ObjectiveTo identify the pathogen species responsible for the wilt disease of Tetradium ruticarpum in Chongqing， investigate there biological characteristics， and screen effective fungicides， so as to provide a theoretical basis for disease control in production. MethodsThe pathogen was isolated via the tissue culture method. Pathogenicity was verified according to Koch's postulates. The pathogen was identified based on morphological characteristics and multi-gene phylogenetic analysis. The mycelial growth rate method was used for biological characterization of the pathogen and fungicide screening. ResultsThe pathogen colonies were nearly circular with irregular edges， white， short， velvety aerial hyphae， and pale purple undersides. Macroconidia were colorless， sickle-shaped， with 3-5 septa， while microconidia were transparent， elliptical， aseptate or with 1-2 septa. Multi-gene phylogenetic analysis showed that the pathogen clustered in the same clade as Fusarium fujikuroi with 100% support， which， combined with morphological characteristics， identified the pathogen causing wilt of T. ruticarpum in Chongqing as F. fujikuroi. The optimal conditions for the mycelial growth of F. fujikuroi were mung bean agar （MBA） with glucose as the carbon source， beef extract and yeast powder as nitrogen sources， 28 ℃， pH 7.0， and alternating light/dark conditions. The optimal conditions for sporulation were potato dextrose agar （PDA） with glucose as the carbon source， beef extract as the nitrogen source， 28 ℃， pH 7.0， and complete darkness. Among chemical fungicides， phenazine-1-carboxylic acid exhibited the strongest inhibitory effect on F. fujikuroi. Shenqinmycin and tetramycin were the most effective bio-fungicides. ConclusionThis study is the first to report F. fujikuroi as the causal agent of wilt disease in T. rutaecarpa. The chemical fungicide phenazine-1-carboxylic acid and the bio-fungicides shenqinmycin and tetramycin showed strong inhibitory effects against F. fujikuroi.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

Objective To investigate the value of first platelet count(PLT)to respiratory rate(RR)ratio(PLT/RR)on admission in the diagnosis and prognosis of secondary sepsis in pneumonia patients.Methods A total of 100 patients with pneumonia admitted to the First Affiliated Hospital of Army Medical University from May 2023 to August 2024 were selected as subjects.According to the presence or absence of pneumonia sepsis,they were divided into sepsis group(63 cases)and non-sepsis group(37 cases).The secondary sepsis in pneumonia pa-tients were followed up continuously for 30 d.According to the survival situation,they were divided into sur-vival group(54 cases)and death group(9 cases).PLT in peripheral blood was measured,vital signs were col-lected on the first day of admission,and PLT/RR was calculated.The receiver operating characteristic curve was used to evaluate the predictive value of PLT,RR and PLT/RR for secondary sepsis in pneumonia pa-tients.The systemic inflammatory response syndrome(SIRS)score,modified early warning score(MEWS)and quick sequential organ failure assessment(qSOFA)score on admission were calculated,and the clinical predictive value of SIRS score,MEWS and qSOFA score was compared.Results PLT and PLT/RR in sepsis group were lower than those in non-sepsis group(P<0.000 1),RR was higher than that in non-sepsis group(P<0.01).The area under the curve(AUC,95%CI)of PLT,RR and PLT/RR were 0.858(0.785-0.931),0.693(0.589-0.796)and 0.902(0.843-0.962),respectively.The optimal cut-off values were 146.5×109/L,20.5 per minute and 8.075,respectively.The specificity were 8.1%,83.8%and 2.7%,respec-tively.The sensitivity was 33.3%,50.8%and 30.2%,respectively.Compared with the non-sepsis group,the sepsis group had a significantly higher SIRS score(P<0.001),a significantly lower MEWS(P<0.000 1),and no significant difference in qSOFA score between the two groups(P>0.05).The AUC(95%CI)of SIRS score,MEWS and qSOFA score in predicting secondary sepsis in pneumonia patients were 0.717(0.616-0.818),0.748(0.650-0.846)and 0.505(0.389-0.622),respectively.The optimal cut-off values were 4.5,2.5 and 1.5 points,respectively.The specificity were 91.9%,2.7%and 100.0%,respectively.The sensitivity was 42.9%,33.3%and 6.3%,respectively.PLT and PLT/RR in death group were lower than those in sur-vival group(P<0.05),RR was higher than that in survival group(P<0.05).Secondary sepsis in pneumonia patients were followed up for 30 d,Kaplan-Meier survival curve showed that patients with PLT≤138.5×109/L had a lower 30 d survival rate(P=0.007 8).Patients with RR>24.5 per minute had a lower 30 d sur-vival rate(P=0.016 1).Patients with PLT/RR≤6.375 had a lower 30 d survival rate(P=0.002 3).Conclu-sion PLT/RR can be used as a biological index to predict secondary sepsis in pneumonia patients,which is better than SIRS score,MEWS and qSOFA score,and the prognosis of secondary sepsis in pneumonia patients with low PLT/RR is worse.

Objective To investigate the relationship between triple motif protein(TRIM)3,TRIM44 and clinicopathological parameters and prognosis in human papillomavirus(HPV)positive cervical cancer tissues.Methods The cancer tissues and adjacent normal cervical tissues(≥2 cm from the resection margin of the cancer)of 158 patients with HPV positive cervical cancer admitted to the hospital from February 2019 to Feb-ruary 2021 were selected,and the cancer tissues of 100 patients with HPV negative cervical cancer admitted to the hospital during the same period were selected.The positive expression rates of TRIM3 and TRIM44 in cancer tissues of HPV positive cervical cancer patients and their adjacent normal cervical tissues,and cancer tissues of HPV negative cervical cancer patients were detected and compared by immunohistochemistry.The relationship between the positive expression rate of TRIM3 and TRIM44 and the clinicopathological parame-ters of HPV positive cervical cancer patients was analyzed.The 3-year cumulative survival rate of patients with negative/positive TRIM3 expression and negative/positive TRIM44 expression were analyzed by Kaplan-Meier survival curve.The influencing factors of the prognosis of HPV positive cervical cancer patients were analyzed by COX regression model.Results Compared with adjacent normal cervical tissues and HPV nega-tive cervical cancer tissues,the positive expression rate of TRIM3 in HPV positive cervical cancer tissues was significantly decreased(P＜0.05),and the patients expression rate of TRIM44 was significantly increased(P＜0.05).Compared with the patients with FIGO stage Ⅰ A-Ⅰ B and no lymph node metastasis,the pa-tients with FIGO stage Ⅱ A and lymph node metastasis,the positive rate of TRIM3 expression decreased sig-nificantly(P＜0.05),while the positive rate of TRIM44 expression increased significantly(P＜0.05).A total of 3 patients were lost during the 3-year follow-up among 158 HPV positive cervical cancer patients,and 155 patients were finally followed up.Kplan-Meier survival curve showed that,the 3-year cumulative survival rate in TRIM3 negative expression group was significantly lower than that in TRIM3 positive expression group(P＜0.05).The 3-year cumulative survival rate in TRIM44 positive expression group was significantly lower than that in TRIM44 negative expression group(P＜0.05).COX regression analysis showed that lymph node metastasis,FIGO stage,negative expression of TRIM3 and positive expression of TRIM44 were risk factors for the prognosis of HPV positive cervical cancer patients(P＜0.05).Conclusion The expression of TRIM3 is low and the expression of TRIM44 is high in HPV positive cervical cancer tissues,which is relate to lymph node metastasis,FIGO stage and decrease 3-year cumulative survival rate.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Under the background of rapid development of artificial intelligence(AI),this paper systematically proposes AI-based education(AIBE).It empowers the teaching process,learning process,research process,and teaching management with AI,and constructs an AI-based educational paradigm,including AI-based teaching(AIBT),AI-based learning(AIBL),AI-based re-search(AIBR),and AI-based management(AIBM).Taking the AI course of immunology teaching as an example,this paper deeply analyzes the practices and explorations of implementing AIBT,AIBL,AIBR and AIBM based on the AI course,so as to accelerate the promotion of the transformation of the fourth generation of medical education.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*