Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

ObjectiveTo investigate the efficacy and safety of simplified diagnosis and treatment strategies and regimens in the treatment of patients with human immunodeficiency virus （HIV）/hepatitis C virus （HCV） co-infection. MethodsThis multicenter prospective real-world study was conducted among 198 patients with HIV/HCV co-infection who received hepatitis C treatment in 10 designated primary hospitals for hepatitis C in Yuxi， China from July 2023 to June 2024， and according to whether genotype detection was performed， they were divided into genotype detection group with 122 patients and non-genotype detection group with 76 patients. The patients were observed in terms of sustained virologic response at 12 weeks （SVR12）， safety， and liver biochemical parameters. Propensity score matching was used to match the two cohorts， using caliper matching （caliper value = 0.02） at a ratio of 1∶1. The independent-samples t test or the Wilcoxon signed-rank test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. ResultsAmong the 122 patients in the genotype detection group， 63.11% （77/122） had genotype 3， while the non-genotype detection group had 76 patients； both groups achieved an SVR12 rate of 100%. From baseline to 12 weeks after treatment， both groups had a significant increase in the serum level of albumin， significant reductions in the serum levels of total bilirubin， alanine aminotransferase， aspartate aminotransferase， and alpha-fetoprotein， and a significant reduction in FibroScan value （all P<0.05）. A total of 54 patients （27.27%） experienced at least one adverse reaction， and anemia was the most common adverse reaction （38 patients， 19.19%）， with an incidence rate of 50.00% （33/66） in patients with liver cirrhosis. HIV viral load remained <50 IU/mL before and after treatment， and there was no significant change in CD4⁺ T lymphocyte count after treatment （Z=-0.969， P=0.202）. Compared with the genotype detection group， the non-genotype detection group had a significantly shorter time from positive HCV RNA testing to treatment initiation （4±2 days vs 19±4 days， t=5.321， P<0.05） and significantly lower testing costs （618±97 yuan vs 789±129 yuan， t=3.661， P<0.05）. ConclusionFor patients with HIV/HCV co-infection， the simplified diagnosis and treatment strategies can achieve a relatively high SVR12 rate， improve biochemical indicators， and effectively reduce time cost and economic burden， with a favorable safety profile.

To explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging(MRI) radiomics combined with deep learning(DL) features for the preoperative noninvasive assessment of mismatch repair-deficient(MMRd) status in endometrial cancer(EC).

Objective To explore the predictive value of automated breast ultrasound(ABUS)features combined with Ki-67 in predicting pathological complete response(pCR)after neoadjuvant chemotherapy(NAC)in triple-negative breast cancer(TNBC).Methods A retrospective analysis was conducted on 127 female TNBC patients treated at Xijing Hospital,Air Force Medical University from March 2019 to December 2023.All patients underwent NAC and surgical treatment after ABUS examination.Based on postoperative pathological results,patients were divided into pCR group(n=60)and non-pathological complete response(npCR)group(n=67).Differences in various parameters before NAC were compared between the two groups.LASSO regression was used to identify independent factors influencing pCR after NAC in TNBC patients,and a predictive model was constructed using multivariate logistic regression.The prediction model was internally validated using the Bootstrap method(1000 resamples).The discriminative ability of the model was evaluated using receiver operating characteristic(ROC)curves,and the area under the curves(AUCs)of different prediction models were compared using De-long's test.The accuracy of the model was assessed using calibration curves,and the clinical benefit of the model was evaluated using clinical decision curve analysis(DCA).Results Significant differences were observed between two groups in terms of age,Ki-67,menopausal status,tumor type,posterior echo,coronal plane convergence sign,coronal plane skip sign,and coronal plane white wall sign before NAC(P<0.05).LASSO regression analysis showed that Ki-67,coronal plane convergence sign,and coronal plane white wall sign were independent influencing factors of pCR after NAC in TNBC patients(P<0.05).The AUC of the multivariate logistic regression model based on Ki-67 was 0.733(95%CI 0.646-0.819),the AUC of ABUS model was 0.777(95%CI 0.695-0.858),and the AUC of ABUS combined with Ki-67 model was 0.816(95%CI 0.741-0.890).De-long's test showed that the AUC of the combined model was higher than those of ABUS feature model and Ki-67 model,with statistically significant differences(P<0.05).There was no significant difference in the AUC between ABUS feature model and Ki-67 model(P=0.40).Hosmer-Lemeshow test indicated that the combined model had a good fit(P=0.304).Internal validation results showed that the combined model had a good stability with a consistency index(C-index)of 0.820(95%CI 0.726-0.879).The calibration curve demonstrated good consistency between the predicted and actual probabilities of the combined prediction model,and the DCA curve indicated that the model had favorable clinical benefit.Conclusion The combined ABUS feature and Ki-67 model can be used to predict the probability of pCR after NAC in TNBC patients,providing a reference for the formulation of clinical treatment plans in TNBC patients.

Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the"therapeutic module"of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Objective To observe right heart function in degenerative mitral regurgitation(DMR)and functional mitral regurgitation(FMR)patients based on two-dimensional speckle tracking imaging(2D-STI)and real-time three dimensional echocardiography(RT-3DE).Methods Thirty cases of DMR(DMR group),30 cases of(FMR group)and 30 healthy subjects(control group)were prospectively enrolled.Routine ultrasound parameters,right ventricular 2D-STI and RT-3DE parameters were compared among 3 groups and between each 2 groups.Results Significant differences of tricuspid annular plane systolic excursion(TAPSE),fractional area change(FAC),pulmonary artery systolic pressure(PASP),right ventricular end systolic volume(RVESV),right ventricular stroke volume(RVSV),right ventricular ejection fraction(RVEF),left ventricular ejection fraction(LVEF)and tricuspid regurgitation effective regurgitant orifice area(TR-EROA)were found among 3 groups(all P＜0.05).Compared with control group,TAPSE decreased but PASP increased in DMR and FMR groups(all P＜0.05),and significant differences of TAPSE,FAC,PASP,RVESV,RVSV,RVEF,LVEF and TR-EROA were noticed between DMR group and FMR group(all P＜0.05).Significant differences of right ventricular global longitudinal strain(RVGLS),right ventricular free wall longitudinal strain(RVFWLS),longitudinal strain of 3 segments of right ventricular free wall,as well as of right ventricular pulmonary artery(RVPA)coupling parameters were found among 3 groups and between each 2 groups(all P＜0.05).Significant differences of RT-3DE-RVESV,RT-3DE-RVSV and RT-3DE-RVEF were found among 3 groups(all P＜0.05)and between FMR group and the other 2 groups(all P＜0.05).Conclusion Compared with DMR,right heart function changed more significantly in FMR patients.

Objective To explore the factors influencing the uptake of 18F-FAPI-04 in normal liver tissue.Methods A total of 77 patients who had undergone 18F-FAPI-04 PET/CT scan were retrospectively enrolled.We collected their medical history,as well as laboratory indicators within one week before the imaging,including blood cell count,liver function and renal function indicators.The maximal standard uptake value(SUVmax)of the liver on 18F-FAPI-04 PET/CT images was manually measured,and the correlations between the SUVmax and the laboratory indicators were analyzed.With SUVmax as the dependent variable,multiple linear regression was performed to obtain independent predictors.Results SUVmax between the groups with or without smoking,drinking,chemotherapy,active tumor,or liver metastasis was not significantly different(P＞0.05).SUVmax had no significant correlation with blood cell count indexes(all P＞0.05),but was positively correlated with glutamic-pyruvic transaminase,γ-glutamyl transpeptidase,direct bilirubin,globulin,blood glucose level and creatinine(r=0.196,P=0.047;r=0.180,P=0.027;r=0.196,P=0.015;r=0.171,P=0.032;r=0.296,P＜0.001;r=0.270,P=0.001),and was negatively correlated with glomerular rate filtration(r=-0.209,P=0.009).Glucose level(β=0.100,P＜0.001),direct bilirubin(β=0.021,P＜0.001)and creatinine(β=0.003,P=0.018)were independent predictors of SUVmax.Conclusion Liver uptake of 18F-FAPI-04 is positively correlated with glutamic-pyruvic transaminase,γ-glutamyl transpeptidase,direct bilirubin,globulin,blood glucose levels and creatinine,but negatively correlated with glomerular rate filtration.Glucose level,direct bilirubin and creatinine are independent predictors of 18F-FAPI-04 uptake by the liver.

To construct a recombinant fowl adenovirus 4(FAdV-4)expressing the Cap protein of goose astrovirus genotype 2(GoAstV-2),the expression cassette of Cap gene was inserted into the natural 1 966 bp deletion region of the FAdV-4 genome in the infectious clone p15A-cm-FAdV4-HNJZ.The resulted recombinant plasmid p15A-cm-FAdV4-HNJZ-Cap/GoAstV-2 was linearized with restriction enzyme and transfected into chicken hepatoma cell line(LMH)to rescue the recombinant FAdV-4 expressing the Cap protein of GoAstV-2,rF Ad V4-Cap/GoAstV-2.After 15 passages in LMH cells,the recombinant rFAdV4-Cap/GoAstV-2 was identified by PCR using primers flanking the insertion site of the Cap gene expression cassette and using viral genome DNA extracted from rFAdV4-Cap/GoAstV-2 infected LMH cells as template.LMH cells were in-fected with 15th passage rFAdV4-Cap/GoAstV-2 and indirect immunofluorescence was performed with a polyclonal antibody against Cap protein as the primary antibody.Western blot was carried out with lysates of rFAdV4-Cap/GoAstV-2 infected LMH cells.The in vitro replication dynamic of the 15th passage of the rFAdV4-Cap/GoAstV-2 was also investigated in LMH cells.The results demonstrated that the Cap gene of GoAstV-2 was presented in the genome of the recombinant vi-rus rF AdV4-Cap/Go Ast V-2,and could be expressed stably.The prepared recombinant virus in this study will lay a foundation for developing inactivated bivalent vaccine candidate against co-in-fection of FAdV-4 and GoAstV-2 in goose.