006］，no rise in body temperature［56.67%（68/120）vs. 39%（39/100）， χ2=6.815， P=0.009］，abdominal distension［25%（30/120）vs. 40%（40/100）， χ2=13.200， P<0.001］，vomiting［30.83%（37/120）vs. 45%（45/100）， χ2=12.797， P<0.001］was significantly different（all P<0.05）；Multivariate Logistic regression analysis：weight<1 500 g（ OR=5.871，95% CI：3.153～9.673， P<0.001），gestational age<30 weeks（ OR=4.256，95% CI：2.641～7.896， P=0.007），hemodynamically significant patent ductus arteriosus（hs-PDA）（ OR=3.113，95% CI：1.865～5.133， P=0.033），severe anemia（ OR=3.057，95% CI：2.165～4.802， P=0.001），feeding intolerance（ OR=4.215，95% CI：1.579～10.802， P=0.005），amniotic fluid pollution（ OR=2.452，95% CI：1.579～3.111， P<0.001）were the independent risk factors for early-onset NEC（all P<0.05）. Conclusion:Weight<1 500 g，gestational age<30 weeks，hs-PDA，severe anemia，feeding intolerance，and amniotic fluid contamination are independent risk factors for early-onset NEC. In clinical practice，more attention should be paid to these factors for disease prevention，early identification，and timely intervention in newborns to reduce the occurrence of NEC.