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.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

OBJECTIVE: Cancer remains a significant global health challenge, necessitating the development of effective treatment approaches. Developing synergistic therapy can provide a highly promising strategy for anti-cancer treatment through combining the benefits of various mechanisms. METHODS: In this study, we developed a synergistic strategy for chemo-photothermal therapy by constructing nanocomposites using gold nanorods (GNRs) and tetrahedral framework nucleic acids (tFNA) loaded with the anti-tumor drug doxorubicin (DOX). RESULTS: Our in vitro studies have systematically clarified the anti-cancer behaviors of tFNA-DOX@GNR nanocomposites, characterized by their enhanced cellular uptake and proficient lysosomal escape capabilities. It was found that the key role of tFNA-DOX@GNR nanocomposites in tumor ablation is primarily due to their capacity to induce cytotoxicity in tumor cells via a photothermal effect, which generates instantaneous high temperatures. This mechanism introduces various responses in tumor cells, facilitated by the thermal effect and the integrated chemotherapeutic action of DOX. These reactions include the induction of endoplasmic reticulum stress, characterized by elevated reactive oxygen species levels, the promotion of apoptotic cell death, and the suppression of tumor cell proliferation. CONCLUSION This work exhibits the potential of synergistic therapy utilizing nanocomposites for cancer treatment and offers a promising avenue for future therapeutic strategies.
Doxorubicin/chemistry* ; Gold/chemistry* ; Nanotubes/chemistry* ; Humans ; Nanocomposites/chemistry* ; Cell Line, Tumor ; Nucleic Acids/chemistry* ; Antibiotics, Antineoplastic/pharmacology* ; Antineoplastic Agents/administration & dosage*

Objective To evaluate the recognition capability of AI-enabled Cellsee CS-BM1 automatic cell morphology analyzer for pe-ripheral blood smears and its roles in assisting manual classification,and explore the application value of AI system in the diagnosis network of tiered primary medical units.Methods The blood samples which triggered the re-examination rules were collected from six primary medical units,including the Laboratory Department of Shanghai Jiahui International Hospital,and so on,from March to No-vember 2023.The smears of peripheral blood were prepared and AI analyzer was used for pre-classification to evaluate its recognition performance in identifying the samples with abnormal WBC and RBC.The sensitivity,specificity,and accuracy of WBC classification by six junior and intermediate technicians,both with and without AI assistance,were analyzed.Additionally,the roles of the AI system in tiered diagnosis of primary medical units were also evaluated.Results The sensitivity,specificity,and accuracy of AI system in recognizing malignant primitive cells were 92.86％,95.16％,and 95.10％,respectively.The sensitivities of AI system in recognizing immature granulocytes,reactive lymphocytes,and nucleated RBCs were all greater than 90％.The sensitivity of AI system in identif-ying abnormal morphology of RBCs reached 99.59％,along with rapid quantitative analysis for various anomalous types of RBCs.In AI-assisted mode,the sensitivity of recognition for all cell types was improved to varying degrees by junior and intermediate technicians,and the sensitivity for recognizing malignant primitive cells,reactive lymphocytes,and immature granulocytes increased to 58.24％,53.39％,and 62.37％for junior technicians,and to 92.06％,83.24％,and 83.12％for intermediate technicians,respectively.The improvements for junior technicians were particularly significant,with increases of 12.46％,10.61％,and 3.71％for each cell type,respectively.Both groups achieved higher specificity and accuracy.Through AI pre-classification and manual review,a variety of pe-ripheral blood cell-related diseases were accurately diagnosed in the tiered healthcare practice of primary medical units,including 339 cases(11.13％)of red blood cell diseases,5 cases(0.16％)of platelet diseases,2 343 cases(76.90％)of infection-related disea-ses,and 28 cases(0.92％)of malignant hematological diseases.In addition,332 cases(10.90％)which lacked an obvious related cause or required further examinations were identified as well.Conclusion AI pre-classification has demonstrated strong cell recogni-tion capabilities and may assist technicians in improving the sensitivity,specificity,and accuracy of blood cell classification.AI could en-hance the disease-screening capabilities in the tiered diagnosis network of primary medical units,presenting a broad application prospect.

Objective:To investigate the causal effects of plasma lipidomics on pancreatitis using Mendelian ran-domization(MR)and evaluate the roles of intra-pancreatic fat deposition(IPFD)and gallstone disease in this relation-ship.Methods:A bidirectional MR analysis was conducted,with 179 plasma lipids as exposures and acute pancreati-tis(AP)and chronic pancreatitis(CP)as outcomes.Data were sourced from genome-wide association studies(GWAS),the UK Biobank,and the FinnGen project.Two-step Mendelian randomization(TSMR)and multivariable Mendelian ran-domization(MVMR)analyses were applied to assess the mediating roles of IPFD and gallstone disease in the associa-tion between plasma lipids and pancreatitis.Results:MR analysis identified two sterols negatively associated with AP(P＜0.05)and seven sterols negatively associated with CP(P＜0.05).One phospholipid showed a positive association with CP(P＜0.05).IPFD was positively associated with both AP and CP.Gallstone disease was confirmed as a risk fac-tor for AP.However,TSMR analysis indicated that neither IPFD nor gallstone disease mediated the relationship be-tween plasma lipids and pancreatitis.Conclusion:The causal relationship exists among plasma lipomics and AP/CP,also between IPFD,cholelithiasis and pancreatitis.These findings highlight novel risk factors and potential biomarkers to support early diagnosis and intervention for pancreatitis.

The chemical constituents of the petroleum ether extract derived from the 90% ethanol extract of Elephantopus scaber were investigated. By silica gel column chromatography, C_(18), MCI column chromatography and semi-preparative high performance liquid chromatography, ten compounds were isolated. Their structures were identified as 3β-hydroxy-6β,7β-epoxytaraxeran-14-ene(1), 3β-hydroxyolean-12-en-28-oic acid(2), D-friedoolean-14-ene-3β,7α-diol(3), 3β-hydroxy-11α-methoxyolean-12-ene(4), 3β-hydroxyolean-11,13(18)-diene(5), 11α-hydroxy-β-amyrin(6), betulinic acid(7), 3β-hydroxy-30-norlupan-20-one(8), 6-acetonylchelerythrine(9), and 4',5'-dehydrodiodictyonema A(10) by analysis of the 1D NMR, 2D NMR, MS, and IR spectral data. Among them, compound 1 was a new triterpene and other compounds except compounds 2 and 7 were isolated from this plant for the first time.
Triterpenes/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Asteraceae/chemistry* ; Chromatography, High Pressure Liquid ; Magnetic Resonance Spectroscopy

Objective To investigate the interaction between a novel antimicrobial compound,HL-J6,and penicillin-binding protein 1(PBP1)of Staphylococcus aureus.Methods With MRSA252 genomic DNA as the template and PBP1F and PBP1R as primers,the expression plasmid pET30a-pbp1-39-608 was constructed by amplifying the target gene fragment followed by cloning into the Nde I/Xho I restriction sites of the pET30a vector.Then the obtained plasmids were transformed into Escherichia coli for the expression of PBP1-39-608 protein,and the product was purified by affinity chromatography.The inhibitory effect of HL-J6 on the transpeptidase activity of PBP1-39-608 was measured using peptidoglycan side chain backbone peptide,with thiol ester analog S2d as the substrate.The affinity between HL-J6 and PBP1-39-608 was detected using microscale thermophoresis(MST),and the binding interaction was confirmed by cellular thermal shift assay(CETSA).Molecular docking and dynamics simulation were performed using AutoDock Vina and Desmond software,respectively,to elucidate the binding mode of HL-J6 with the PBP1-39-608 protein and the key amino acid residues involved.Results The recombinant plasmid pET30a-pbp1-39-608 was successfully constructed,and PBP1-39-608 protein was produced after induction and purified,yielding a protein with an approximate molecular mass of 65×103.HL-J6 inhibited the transpeptidase activity of PBP1-39-608 in a time-dependent manner(P<0.001).The dissociation constant Kd of the binding between HL-J6 and PBP1-39-608 was 64.92 μmol/L.Molecular docking results showed that HL-J6 bound to the active pocket of PBP1-39-608 by interacting with key residues such as ILE-348,ASN-370,THR-516 and PHE-423,with a binding score of-8.38 kcal/mol(<-5.00 kcal/mol).Dynamics simulation results indicated that the complex became stable after 50 ns.Conclusion HL-J6 effectively inhibits the transpeptidase activity of Staphylococcus aureus PBP1,and shows stable interaction with the protein.