OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

Objective:To investigate the anti-leukemic effects and resistance mechanisms of venetoclax in acute myeloid leukemia (AML). Genomic, transcriptomic, and clinical data from AML patients who underwent venetoclax drug sensitivity testing were downloaded from the Beat AML database. Correlation analysis was performed between these data and venetoclax sensitivity outcomes. Differentially expressed genes (DEGs) associated with venetoclax sensitivity were identified from transcriptomic data and subsequently validated using GEO database transcriptomic results and in vitro experiments (including Western blot). Functional enrichment analyses (KEGG and GSEA), transcription factor enrichment analysis (KnockTF), and data from public databases were employed to further investigate key genes and pathways influencing drug sensitivity.Results:After filtering the Beat AML cohort, data from 52 patient samples with available in vitro venetoclax sensitivity results were included for analysis. Patients with FLT3 mutations exhibited greater sensitivity to venetoclax compared to those with FLT3 wild-type. Correlation analysis between clinical information and drug sensitivity data indicated that higher peripheral blood tumor burden was associated with increased sensitivity to venetoclax. Transcriptomic analysis and in vitro experiments confirmed that venetoclax inhibits the FLT3-related signaling pathway, including downregulation of FLT3 expression and reduced phosphorylation of its downstream targets AKT and STAT5. KEGG pathway and KnockTF transcription factor enrichment analyses indicated that venetoclax resistance was associated with increased transcriptional activity of FOXM1 and STAT3. Moreover, high expression of FOXM1 and STAT3 correlated with shorter overall survival in patients.Conclusion:Venetoclax can inhibit the activation of FLT3-related signaling pathways. The activation of STAT3 and FOXM1 transcription factors is a potential key mechanism contributing to venetoclax resistance in AML.

OBJECTIVE: To investigate the impact of bone mass and volume of low-density zones beneath the tibial plateau on the maximum von Mises stresses experienced by the cartilage and meniscus in the knee joint. METHODS: The study included one healthy adult volunteer, from whom CT scans were obtained, and one patient diagnosed with knee osteoarthrisis (KOA), for whom X-ray films were acquired. A static model of the knee joint featuring a low-density zone was established based on a normal knee model. In the finite element analysis, axial loads of 1 000 N and 1 800 N were applied to the weight-bearing region of the upper surface of the femoral head for model validation and subsequent finite element studies, respectively. The maximum von Mises stresses in the femoral cartilage, as well as the medial and lateral tibial cartilage and menisci, were observed, and the stress percentage of the medial and lateral components were concurrently analyzed. Additionally, HE staining, as well as alkaline magenta staining, were performed on the pathological specimens of patients with KOA in various low-density regions. RESULTS: The results of model validation indicated that the model was consistent with normal anatomical structures and correlated with previous calculations documented in the literature. Static analysis revealed that the maximum von Mises stress in the medial component of the normal knee was the lowest and increased with the advancement of the hypointensity zone. In contrast, the lateral component exhibited an opposing trend, with the maximum von Mises stress in the lateral component being the highest and decreasing as the hypointensity zone progressed. Additionally, the medial component experienced an increasing proportion of stress within the overall knee joint. HE staining demonstrated that the chondrocyte layer progressively deteriorated and may even disappear as the hypointensity zone expanded. Furthermore, alkaline magenta staining indicated that the severity of microfractures in the trabecular bone increased concurrently with the expansion of the hypointensity zone. CONCLUSION The presence of subtalar plateau low-density zone may aggravate joint degeneration. In clinical practice, it is necessary to pay attention to the changes in the subtalar plateau low-density zone and actively take effective measures to strengthen the bone status of the subtalar plateau low-density zone and restore the complete biomechanical function of the knee joint, in order to slow down or reverse the progression of osteoarthritis.
Humans ; Finite Element Analysis ; Knee Joint/physiology* ; Tibia/anatomy & histology* ; Cartilage, Articular/physiology* ; Menisci, Tibial/physiopathology* ; Tomography, X-Ray Computed ; Osteoarthritis, Knee/diagnostic imaging* ; Weight-Bearing ; Bone Density ; Adult ; Stress, Mechanical ; Male ; Middle Aged ; Biomechanical Phenomena ; Female

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

AIM To evaluate the quality of Rubi Fructus.METHODS UPLC-Q-TOF-MS/MS was adopted in the component identification,after which the HPLC fingerprints were established,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition.and the contents of chlorogenic acid,ferulic acid,ellagic acid,isoquercitrin,kaempferol-3-O-rutinoside,astragalin,tiliroside quercetin,kaempferol were determined.RESULTS Total 34 constituents were identified.There were 19 common peaks in the fingerprints for 31 batches of medicinal materials with the similarities of more than 0.8.Wild varieties and cultivated varieties,and medicinal materials from different producing areas could be distinguished;4 principal components demonstrated the accumulative variance contribution rate of 84.142％;8 differential components were screened,2 of which were ellagic acid and astragalin.Ellagic acid and astragalin displayed higher contents in the wild varieties than those in the cultivated varieties(P＜0.05,P＜0.01).CONCLUSION UPLC-Q-TOF-MS/MS,HPLC fingerprints combined with content determination can be used for the quality control of Rubi Fructus.

AIM To investigate the effects of Congrong San on neuronal apoptosis and the Bax/Bcl-2/Caspase3 signaling pathway in a rat model of Alzheimer's disease(AD).METHODS A total of 60 2-month-old SD male rats were randomly divided into the blank group,the model group,the memantine hydrochloride group(0.025 g/kg)and low-dose,medium-dose and high-dose Congrong San groups(4.62,9.24,18.48 g/kg).All groups except the control group received stereotactic intracerebral injection of Aβ1-42 to establish AD models.Following the successful modeling,each group received its corresponding intragastric administration once daily for 28 consecutive days.After the administration,the rats had their learning and memory ability detected by the morris water maze test;their hippocampal neuronal morphology observed with HE and Nissl staining;their hippocampal neuronal apoptosis observed with TUNEL staining;and their hippocampal expressions of amyloid precursor protein(APP),β-site APP-cleaving enzyme 1(BACE1),and apoptosis-related proteins Bax,Bcl-2 and Caspase3 detected with Western blot assay.RESULTS Compared with the model group,the groups intervened with medium-dose and high-dose Congrong San exhibited improved learning and memory performance,alleviated hippocampal neuronal damage,increased Nissl body count(P＜0.01),reduced hippocampal apoptosis rate(P＜0.05,P＜0.01),decreased protein expressions of APP,BACE1,Bax and cleaved-Caspase3/Caspase3 ratio(P＜0.05,P＜0.01),and elevated Bcl-2 expression(P＜0.01).CONCLUSION Congrong San mitigates cognitive impairment,hippocampal neuronal damage,and apoptosis in AD rats,probably through inhibition of the Bax/Bcl-2/Caspase3 signaling pathway activation.

OBJECTIVE To design and synthesize rifamycin S derivatives and load them into milk exosomes to evaluate their in vitro antimicrobial activity.METHODS Rifamycin S derivatives were synthe-sized and characterized by mass spectrometry and NMR.Using the dilution assay method,the inhibitory activity of each rifamycin S derivatives molecule against Staphylococcus aureus and Pseudomonas aerugi-nosa was determined,and the IC50 was calculated.Derivatives molecules with excellent antimicrobial activity were selected and loaded into milk exosomes using the ultrasonication method,resulting in the preparation of milk exosome-loaded rifamycin S derivatives.The antimicrobial activity against Staphylo-coccus aureus was determined using the dilution assay method.The inhibitory effect of the exosome-loaded rifamycin S derivatives on Staphylococcus aureus residing within macrophages was detected using the plate colony counting method.RESULTS Three rifamycin S derivatives were successfully designed and synthesized,which demonstrated superior antimicrobial activity against Staphylococcus aureus(the parent compound's antimicrobial activity is merely from 1/20 to 1/80 of that of the three rifamycin S derivatives)and Pseudomonas aeruginosa(the parent compound's antimicrobial activity is only 1/14 and 1/9 of that of compound 1 and compound 3)compared to the parent compound.The loading of milk exosomes with the rifamycin S derivatives compound 3 was successfully achieved,with a loading efficiency of 10.9％.The antimicrobial activity of the compound after exosome loading was significantly enhanced against Staphylococcus aureus in vitro and against Staphylococcus aureus residing within macrophages(P＜0.01).CONCLUSION The designed and synthesized derivatives of rifamycin S possess stronger anti-microbial activity,and their antibacterial efficacy against both extracellular and intracellular bacteria can be further enhanced after loading into exosomes.

Objective To evaluate the safety and efficacy of valve-in-valve transcatheter mitral valve replacement(ViV-TMVR)in patients with bioprosthetic valve degeneration who are at high surgical risk.Methods This study is a multi-center,retrospective cohort analysis of 20 consecutive patients who underwent transseptal ViV-TMVR using the Edwards SAPIEN 3 transcatheter heart valve(THV).The primary endpoints include technical success and procedural success,both defined according to the Mitral Valve Academic Research Consortium(MVARC)criteria,as well as mortality and functional change assessed based on New York Heart Association(NYHA)classification at 30-days and six months post-procedure.Clinical follow-up assessments are conducted at 30-days and six months.Results From February 2021 to October 2022,a total of 20 patients with symptoms of bioprosthetic valve degeneration were enrolled across nine sites in China.The patients had a mean age of(73.5±5.5)years,with 85.0％being females and 70.0％classified as NYHA class Ⅲ/Ⅳ.The study achieved a 100.0％technical success rate and a 90.0％procedural success rate finally.All patients remained alive during the 30-day follow-up period.However,six months post-intervention,two patients(10.0％)were re-hospitalized due to heart failure,and sadly,one of them(5.0％)died.None of the patients reported any adverse events related to ViV-TMVR during the follow-up period.Notably,there was a significant improvement in NYHA class compared to baseline(P=0.0004)at six-month follow-ups.Conclusions The transseptal ViV-TMVR technique proved to be highly successful and was associated with significant improvement in NYHA class function.These findings strongly suggest that it serves as a safe and efficient treatment alternative for high-risk patients suffering from bioprosthetic valve degeneration.

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).