ObjectiveThe nucleolar protein PES1 (Pescadillo homolog 1) plays critical roles in ribosome biogenesis and cell cycle regulation, yet its involvement in cellular senescence remains poorly understood. This study aimed to comprehensively investigate the functional consequences of PES1 suppression in cellular senescence and elucidate the molecular mechanisms underlying its regulatory role. MethodsInitially, we assessed PES1 expression patterns in two distinct senescence models: replicative senescent mouse embryonic fibroblasts (MEFs) and doxorubicin-induced senescent human hepatocellular carcinoma HepG2 cells. Subsequently, PES1 expression was specifically downregulated using siRNA-mediated knockdown in these cell lines as well as additional relevant cell types. Cellular proliferation and senescence were assessed by EdU incorporation and SA-β-gal staining assays, respectively. The expression of senescence-associated proteins (p53, p21, and Rb) and SASP factors (IL-6, IL-1β, and IL-8) were analyzed by Western blot or qPCR. Furthermore, Northern blot and immunofluorescence were employed to evaluate pre-rRNA processing and nucleolar morphology. ResultsPES1 expression was significantly downregulated in senescent MEFs and HepG2 cells. PES1 knockdown resulted in decreased EdU-positive cells and increased SA‑β‑gal-positive cells, indicating proliferation inhibition and senescence induction. Mechanistically, PES1 suppression activated the p53-p21 pathway without affecting Rb expression, while upregulating IL-6, IL-1β, and IL-8 production. Notably, PES1 depletion impaired pre-rRNA maturation and induced nucleolar stress, as evidenced by aberrant nucleolar morphology. ConclusionOur findings demonstrate that PES1 deficiency triggers nucleolar stress and promotes p53-dependent (but Rb-independent) cellular senescence, highlighting its crucial role in maintaining nucleolar homeostasis and regulating senescence-associated pathways.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

OBJECTIVE: To characterize the occurrence of SRSF2 mutations in chronic myelomonocytic leukemia(CMML) patients and their correlation with other gene mutations and some clinical characteristics. METHODS: The clinical data of 43 CMML patients diagnosed in Changzhou No.2 People's Hospital and Wuxi No.2 People's Hospital were retrospectively analyzed, and gene mutations detection was performed using next-generation sequencing (NGS). RESULTS: Among the 43 CMML patients the SRSF2 mutation detection rate was 39.5%(17/43). These mutations clustered collectively at the proline 95 residue in the splicing factor SRSF2. The other genes with mutation rate greater than 15% were ASXL1 (48.8%), TET2 (41.9%), NRAS (30.2%), RUNX1 (25.6%), and SETBP1 (16.3%). Among SRSF2- mutated patients, the most common co-mutation was ASXL1, followed by TET2. The median age of SRSF2 mutant patients was significantly higher than that of the wild type (68 vs 51.5, P < 0.001), but there was not statistically significant differences in gender, peripheral leukocytes, hemoglobin, platelets, karyotype, and blast cell compared to the wild-type (all P >0.05). Notably, 4 out of the 6 SRSF2 ^mutASXL1^mut CMML patients developed leukemia transformation, and 1 out of 10 SRSF2 ^wtASXL1^wt CMML patients developed leukemia transformation, with statistically significant difference in leukemia transformation rates (66.7% vs 10%, P =0.036). CONCLUSION SRSF2 mutations have a high incidence in CMML, occurring frequently in older patients, and often coexisting with ASXL1 and TET2 mutations. Patients with CMML carrying both SRSF2^mut ASXL1^mut double mutations have a higher risk of acute leukemia transformation.
Humans ; Serine-Arginine Splicing Factors/genetics* ; Mutation ; Leukemia, Myelomonocytic, Chronic/genetics* ; Retrospective Studies ; Male ; Female ; Repressor Proteins/genetics* ; DNA-Binding Proteins/genetics* ; Dioxygenases ; Middle Aged ; Aged ; Proto-Oncogene Proteins/genetics*

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

A method was developed for determination of dilauryl thiodipropionate(DLTDP)in fried foods by coupling solid-phase extraction(SPE)pretreatment with reverse-phase liquid chromatography-tandem mass spectrometry(RPLC-MS/MS)detection.Samples were extracted with n-hexane as the solvent,purified using a neutral alumina SPE cartridge,and finally analyzed by RPLC-MS/MS.Quantitative analysis was performed using matrix-matched calibration curves combined with an external standard method under optimal experimental conditions.The results showed that DLTDP exhibited good linearity in the range of 2.0-50.0 μg/L,with a correlation coefficient(R2)≥0.999.The limit of detection(LOD)and the limit of quantification(LOQ)of the method were 0.15 mg/kg and 0.5 mg/kg,respectively.The mean recoveries at three fortification levels(0.5,1.0,and 200 mg/kg)in different samples ranged from 84.8%to 96.8%,with the relative standard deviations(RSDs)all less than 8.0%.The developed method was highly sensitive,accurate and reliable,and easy to operate,making it well suited for the routine quantitative analysis of DLTDP in fried foods.

Objective To evaluate the predictive value of dose-surface histogram(DSH)for radiation proctitis(RP)in prostate cancer(PCa)patients undergoing radiotherapy.Methods This prospective randomized controlled clinical trial included 380 PCa patients who underwent image-guided radiotherapy in the First Affiliated Hospital of Hebei Northern University from January 2018 to January 2023.Patients were randomly divided into observation group(n=200)and control group(n=180).The rectal dose distribution of patients in the two groups was evaluated by using DSH and dose-volume histogram(DVH),respectively.The receiver operating characteristic(ROC)curve was utilized to evaluate the predictive value of DSH for acute RP,with DVH serving as a reference.Results The difference was not statistically significant in clinical information such as age,KPS score,and body mass index(BMI)between the observation and control groups(P＞0.05),as well as in acute RP incidence(P＞0.05).There were significant differences in S40 and V40,S50 and V50,S60 and V60,S70 and V70,and S78 and V78 between the two groups(P＜0.05).S40,S50,V40,and V50 showed low efficacy(P＜0.001)in predicting acute RP at each level,with AUC≤0.700.S60 and V60 showed moderate efficacy(P＜0.001)in predicting acute RP at each level,with AUC 0.700-0.900.S70,S78,V70 and V78 showed high efficacy(P＜0.001)in predicting acute RP at each level,with AUC＞0.900.Conclusions The predictive value of DSH for rectal toxicity in patients with PCa is basically consistent with that of DVH.It is expected to become a novel and valuable tool for evaluating radiotherapy plans in the future.

Objective To compare the effect of preoperative CTA examination using group injection test and group injection tracking delay protocol on image quality in patients with transcatheter aortic valve implantation(TAVI)/transcatheter aortic valve replacement(TAVR).Methods A total of 43 patients who underwent TAVI/TAVR preoperative one-stop CTA examination in our hospital were randomly divided into two groups,and preoperative CTA examination was performed using group injection tracking and group injection test.After the scanning was completed,the aortic root sinus(C1),the proximal left and right coronary arteries(C2,C3),the ascending aorta(A1),the abdominal aorta(A2),the left and right femoral arteries(A3,A4),and the CT value and SD value of the paraspinal muscles of the same layer were measured;the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of the corresponding parts were calculated.The obtained data results were compared and analyzed from both subjective and objective aspects.Results There was no significant difference in image quality between the two groups in the aortic root sinus(C1),the proximal left and right coronary arteries(C2,C3),or the ascending aorta(A1)(P＞0.05).At the renal artery level,there was a significant difference in image quality between the abdominal aorta(A2)and the left and right femoral artery levels(A3,A4)(P＜0.05).Conclusion In the preoperative one-stop CTA examination of TAVI/TAVR patients,the CTA images below the renal artery level can obtain better image quality than the group injection tracking method.

ObjectiveTo analyze the expression of molecular marker affecting the prognosis of acute myeloid leukemia （AML） patients from bioinformatics database， thus providing an experimental basis for further exploration of a novel molecular marker for the prognosis of AML. MethodsThe prognostic data of 179 AML patients from The Cancer Genome Atlas （TCGA） database were examined for differential gene analysis and survival analysis. The bone marrow samples of 74 healthy individuals （HI） and 542 de novo AML patients in the dataset GSE13159 downloaded from the Gene Expression Omnibus （GEO） database were analyzed to detect the difference in the expression levels of differential target genes. Peripheral blood and bone marrow samples were collected from 18 de novo AML patients and 20 age- and gender-matched healthy controls， and real-time fluorescent quantitative PCR was used to validate the expression levels of the differential genes in the AML patients. ResultsBioinformatics data analysis showed that the optimal cut-off value of Homo sapiens NK2 homeobox 3 （NKX2-3） calculated by R language was 0.051. Survival analysis revealed a statistically poorer overall survival in de novo AML patients with high NKX2-3 expression than in those with low NKX2-3 expression （P = 0.0036）. NKX2-3 was highly expressed in patients with de novo AML than in HI and the difference was statistically significant （P < 0.001）. Real-time fluorescence quantitative PCR verified the expression levels of the NKX2-3 gene in AML patients and confirmed that compared with those in HI， in the de novo AML patients， NKX2-3-1 and NKX2-3-2 were highly expressed and were significantly correlated （P = 0.000， P = 0.000）. ConclusionNKX2-3 is highly expressed in de novo AML patients， and the AML patients with high NKX2-3 expression have poor overal survival. NKX2-3 may be closely related to the clinical outcome and prognosis of AML.

Objective To construct machine learning models that can be used to predict AUC fold change(FC)using a database of existing pharmacokinetic(PK)and drug-drug interaction(DDI)information,which can be used to explore the possibility of predicting existing drug interactions and to provide certain rational recommendations for clinical drug use.Methods PK data of DDIs and AUC fold change data were extracted from FDA-approved drug labels.Peptide and pharmacodynamic(PD)information related to drug interactions were retrieved through DrugBank,and PPDT identification of relevant peptide IDs was performed using Protein Resource(UniProt),and a matrix normalization code was used to generate multidimensional vector data that were easy to analysis.The effect of PPDT on the AUC,and the resulting multiplicity change was used as the dependent variable for machine learning model construction.The model with the smallest root mean square error(RMES)value was used for model construction to train a bagged decision tree(Bagged)prediction model.The models were tested using the trained models for some of the drug tests.The models were evaluated by reviewing the available literature findings on detection of drug interaction pairs and analyzing and comparing the predicted values.Results A total of 16 pairs of model drug pairs were tested for the effects of 16 drugs on tacrolimus,and it was found that the accuracy of the prediction of the presence or absence of drug interactions was 81.25％;the prediction results were classified according to the FDA standard classification of the strong and weak for the strength of drug interactions,and the results showed that the prediction of the strength of drug interactions,with a large deviation from the larger prediction was less.Conclusion The evaluation of the model to predict the presence or absence of drug interactions was general;however,after classifying the strengths and weaknesses of drug interactions,the prediction of drug interactions was better,and the prediction results indicated that the model prediction performance has a certain reference value for potential DDI assessment before clinical trials.

OBJECTIVE To explore the potential of Aconiti Kusnezoffii Radix in affecting diabetes mellitus type 2(T2DM) and the potential mechanism for T2DM related symptoms based on systematic pharmacology, bioinformatics, molecular docking and in vitro and in vivo experiments. METHODS The database was used to search the related chemical components of Aconiti Kusnezoffii Radix, predict the potential targets and intervene related diseases. The differential genes of T2DM relative healthy people were retrieved from GEO database, mapped with the action target of Aconiti Kusnezoffii Radix, and placed in DAVID database for biological function enrichment. The sensitivity of the target gene to T2DM was analyzed by one-way ANOVA, binary logistic regression analysis and ROC curve. The binding position and interaction force between chemical compounds of Aconiti Kusnezoffii Radix and target proteins were analyzed by molecular docking technology. The effect of Aconiti Kusnezoffii Radix and its chemical compounds on the expression of target protein was verified by T2DM model in vivo and in vitro. RESULTS Through database retrieval and analysis, 304 kinds of target related diseases(P value<0.05, FDR<0.05) were obtained, and T2DM with the highest degree value(Degree=59) was selected and analyzed. The 43 target genes were obtained from the intersection of differential genes in T2DM relatively healthy people and potential action targets of Aconiti Kusnezoffii Radix. A total of 9 genes with significant differences were obtained by one-way ANOVA, 5 meaningful genes were obtained by binary logistic regression analysis, and 3 genes with area under ROC curve AUC>0.5 were obtained. By molecular docking (+)-Isoboldine binds to proteins APEX1, CASP1 and CBFB, Napelline binds to proteins CBX1 and EHMT2 through different forces such as hydrogen bond interaction, ligand interaction, hydrophobic interaction, ionizability and electrostatic interaction, so as to increase the ability of ligands to target proteins. After 2 weeks of treatment with Aconiti Kusnezoffii Radix aqueous extract, Aconiti Kusnezoffii Radix may alleviated the symptoms of T2DM by improving peripheral neuropathy. Moreover, Aconiti Kusnezoffii Radix could affect the protein expression of APEX1, CASP1, CBFB, CBX1 and EHMT2 in rat liver tissue. The effect of (+)-Isoboldine and Napelline chemical compounds in Aconiti Kusnezoffii Radix on the target protein of the model in vitro was consistent with that in vivo. CONCLUSION It is preliminarily revealed that Aconiti Kusnezoffii Radix can be used as a potential therapeutic drug to improve T2DM peripheral neuropathy, which lays a theoretical foundation for the research and development of Chinese Mongolian medicine and the excavation of new target drugs for the treatment of T2DM.