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.

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.

Peptide-based radiopharmaceuticals targeting integrin α5β1 show promise for precise tumor diagnosis and treatment. However, current peptide-based radioligands that target α5β1 demonstrate inadequate in vivo performance owing to limited tumor retention. The use of PEGylation to enhance the tumor retention of radiopharmaceuticals by prolonging blood circulation time poses a risk of increased blood toxicity. Therefore, a PEGylation strategy that boosts tumor retention while minimizing blood circulation time is urgently needed. Here, we developed a PEGylation-enabled peptide multidisplay platform (PEGibody) for PR_b, an α5β1 targeting peptide. PEGibody generation involved PEGylation and self-assembly. [⁶⁴Cu]QM-2303 PEGibodies displayed spherical nanoparticles ranging from 100 to 200 nm in diameter. Compared with non-PEGylated radioligands, [⁶⁴Cu]QM-2303 demonstrated enhanced tumor retention time due to increased binding affinity and stability. Importantly, the biodistribution analysis confirmed rapid clearance of [⁶⁴Cu]QM-2303 from the bloodstream. Administration of a single dose of [¹⁷⁷Lu]QM-2303 led to robust antitumor efficacy. Furthermore, [⁶⁴Cu]/[¹⁷⁷Lu]QM-2303 exhibited low hematological and organ toxicity in both healthy and tumor-bearing mice. Therefore, this study presents a PEGibody-based radiotheranostic approach that enhances tumor retention time and provides long-lasting antitumor effects without prolonging blood circulation lifetime. The PEGibody-based radiopharmaceutical [⁶⁴Cu]/[¹⁷⁷Lu]QM-2303 shows great potential for positron emission tomography imaging-guided targeted radionuclide therapy for α5β1-overexpressing tumors.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, which increases the incidence of colorectal cancer (CRC). In the pathophysiology of IBD, ubiquitination/deubiquitination plays a critical regulatory function. Josephin domain containing 2 (JOSD2), a deubiquitinating enzyme, controls cell proliferation and carcinogenesis. However, its role in IBD remains unknown. Colitis mice model developed by dextran sodium sulfate (DSS) or colon tissues from individuals with ulcerative colitis and Crohn's disease showed a significant upregulation of JOSD2 expression in the macrophages. JOSD2 deficiency exacerbated the phenotypes of DSS-induced colitis by enhancing colon inflammation. DSS-challenged mice with myeloid-specific JOSD2 deletion developed severe colitis after bone marrow transplantation. Mechanistically, JOSD2 binds to the C-terminal of inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) and preferentially cleaves K63-linked polyubiquitin chains at the K134 site, suppressing IMPDH2 activity and preventing activation of nuclear factor kappa B (NF-κB) and inflammation in macrophages. It was also shown that JOSD2 knockout significantly exacerbated increased azoxymethane (AOM)/DSS-induced CRC, and AAV6-mediated JOSD2 overexpression in macrophages prevented the development of colitis in mice. These outcomes reveal a novel role for JOSD2 in colitis through deubiquitinating IMPDH2, suggesting that targeting JOSD2 is a potential strategy for treating IBD.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

BACKGROUND: Knee osteoarthritis (OA) is still challenging to prevent or treat. Enhanced endoplasmic reticulum (ER) stress and increased pyroptosis in chondrocytes may be responsible for cartilage degeneration. This study aims to investigate the effect of ER stress on chondrocyte pyroptosis and the upstream regulatory mechanisms, which have rarely been reported. METHODS: The expression of the histone methyltransferase enhancer of zeste homolog 2 (EZH2), microRNA-142-3p (miR-142-3p), and high mobility group box 1 (HMGB1) and the levels of ER stress, pyroptosis, and metabolic markers in normal and OA chondrocytes were investigated by western blotting, quantitative polymerase chain reaction, immunohistochemistry, fluorescence in situ hybridization, fluorescein amidite-tyrosine-valine-alanine-aspartic acid-fluoromethyl ketone (FAM-YVAD-FMK)/Hoechst 33342/propidium iodide (PI) staining, lactate dehydrogenase (LDH) release assays, and cell viability assessments. The effects of EZH2, miR-142-3p, and HMGB1 on ER stress and pyroptosis and the hierarchical regulatory relationship between them were analyzed by chromatin immunoprecipitation, luciferase reporters, gain/loss-of-function assays, and rescue assays in interleukin (IL)-1β-induced OA chondrocytes. The mechanistic contribution of EZH2, miR-142-3p, and HMGB1 to chondrocyte ER stress and pyroptosis and therapeutic prospects were validated radiologically, histologically, and immunohistochemically in surgically induced OA rats. RESULTS: Increased EZH2 and HMGB1, decreased miR-142-3p, enhanced ER stress, and activated pyroptosis in chondrocytes were associated with OA occurrence and progression. EZH2 and HMGB1 exacerbated and miR-142-3p alleviated ER stress and pyroptosis in OA chondrocytes. EZH2 transcriptionally silenced miR-142-3p via H3K27 trimethylation, and miR-142-3p posttranscriptionally silenced HMGB1 by targeting the 3'-UTR of the HMGB1 gene. Moreover, ER stress mediated the effects of EZH2, miR-142-3p, and HMGB1 on chondrocyte pyroptosis. In vivo experiments mechanistically validated the hierarchical regulatory relationship between EZH2, miR-142-3p, and HMGB1 and their effects on chondrocyte ER stress and pyroptosis. CONCLUSIONS A novel EZH2/miR-142-3p/HMGB1 axis mediates chondrocyte pyroptosis and cartilage degeneration by regulating ER stress in OA, contributing novel mechanistic insights into OA pathogenesis and providing potential targets for future therapeutic research.
Enhancer of Zeste Homolog 2 Protein/genetics* ; Osteoarthritis, Knee/pathology* ; Chondrocytes/metabolism* ; Pyroptosis/physiology* ; HMGB1 Protein/genetics* ; MicroRNAs/metabolism* ; Endoplasmic Reticulum Stress/genetics* ; Humans ; Animals ; Rats ; Male ; Rats, Sprague-Dawley ; Middle Aged

The purpose of the present study was to investigate the effects of resistance combined with aerobic chrono-exercise on the common carotid artery elasticity and hemodynamics. 24 healthy young men (21.96±0.43 years old) underwent a single acute resistance combined with aerobic exercise intervention at eight time periods (6, 8, 10, 12, 14, 16, 18, and 20 o'clock). The axial flow velocity and diameter waveforms of the common carotid artery were measured, and the hemodynamics were calculated using the classical hemodynamic theory before exercise, immediately after exercise, 10 min and 20 min after exercise. The results showed that during exercise recovery, systolic and mean pressures decreased more markedly after exercise at 8 o'clock (P < 0.05); At 20 min post-exercise, arterial stiffness index and pressure-strain elastic modulus after exercise at 6 o'clock were reduced compared with the resting state, but were significantly elevated after exercise at 20 o'clock (P < 0.05). Immediately after exercise, the pressure rise was higher after exercise at 6 o'clock and the mean wall shear stress was higher after exercise at 20 o'clock (P < 0.05). These results suggest that resistance combined with aerobic chrono-exercise produces different effects on common carotid artery hemodynamics in young men. A single acute session of resistance combined with aerobic exercise at 8 o'clock is more effective in lowering blood pressure. Exercise at 6 o'clock is beneficial to improve arterial elasticity but is not recommended for young male individuals with cardiovascular disease risks because of the excessive increase in blood pressure immediately after exercise. Exercise at 20 o'clock is more effective in improving wall shear stress but is accompanied by elevated arterial stiffness indices and pressure-strain elastic modulus. These results provide a scientific basis for healthy young men in choosing the time of exercise by exploring the common carotid artery elasticity and hemodynamic-related indices.
Humans ; Male ; Young Adult ; Exercise/physiology* ; Carotid Artery, Common/physiology* ; Hemodynamics/physiology* ; Vascular Stiffness/physiology* ; Elasticity ; Resistance Training ; Adult

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.