[Objective] To validate and optimize the platelet transfusion refractoriness (PTR) prediction model for patients with hematological disorders established by our center. [Methods] The data of patients with hematological diseases who received platelet transfusions from December 2021 to December 2022 were used as the training set, and data from January 2023 to December 2023 as the validation set. The validation set data was used to validate the predictive model constructed on the training set. Relevant risk factors for PTR were collected through literature review and preliminary studies。 The patients were divided into effective and ineffective groups according to the corrected count increment (CCI) of platelet counts. Predictive factors were screened using univariate and multivariate logistic regression. The calibration of the model were assessed via calibration curves, while discrimination, accuracy, sensitivity, and specificity were evaluated using receiver operating characteristic (ROC) curves Clinical utility was further analyzed with decision curve analysis (DCA). [Results] The Hosmer-Lemeshow (H-L) goodness-of-fit test for the validation set yielded S: P=0.000, indicating that the original model needs optimization. Baseline comparisons and logistic regression identified the number of red blood cell units (RBCU) and platelet units (PLT-U) transfused as key predictors for the optimized model. The H-L goodness-of-fit test S: P values for the training and validation sets were 0.930 and 0.056, respectively; the ROC areas were 0.793 5 and 0.809 4, specificities 90.95% and 84.21%, sensitivities 59.26% and 70.04%, and accuracies 78.14% and 74.10%, respectively. DCA demonstrated clinical net benefit within a prediction probability threshold range of 0.2-0.8. [Conclusion] Transfusion volumes of RBC-U and PLT-U were inversely associated with PTR in hematological patients. The resulting PTR prediction model exhibits moderate predictive efficacy and clinical benefit.

Objective To investigate the sputum metabolic profiles of patients with occupational coal workers' pneumoconiosis (CWP) by an untargeted metabolomics method, and to identify relevant differential metabolic pathways and potential biomarkers. Methods A total of 12 male patients with stage Ⅰ CWP were selected as the CWP group, and 16 healthy male individuals were selected as the control group, using a judgmental sampling method. Sputum metabolites of individuals in both groups were detected to perform non-targeted metabolomic analysis using the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Differential metabolites (DMs) and their pathways were screened using principal component analysis, partial least squares discriminant analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Potential biomarkers were analyzed and identified via the receiver operating characteristic curve (ROC). Results There were apparent metabolic alterations observed in sputum of CWP patients compared with healthy controls. In the positive ion mode, a total of 42 DMs were identified in sputum from CWP patients, including 19 downregulated and 23 upregulated metabolites. In the negative ion mode, a total of 25 DMs were identified in sputum from CWP patients, including 16 downregulated and 9 upregulated metabolites. KEGG enrichment analysis of sputum from CWP patients showed that seven DMs pathways were enriched in ABC transporters, histidine metabolism, phenylalanine metabolism, arachidonic acid metabolism, linoleic acid metabolism, purine metabolism, and oxidative phosphorylation, involving 26 DMs. ROC analysis indicated that 16(R)-hydroxyarachidonic acid, pyrophosphate, and 2-hydroxyphenylacetate of these 26 DMs may serve as potential biomarkers for CWP. Conclusion Sputum metabolomic profiles were altered in CWP patients compared with healthy controls. The potential biomarkers of CWP prevention and treatment are 16(R)-hydroxyarachidonic acid, pyrophosphate, and 2-hydroxyphenylacetate.

BACKGROUND: T-cell lymphoblastic lymphoma/acute lymphoblastic leukemia (T-LBL/ALL) is an aggressive form of hematological malignancy associated with poor prognosis in adult patients. Histone deacetylases (HDACs) are aberrantly expressed in T-LBL/ALL and are considered potential therapeutic targets. Here, we investigated the antitumor effect of a novel HDAC inhibitor, chidamide, on T-LBL/ALL. METHODS: HDAC1, HDAC2 and HDAC3 levels in T-LBL/ALL cell lines and patient samples were compared with those in normal controls. Flow cytometry, transmission electron microscopy, and lactate dehydrogenase release assays were conducted in Jurkat and MOLT-4 cells to assess apoptosis and pyroptosis. A specific forkhead box O1 (FOXO1) inhibitor was used to rescue pyroptosis and upregulated gasdermin E (GSDME) expression caused by chidamide treatment. The role of the FOXO1 transcription factor was evaluated by dual-luciferase reporter and chromatin immunoprecipitation assays. The efficacy of chidamide in vivo was evaluated in a xenograft mouse. RESULTS: The expression of HDAC1, HDAC2 and HDAC3 was significantly upregulated in T-LBL/ALL. Cell viability was obviously inhibited after chidamide treatment. Pyroptosis, characterized by cell swelling, pore formation on the plasma membrane and lactate dehydrogenase leakage, was identified as a new mechanism of chidamide treatment. Chidamide triggered pyroptosis through caspase 3 activation and GSDME transcriptional upregulation. Chromatin immunoprecipitation assays confirmed that chidamide led to the increased transcription of GSDME through a more relaxed chromatin structure at the promoter and the upregulation of FOXO1 expression. Moreover, we identified the therapeutic effect of chidamide in vivo . CONCLUSIONS This study suggested that chidamide exerts an antitumor effect on T-LBL/ALL and promotes a more inflammatory form of cell death via the FOXO1/GSDME axis, which provides a novel choice of targeted therapy for patients with T-LBL/ALL.
Humans ; Pyroptosis/drug effects* ; Forkhead Box Protein O1/genetics* ; Aminopyridines/pharmacology* ; Animals ; Mice ; Benzamides/pharmacology* ; Cell Line, Tumor ; Precursor T-Cell Lymphoblastic Leukemia-Lymphoma/drug therapy* ; Phosphate-Binding Proteins/metabolism* ; Histone Deacetylase Inhibitors/pharmacology* ; Jurkat Cells ; Histone Deacetylases/metabolism* ; Apoptosis/drug effects* ; Gasdermins

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Spontaneous intracerebral hemorrhage (ICH) is the second common subtype of stroke, characterized by high incidence, high mortality and high disability rates. The recurrent ICH not only brings physical pain to patients, but also increases the burden on families and society. In recent years, researches on the risk factors for recurrent ICH have been increasing day by day. This article reviews the risk factors for recurrent ICH from the perspectives of clinical features, biomarkers, and imaging characteristics, in order to provide a basis for the secondary prevention of ICH.

Cerebral blood flow directly affects the metabolism of substances and neural activity in the brain, and is closely associated with the occurrence and development of cerebral small vessel disease (CSVD). Multiple studies have revealed that various imaging biomarkers in patients with CSVD, such as lacunar infarction, enlarged perivascular spaces, cerebral microbleeds, cerebral atrophy, and white matter hyperintensities, are closely associated with cerebral autoregulation (CA) function. Therefore, understanding the regulatory mechanism of CA in patients with CSVD is of great significance for delaying the further development of CSVD, improving cerebral ischemia and cognitive impairment. This article reviews the correlation and mechanism between CA and CSVD.

Objective To discuss the efficacy of transcatheter arterial chemoembolization(TACE)in combination with targeted therapy and immune checkpoint inhibitors for advanced hepatocellular carcinoma(HCC),and to identify the influencing factors.Methods A total of 60 patients with advanced HCC,who were admitted to the Air Force Medical Center of China from January 2016 to December 2022,were enrolled in this study.Thirty patients received TACE combined with targeted therapy and immune checkpoint inhibitors(TACE-L-P group),and the other 30 patients received TACE combined with targeted therapy(TACE-L group).The progression-free survival(PFS),overall survival(OS),disease control rate(DCR),and objective response rate(ORR)were compared between the two groups.Results In the TACE-L group and TACE-L-P group,the median PFS(mPFS)was 7 months and 10 months respectively(P=0.011),the median OS(mOS)was 15.5 months and 29 months respectively(P=0.014).Child-Pugh class B(HR=3.89,95％CI:1.27-11.94,P=0.018)and Barcelona Clinic Liver Cancer(BCLC)stage C(HR=2.83,95％CI:1.32-6.03,P=0.007)were the independent risk factors for OS,while micro wave ablation(HR=0.21,95％CI:0.07-0.63,P=0.005)and TACE-L-P(HR=0.09,95％CI:0.03-0.3,P=0.001)were the independent protection factors for OS.Besides,elevated bilirubin level(HR=1.03,95％CI:1-1.06,P=0.032)and elevated gamma-glutamyl transferase(GGT)level(HR=1.01,95％CI:1-1.01,P=0.002)were the independent risk factors for disease progression,and TACE-L-P(HR=0.27,95％CI:0.09-0.79,P=0.017)was the independent protection factor for disease progression.The ORR and DCR in TACE-L-P group were remarkably higher than those in TACE-L group,which were 43.4％vs 13.3％and 63.4％vs 23.3％respectively,the differences between the two groups were statistically significant(both P＜0.05).Conclusion In treating advanced HCC,TACE combined with targeted therapy and immune checkpoint inhibitors is superior to TACE combined with targeted therapy in therapeutic efficacy.

The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Objective:To analyze the role of dandelion and mulberry leaf in the progression of acute myeloid leukemia(AML)by network pharmacology,and to clarify the active components and their mechanisms in treating AML.Methods:The active components of dandelion and mulberry leaf were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).The targets were predicted by SwissTargetPrediction Database.The AML-related genes and protein targets were retrieved from the SymMap Database,the GeneCards Human Gene Database,the DisGeNET Database,and the Online Mendelian Inheritance in Man(OMIM)Database.The AML-related genes and target genes of dandelion and mulberry leaf were compared by comparative analysis and were identify by the enrichment genes,followed by Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis.The drug-active component-target network and protein-protein interaction(PPI)network were constructed by Cytoscape 3.8.0 software,and the core genes were selected by CytoNCA plugin;the molecular docking was conducted by AutoDock software.Results:After filtering by databases,39 active components were identified,and 148 common targets between dandelion-mulberry leaf and AML were collected.The GO functional enrichment analysis mainly involved cytokine-mediated signaling pathways,positive regulation of kinase activity,and oxidative stress responses.The KEGG signaling pathway enrichment analysis focused on the phosphatidylinositol 3 kinase/protein kinase B(PI3K-AKT)signaling pathway,the tumor necrosis factor(TNF)signaling pathway,and the Janus kinase/signal transducer and activator of transcription(JAK-STAT)signaling pathway.The key targets were identified by topological analysis including signal transducer and activator of transcription 3(STAT3),epidermal growth factor receptor(EGFR),protein kinase B1(AKT1),recombinant human epidermal growth factor(EGF),vascular endothelial growth factor A(VEGFA),oncogene MYC,tumor protein P53(TP53),mitogen-activated protein kinase 3(MAPK3),cysteiny asparate specific protease-3(CASP3),oncogene SRC,heat shock protein 90 alpha family class A member 1(HSP90AA1),tenascin XB1(CTNNB1),phosphoinositide kinase-3 catalytic subunit alpha(PIK3CA),interleukin 6(IL-6),TNF,mitogen-activated protein kinase 1(MAPK1),and phosphatidylinositide kinase-3 regulatory subunit 1(PIK3R1).The molecular docking results showed the highest affinity pairing to be taraxerol with MYC(-8.74 kcal·mol-1),and quercetin,kaempferol,luteolin,and artemetin demonstrated good binding affinities with various targets.Conclusion:The main active components of dandelion-mulberry leaf,such as quercetin,taraxerol,kaempferol,luteolin,and artemetin,may exert the anti-AML effect by regulating AKT1,STAT3,HSP90AA1,IL-6,and MAPK1;regulation the PI3K-AKT signaling pathway may be the critical mechanism of anti-AML effect by dandelion-mulberry leaf.

Cerebral hyperperfusion syndrome (CHS) is a rare but serious complication after cerebral revascularization, which may lead to catastrophic consequences. The mechanism of CHS is not fully understood, and it may be related to cerebral autoregulation dysfunction and the increase of blood pressure after operation. Timely detection and treatment of cerebral hyperperfusion can avoid CHS. This article reviews the pathogenesis, diagnosis, clinical manifestations, prevention and treatment of CHS.