ObjectiveThis study aimed to construct a neural network model integrating readily available clinical data and two-dimensional （2D） conventional ultrasound images to evaluate the severity of steatotic liver disease （SLD）. MethodsClinical data and ultrasound images were retrospectively collected from 649 patients who underwent ultrasound attenuation imaging （ATI）. Using ATI as the reference standard， patients were divided into four groups： normal （S0）， mild （S1）， moderate （S2）， and severe （S3） SLD. Data were randomly divided into training and validation sets at an 8∶2 ratio. A contrastive language-image pre-training （CLIP） model was utilized to extract features from both clinical and imaging data， which were then used to train random forest （RF） and multilayer perceptron （MLP） models. The diagnostic performance of these multimodal models was compared their single-modal counterparts in the validation set. ResultsThe MLP model outperformed the RF model （AUC［95%CI］： S0 = 0.96 ［0.93-0.99］， S1 = 0.99 ［0.96-1.00］， S2 = 0.75 ［0.64-0.82］， S3 = 0.88 ［0.81-0.93］）. Furthermore， the multimodal fusion model demonstrated significantly superior performance compared to single-modal models （clinical data only： AUC［95%CI］ of 0.89 ［0.81-0.95］， 0.69 ［0.52-0.79］， 0.64 ［0.43-0.67］ and 0.80 ［0.73-0.90］ for S0-S3， respectively； image data only： AUC［95%CI］ of 0.91 ［0.86-0.96］， 0.89 ［0.67-0.89］， 0.69 ［0.58-0.78］ and 0.86 ［0.82-0.94］）. Metrics including precision， recall， F1-score， F2-score， confusion matrices， and loss function learning curves further confirmed that multimodal data fusion significantly improved the predictive capability. ConclusionThe proposed CLIP-based MLP multimodal model can effectively and automatically grade SLD severity. This demonstrates the significant advantage of integrating clinical and ultrasound data， providing an accurate and reliable adjunctive tool for the clinical management of chronic SLD.

Objective:To identify the key epithelial cell characteristics that can accurately diagnose eosinophilic chronic sinusitis with nasal polyps（ECRSwNP） through nasal brush sampling and comparing with the pathological results of nasal polyp tissue sections. Methods:Ninety-one patients underwent surgery in the Ophthalmology and ENT Department of the Second People's Hospital of Longgang District, Shenzhen, from January 2022 to July 2024 were selected. The cohort comprised 58 males and 33 females（mean age: 41.4 years; range: 12.0-71.0）. The clinical characteristics of the patients, including gender, age, disease duration, smoking and drinking history, asthma history, subjective symptoms, sinus CT, and nasal endoscopy scores, were recorded. Nasal brush sampling of nasal polyps and inferior turbinate mucosa was performed before surgery to obtain cytological specimens, and nasal polyp tissues were collected during surgery. The demographic and clinical characteristics of patients with eosinophilic and non-eosinophilic nasal polyps were compared, as well as the relationship between nasal brush cytology of nasal polyps and inferior turbinate and nasal polyp histopathology. Statistical analysis was performed using SPSS 23.0 software. Results:Among the 91 patients, no significant differences were observed between ECRSwNP and NECRSwNP patients in terms of age, gender, smoking status, alcohol consumption, and disease duration. The nasal brush cell population in ECRSwNP patients was more likely to contain eosinophils（P<0.001） and less likely to contain lymphocytes and plasma cells（P<0.001）. Additionally, the ciliated cells in ECRSwNP patients exhibited larger widths（P=0.036）, shorter cilium lengths（P<0.001）, and more disordered arrangements（P<0.001） compared to NECRSwNP patients. In nasal brush cells from the inferior turbinate, ECRSwNP patients also showed shorter cilium lengths（P<0.001） and shorter cilia（P=0.024） compared to NECRSwNP patients. Conclusion:There are significant differences in obtaining epithelial cytological information from nasal polyps or inferior turbinates through nasal brush sampling between ECRSwNP and NECRSwNP patients.
Humans ; Male ; Female ; Middle Aged ; Adult ; Nasal Polyps/complications* ; Sinusitis/complications* ; Aged ; Chronic Disease ; Adolescent ; Nasal Mucosa/pathology* ; Young Adult ; Rhinitis/complications* ; Eosinophilia/pathology* ; Child ; Eosinophils/pathology* ; Rhinosinusitis

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To explore the biological function and downstream mechanism of ETS1 in glioma.Methods Bioinformatics and immunohistochemistry were used to analyze the differential expression characteristics of ETS1 in gliomas;qRT-PCR was employed to detect the expression level of ETS1 mRNA and lncRNA X-inactive specific transcript(XIST).CCK-8 and 5-ethyl-2′-deoxyuridine experiments were conducted to detect cell growth.Western blot was used to detect the expression of apoptosis-related proteins(Bax,Bak,Bcl-2).PROMO database was utilized to predict the binding sites between ETS1 and XIST promoter.Dual-luciferase reporter gene assay and chromatin immunoprecipitation-quantitative polymerase chain reaction assays were performed to verify the binding relationship between ETS1 and the XIST promoter region.cBioPortal database was used to analyze the correlation between the expression of ETS1 mRNA and XIST in glioma tissues.Results The expression levels of ETS1 mRNA and protein were significantly upregulated in glioma(P<0.05).The depletion of ETS1 significantly inhibited the proliferation of glioma cells and promoted cell apoptosis(P<0.05).ETS1 could target and bind with the XIST promoter and promote the expression of XIST(P<0.05).The overexpression of XIST reversed the effects of ETS1 on the proliferation of glioma cells and the promotion of cell apoptosis(P<0.05).Conclusion ETS1 is highly expressed in glioma tissues.It could promote the expression of lncRNA XIST,boost the proliferation of glioma cells,and inhibit cell apoptosis.

This study was aimed to detect the heavy metal residues of medicinal leeches, in order to understand the current market circulation situation of medicinal leeches, and to analyze possible factors which may cause heavy metal pollution, and to provide references for the standardized safe cultivation of medicinal leeches. Inductively coupled plasma mass spectrometry (ICP-MS) technique was applied to detect heavy metal residues in medicinal leeches. The results showed that medicinal leech samples tested for heavy metal content severely exceeded the standard, which caused a great threat to the safety of medication. It was conclude that more attentions should be paid on factors for causing heavy metal accumulation within medicinal leeches during the breeding process. The related department should also list safety limits explicitly and separately for animal drugs such as medicinal leech during the development of quality standards of Chinese materia medica.

OBJECTIVE:To explore the trend of cooper enrichment in hirudinidae influenced by culture environment. MET-HODS:The soil containing low-content,medium-content and high-content of cooper groups(30.00,60.00,90.00 μg/g)and water culture control group were set up. Hirudinidae leech were culture for 60 d,and sampled every 15 days. ICP-MS techniques was used to determined and compared the contents of cooper in W. pigra and soil. RESULTS:In first 15 days,the contents of cooper in leech from 4 groups increased greatly,compared with before;in the following 15 days,the content of cooper kept stable in high-content group while decreased in other 3 groups;in the 30-45 day,the contents of cooper increased rapidly in 4 groups,and those of low-content group and high-content group reached the peak in this experiment;in the last 15 days,the contents of cooper in control group and high-content group increased continuously,while those of low-content and medium-content groups decreased to some extent. Compared with before,the contents of cooper in leech from control group,low-content,medium-content and high-content groups increased by 292%,186%,293%,464% respectively;those of soil from latter 3 groups increased by 81.12%,35.98% and 21.28% respectively. CONCLUSIONS:The content of cooper in leech increase with time,and is positively correlated with the content of cooper in soil. It is suggested to control the content of cooper in hirudinidae through controlling cul-ture environment when hirudinidae are cultured as medicinal material,in order to meet the quality standard of heavy metal in medic-inal material.

OBJECTIVETo explore effects of iron overload on hematopoiesis in mice with bone marrow injury and its possible mechanism (s).

METHODSC57BL/6 mice were divided into control, iron, irradiation, irradiation+iron groups. The iron-overloaded model of bone marrow injury was set up after mice were exposed to the dose of 4 Gy total body irradiation and (or) were injected iron dextran intraperitoneally. Iron overload was confirmed by observing iron deposits in mice and bone marrow labile iron pool. Additionally, the number of peripheral blood and bone marrow mononuclear cells and the frequency of erythroid cells and myeloid cells were counted and hematopoietic function was assessed.

RESULTS(1)Iron overload occurred by bone marrow biopsy and flow cytometry analysis. (2)Compared with control group, the number of platelets [(801.9±81.2)×10⁹/L vs (926.0±28.2)×10⁹/L] and BMMNC and the frequency of erythroid cells and myeloid cells decreased. Moreover, hematopoietic colony forming units and single-cell cloning counts decreased significantly in irradiation group (P<0.05). (3)Compared with irradiation group, the number of platelets [(619.0±60.9)×10⁹/L vs (801.9±81.2)×10⁹/L] and the frequency of erythroid cells and myeloid cells decreased; moreover, hematopoietic colony forming units and single-cell cloning counts decreased significantly in irradiation+iron group (P<0.05). (4)Compared with irradiation group, ROS level increased by 1.94 fold in BMMNC, 1.93 fold in erythroid cells and 2.70 fold in myeloid cells, respectively (P<0.05).

CONCLUSIONThe dose of 4 Gy total body irradiation caused bone marrow damage and iron overload based on this injury model, which could damage bone marrow hematopoietic function aggravatingly. And further study found that iron overload was closely related to increased ROS level in BMMNC. The findings would be helpful to further study the injury mechanism of iron overload on the hematopoiesis of bone marrow.