AIM: To screen and analyze the influencing factors of myopia among primary and secondary school students and establish a predictive model to provide ideas for the prevention and control measures of myopia among children and adolescents.METHODS:A total of 1 759 primary and secondary school students from 2 primary schools, 2 junior high schools, 2 senior high schools and 1 vocational high school in the urban area of Qingdao were sampled by means of stratified cluster sampling in September 2023. Vision screening and a questionnaire survey on influencing factors were carried out based on machine learning algorithms. The screening and determination were mainly conducted in accordance with the Standard Logarithmic Visual Acuity Chart(GB/T11533-2011)and the Specifications for Screening Myopia in Children and Adolescents. The influencing factors of myopia were analyzed and a prediction model was developed based on the machine learning algorithms LASSO in combination with XGBoost, and visualization was achieved through an interactive Nomogram. Statistical analysis was performed using R statistical software version 4.3.3.RESULTS:The screening prevalence of myopia among primary and secondary school students in the urban area of Qingdao was 70.61%(1 242 cases). The optimal predictive variables for screening were grade, gender, whether parents were myopic, daily indoor sedentary time, appropriate distance between eyes and books during reading and writing, daily sleep time, distance between eyes and TV screen when watching TV/playing video games exceeding 3 meters, the playground during breaks, total duration of tutorial classes, how often eyes are rested during near work, daily computer usage time, and average daily homework time after school, totaling 12 influencing factors. The AUCs of the training set and test set were 0.770(95%CI:0.751-0.789)and 0.732(95%CI:0.714-0.750), respectively.CONCLUSION: A machine learning-based prediction model was developed and validated to predict the risk of myopia onset in primary and secondary school students, accompanied by effective visualization techniques.

ObjectiveThis study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy. MethodsFirst, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance. ResultsExperimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model’s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments. ConclusionThe integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.

The codon usage bias of the Marburg virus(MARV)genome and related influencing factors were investigated to provide a reference for preparation of vaccines and specific antibodies.In total,93 MARV coding sequences were retrieved from the GenBank database and analyzed with EMBOSS,Codon W,and SPSS 26.0,while plots were generated with SigmaPlot14.0 and GraphPad Prism 9.5.The effective number of codons(ENC values)of the MARV genomes for each protein ranged from 49.84 to 60.92,and the codon adaptation index was close to 0.7.Relative synonymous codon usage showed that GCA,AGA,AAU,GA A,GGA,AUU,CCU,and AC A were common high-frequency codons of seven proteins,and the codons with fa-voritism mostly ended with A/U.Neutral,ENC-Plot,and PR2 parity analyses indicated that the MARV codon usage prefer-ence was dominated by natural selection,followed by mutational pressure and various other factors.Comparisons of MARV gene codon usage frequency with four expression systems(Escherichia coli,yeast,human,and baculovirus)found differences in the codon usage frequency between MARV and E.coli.MARV codon usage favoritism was shaped by a combination of fac-tors,with natural selection playing a dominant role.The E.coli expression system was most suitable for in vitro expression of MARV proteins.

This study was aimed at understanding the trends in,and scope of,severe fever with thrombocytopenia syndrome(SFTS)in Nanjing,analyzing the spatial distribution pattern,detecting high incidence cluster areas and key popula-tions,and scientifically guiding prevention and control strategies and measures.We obtained data on SFTS cases from 2010 to 2023 in Nanjing from the China Disease Control and Prevention Information System,and described the time,popu-lation,and spatial distribution characteristics.We used joinpoint regression to calculate the annual percentage change(APC)in incidence,then used FleXScan spatial clustering scan analysis to explore spatial clustering areas at the street level.A total of 507 SFTS cases were reported from 2010 to 2023 in Nanjing.The APC was 31.8％(95％CI:22.5％-41.9％,P＜0.001),and the reported incidence in 2023 was 1.42/100 000(134 cases).The seasonal indices from May to August were 2.7,2.1,3.0,and 1.3,respectively,accounting for 76.1％of the total cases.The median age was 66(IQR:55,73)years,which gradually increased from 59 years in 2010-2011 to 68 in 2022-2023(P＜0.001);94.1％of cases were in individuals 45 years or older.Farmers,homemakers/unemployed individuals,and retirees accounted for 90.1％.The epidemic area increased from 11 streets in four districts in 2010-2011 to 58 streets in 11 dis-tricts in 2022-2023.Except for 2012-2013,global spatial autocorrelation analysis showed positive Moran's I values(0.224-0.526,P＜0.001),and FlexScan scan indicated that several streets in Lishui District and Jiangning District were the most likely clusters.Four streets in Pukou District were the secondary clusters from 2018 to 2023,and three streets in Luhe District in 2022-2023 were the secondary clusters(all P＜0.05).The reported incidence of SFTS in Nanjing showed a rapid upward trend,with spread of epidemic areas.The spatial distribution pattern was clustered.Strengthened training in diagnosis and treatment technology and detection ability of medical institutions,surveillance in high-incidence areas,tracing of case flow,and health education of tick and disease prevention knowledge are recommended.

Objective:To investigate the relationship between mutated genes and clinical features in patients with essential thrombocythemia(ET).Methods:The clinical data of 69 patients with ET from October 2018 to March 2022 were retrospectively analyzed.According to driver mutation type,patients were divided into JAK2 group,CALR group and triple-negative group.The sex,age,cardiovascular risk factors,thrombosis,splenomegaly,routine blood test and coagulation status of patients in three groups were analyzed.Results:Among 69 ET patients,46 cases were associated with JAK2 mutation,14 cases with CALR mutation,8 cases with triple-negative mutation,and one with MPL gene mutation.There were no significant differences in age and sex among the three groups(P＞0.05).The highest thrombotic rate was 26.09％(12/46)in JAK2 group,then 12.5％(1/8)in triple-negative group,while no thrombotic events occurred in CALR group.The incidence of splenomegaly was the highest in JAK2 group(34.78％),while no splenomegaly occurred in triple-negative group.The white blood cell(WBC)count in JAK2 group was(9.00±4.86)× 109/L,which was significantly higher than(6.03±2.32)× 109/L in CALR group(P＜0.05).The hemoglobin(Hb)and hematocrit(HCT)in JAK2 group were(148.42±18.79)g/L and(0.44±0.06)％,respectively,which were both significantly higher than(131.00±15.17)g/L and(0.39±0.05)％in triple-negative group(P＜0.05).The platelet(PLT)in JAK2 group was(584.17±175.77)× 109/L,which was significantly lower than(703.07±225.60)× 109/L in CALR group(P＜0.05).The fibrinogen(Fg)in JAK2 and triple-negative group were(2.64±0.69)g/L and(3.05±0.77)g/L,respectively,which were both significantly higher than(2.24±0.47)g/L in CALR group(P＜0.05,P＜0.01).The activated partial thromboplastin time(APTT)in triple-negative group was(28.61±1.99)s,which was significantly decreased compared with(31.45±3.35)s in CALR group(P＜0.05).Conclusions:There are differences in blood cell count and coagulation status among ET patients with different driver gene mutations.Among ET patients,JAK2 mutation is most common.Compared with CALR group,the thrombotic rate,WBC and Fg significantly increase in JAK2 group,while PLT decrease.Compared with triple-negative group,the incidence of splenomegaly and HCT significantly increase.Compared with CALR group,Fg significantly increases but APTT decreases in triple-negative group.

Objective:To investigate the effects of selinexor,a inhibitor of nuclear export protein 1(XPO1)on the proliferation inhibition and apoptosis of Kasumi-1 cells in acute myeloid leukemia(AML).Methods:MTS method was used to detect the inhibitory effect of different concentrations of selinexor on the proliferation of Kasumi-1 cells at different time points.The apoptosis rate and cell cycle changes after treatment with different concentration of selinexor were detected by flow cytometry.Results:Selinexor inhibited the growth of Kasumi-1 cells at different time points in a concentration-dependent manner(r24 h=0.7592,r48 h=0.9456,and r72 h=0.9425).Selinexor inhibited Kasumi-1 cells growth in a time-dependent manner(r=0.9057 in 2.5 μmol/L group,r=0.9897 in 5 μmol/L group and r=0.9994 in 10 μmol/L group).Selinexor could induce apoptosis of Kasumi-1 cells in a dose-dependent manner(r=0.9732),and the apoptosis of Kasumi-1 cells was more obvious with the increase of drug concentration.The proportion of G0/G1 phase was significantly increased and the proportion of S phase was significantly decreased after the treatment of Kasumi-1 cells by selinexor.With the increase of drug concentration,the proportion of Kasumi-1 cells cycle arrest in G0/G1 phase was increased and the cell synthesis was decreased.Conclusion:Selinexor can promote the death of tumor cells by inhibiting Kasumi-1 cells proliferation,inducing apoptosis and blocking cell cycle.

Objective:To analyze the genes related to platelet activation in essential thrombocythemia (ET)based on transcriptome sequencing technology (RNA-seq ),and to explore the potential targets related to ET thrombosis. Methods:Blood samples from ET patients and healthy individuals were collected for RNA-seq,and differentially expressed lncRNAs,miRNAs,and mRNAs were selected to construct a lncRNA-miRNA-mRNA regulatory network. Differential mRNAs in the regulatory network were enriched and analyzed using Gene Ontology (GO ) and Kyoto Encyclopedia of Genes and Genomes (KEGG).The real-time PCR method was applied to validate differential mRNAs on crucial signaling pathways.Results:A total of 32 lncRNAs (3 up-regulated,29 down-regulated),16 miRNAs (8 up-regulated,8 down-regulated),and 35 mRNAs (27 up-regulated,8 down-regulated)were identified as differentially expressed.Among them,5 lncRNAs,12 miRNAs,and 19 mRNAs constituted the regulatory network.KEGG enrichment analysis showed that the differential mRNAs were related to the platelet activation signaling pathway,and there were 6 differential mRNAs related to platelet activation,namely F2R,ITGA2B,ITGB1,ITGB3,PTGS1,and GP1 BB,which were all up-regulated in their expression.RT-PCR results showed that the expression of five mRNAs including F2R,ITGA2B,ITGB1,ITGB3,and GP1BB were upregulated in ET patients compared with healthy subjects,and consistent with RNA-seq results,while PTGS1 expression was not significantly different.Conclusion:Differential mRNAs in ET patients are related to the platelet activation pathway,and F2R,ITGA2B,ITGB1,ITGB3,and GP1BB mRNAs may serve as novel targets associated with platelet activation in ET.

Objective:To investigate the clinical and genetic characteristics of a case of primary ciliary dyskinesia(PCD).Methods:We collected the clinical data on a case of PCD treated in the Department of Reproductive Medicine of Linyi People's Hospi-tal in July 2020,detected the genes of the patient by whole-exome sequencing(WES),verified the candidate mutations by Sanger se-quencing,and predicted the protein structure of the mutant gene by SWISS-MODEL.Results:The proband was found with the clini-cal phenotypes of chronic rhinitis,bronchiectasis,visceral transposition and male infertility.WES revealed a homozygous frameshift variation of c.12890dup(p.N4297Kfs*13)in exon 74 of the DNAH5 gene,which led to the premature termination of polypeptide chain synthesis and affected the gene function.SWISS-MODEL prediction showed that some of the amino acid residues were deleted af-ter mutation,resulting in a 3D conformational change of the protein.This variation was not recorded in the ClinVar,gnomAD and OMIM databases and,according to the relevant guidelines of the American College of Genetics and Genomics,was classified as a path-ogenic variation(PVS1+PM2_P+PM3_P).Conclusion:The homozygous variation of the DNAH5 gene c.12890dup(p.N4297Kfs*13)may be the cause of the clinical phenotype of this case of PCD,and the above findings have enriched the variation spectrum of the DNAH5 gene.

Objective:To assess the usage of the robot-assisted core-needle biopsy for the bone tumors, moreover to compare its outcomes with the manual technique.Methods:A retrospective study was conducted from February 2019 to February 2021, the medical records of the patients with bone lesions that had received core-needle biopsy were collected. There were 57 males and 45 females, the age was 45.9 (10~79) years. Eight patients received robot-assisted biopsy, whereas 94 patients underwent C-arm/ CT guided biopsy, the recorded data included operational duration, aspirational direction adjustment, etc. The pathological diagnosis reports of the biopsy specimens and the operational specimens were compared.Results:The diagnosis outcomes included metastases (33 cases), osteosarcoma (12 cases), chondrosarcoma (12 cases), giant cell tumor of bone (12 cases), fibrous dysplasia (7 cases), chronic osteomyelitis (7 cases), lymphoma (4 cases), multiple myeloma (4 cases), chronic fracture (3 cases), chondroblastoma (2 cases), pleomorphic undifferentiated sarcoma (2 cases), leiomyosarcoma (1 case), and Langerhans cell histiocytosis (1 case). Eighty-seven cases (85.29 %) lesions were found in the limbs, whereas 15 cases (14.71%) were in the axial locations. Compared with the manual group, the robot-assisted group had more axial locations: 7/8 vs. 11.70%(11/94), P<0.01; fewer aspirational direction adjustment: (0.4 ± 0.1) times vs. (3.1 ± 1.5) times, P<0.01 ; longer operational duration: (48.8 ± 8.8) min vs. (29.6 ± 6.0) min, P<0.01. There were no statistical differences between the two groups regarding the sex, age, pathological fracture, diagnostic accuracy, open biopsy rate and complications ( P>0.05). Conclusions:The robot-assisted core-needle biopsy is a reliable technique, it helps decrease the operational difficulty. The usage of this technique is recommendable for the bone lesions with great difficulty for biopsy, such as the minimal bone tumors and the lesions in the spine and the pelvis.

Objective To explore the value of 11C-methionine(MET)PET/CT radiomics model for evaluating isocitrate dehydrogenase1(IDH1)status of glioblastoma.Methods Data of 157 patients with glioblastoma who underwent 11C-MET PET/CT examination,including 68 cases of IDH1 mutation and 89 cases of IDH1 wild-type were retrospectively analyzed.The patients were divided into training set(n=125)and validation set(n=32)at the ratio of 8:2.Based on PET/CT images,lesions ROI were delineated and radiomics features were extracted and screened to establish radiomics models with logistic regression(LR),support vector machine(SVM)and decision trees(DT),respectively.Meanwhile,the nomogram based on patients'age and radiomics features was drawn.The efficacy of radiomics models and clinical-radiomics nomogram for evaluating IDH1 status were comparatively observed.Results The area under the curve(AUC)of DT radiomics model for evaluating IDH1 status of glioblastoma in training set was 0.910,higher than that of LR(0.697)and SVM(0.698)models(both P＜0.05).In validation set,the AUC of DT model for evaluating IDH1 status of glioblastoma was 0.805,which was higher than that of LR model(0.740)and clinical-radiomics nomogram(0.704)(both P＜0.05).Conclusion 11C-MET PET/CT radiomics model based on DT was helpful for evaluating IDH1 status of glioblastoma.