Objective To explore the value of deep learning(DL)model based on grayscale ultrasound for predicting asymptomatic advanced chronic liver disease(cACLD).Methods Totally 258 patients with asymptomatic compensatory chronic liver diseases were retrospectively included,among them 117 with F3 or F4 stage liver fibrosis were classified into cACLD group,while 141 with F1 or F2 stage liver fibrosis were taken as non-cACLD group.The patients were divided into training set(n=180,including 82 cases of cACLD and 98 cases of non-cACLD)and validation set(n=78,including 35 cases of cACLD and 43 cases of non-cACLD)at the ratio of 7∶3.Univariate and multivariate logistic regression were used to screen independent clinical predictors of cACLD and construct a clinical model.Based on liver grayscale ultrasound,optimal DL features were extracted and screened,and Resnet50 network was adopted as framework,na?ve Bayes classifier was used to construct DL model,and a combined model was constructed based on clinical model and DL model.The efficacy and clinical value of each model for predicting asymptomatic cACLD were evaluated.Results Age,gamma-glutamyl transferase and platelet count were all independent clinical predictors of cACLD,and a clinical model was constructed.Totally 38 optimal DL features were screened to build a DL model.The AUC of combined model in training set and validation set was 0.950 and 0.740,of DL model was 0.944 and 0.737,respectively,being not significantly different(both P＞0.05)but all higher than that of clinical model(0.667 and 0.573,all P＜0.05).Taken 0.59-0.90 as the threshold,the net benefits of combined model in both training and validation sets were higher than that of other models.Conclusion DL model based on grayscale ultrasound could be used to effectively predict asymptomatic cACLD.Combining with clinical characteristics might improve clinical net benefit of this model.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

Purpose To construct a comprehensive model of deep learning features and clinical features based on renal ultrasound for early identification of renal impairment in the pregnant women with preeclampsia.Materials and Methods The information of 279 pregnant women in the First Affiliated Hospital of Guangxi Medical University from January 2018 to June 2023 were retrospectively collected,and all pregnant women were divided the into preeclampsia group(151 cases)and normal group(128 cases).The dataset was randomly divided into a training set(195 samples)and a testing set(84 samples)at a ratio of 7∶3.Based on ultrasound images,the deep learning convolutional neural networks Resnet152 was used to extract deep learning features.The non-zero coefficient features were selected from the deep learning features by the least absolute shrinkage and selection operator,and the K-nearest neighbor algorithm was used to establish the deep learning model.Then,the same classifier model was used to construct a comprehensive model based on clinical data.The receiver operating characteristic curve was used to evaluate the prediction effect.To address the interpretability visualization of models using gradient_weighted class activation mapping and SHapley Additive exPlanations(SHAP)values.Results The area under the curve of the composite model was 0.964(95%CI 0.940-0.988)in the training cohort and 0.899(95%CI 0.835-0.963)in the test cohort.SHAP analysis showed that deep learning features contributed the highest value in the prediction model.Conclusion The comprehensive model based on deep learning combined with clinical features of renal ultrasound can be used to identify renal impairment in normal pregnancy and preeclampsia pregnant women at an early stage,which is conducive to early clinical intervention.

Objective To explore the value of deep learning(DL)model based on grayscale ultrasound for predicting asymptomatic advanced chronic liver disease(cACLD).Methods Totally 258 patients with asymptomatic compensatory chronic liver diseases were retrospectively included,among them 117 with F3 or F4 stage liver fibrosis were classified into cACLD group,while 141 with F1 or F2 stage liver fibrosis were taken as non-cACLD group.The patients were divided into training set(n=180,including 82 cases of cACLD and 98 cases of non-cACLD)and validation set(n=78,including 35 cases of cACLD and 43 cases of non-cACLD)at the ratio of 7∶3.Univariate and multivariate logistic regression were used to screen independent clinical predictors of cACLD and construct a clinical model.Based on liver grayscale ultrasound,optimal DL features were extracted and screened,and Resnet50 network was adopted as framework,na?ve Bayes classifier was used to construct DL model,and a combined model was constructed based on clinical model and DL model.The efficacy and clinical value of each model for predicting asymptomatic cACLD were evaluated.Results Age,gamma-glutamyl transferase and platelet count were all independent clinical predictors of cACLD,and a clinical model was constructed.Totally 38 optimal DL features were screened to build a DL model.The AUC of combined model in training set and validation set was 0.950 and 0.740,of DL model was 0.944 and 0.737,respectively,being not significantly different(both P＞0.05)but all higher than that of clinical model(0.667 and 0.573,all P＜0.05).Taken 0.59-0.90 as the threshold,the net benefits of combined model in both training and validation sets were higher than that of other models.Conclusion DL model based on grayscale ultrasound could be used to effectively predict asymptomatic cACLD.Combining with clinical characteristics might improve clinical net benefit of this model.

Purpose To construct a comprehensive model of deep learning features and clinical features based on renal ultrasound for early identification of renal impairment in the pregnant women with preeclampsia.Materials and Methods The information of 279 pregnant women in the First Affiliated Hospital of Guangxi Medical University from January 2018 to June 2023 were retrospectively collected,and all pregnant women were divided the into preeclampsia group(151 cases)and normal group(128 cases).The dataset was randomly divided into a training set(195 samples)and a testing set(84 samples)at a ratio of 7∶3.Based on ultrasound images,the deep learning convolutional neural networks Resnet152 was used to extract deep learning features.The non-zero coefficient features were selected from the deep learning features by the least absolute shrinkage and selection operator,and the K-nearest neighbor algorithm was used to establish the deep learning model.Then,the same classifier model was used to construct a comprehensive model based on clinical data.The receiver operating characteristic curve was used to evaluate the prediction effect.To address the interpretability visualization of models using gradient_weighted class activation mapping and SHapley Additive exPlanations(SHAP)values.Results The area under the curve of the composite model was 0.964(95%CI 0.940-0.988)in the training cohort and 0.899(95%CI 0.835-0.963)in the test cohort.SHAP analysis showed that deep learning features contributed the highest value in the prediction model.Conclusion The comprehensive model based on deep learning combined with clinical features of renal ultrasound can be used to identify renal impairment in normal pregnancy and preeclampsia pregnant women at an early stage,which is conducive to early clinical intervention.

Objective This study aims at establishing a teaching catalog and content for breast rare dis-eases and developing the syllabus for the breast rare disease using integrated multimodality of case-/problem-/resource-based learning(CBL+PBL+RBL).Methods By conducting bibliometrics co-occurrence analysis,we collected 6291 articles on breast rare disease published from January,1975 to June,2024.Additionally,we re-trieved the Textbook on Rare Diseases,the Catalog of Chinese Rare Disease,and Second Batch of Rare Dis-ease Catalog and then decided the teaching content.Results From 16,387 keywords,1000(6.1％)keywords were identified through co-occurrence analysis,including 50(0.3％)candidate diseases.These were classified into three categories:rare primary breast diseases,rare genetic mutation-related diseases associated with breast cancer,and rare systemic multi-system diseases involving the breast.From the candidate list,20(0.1％)rare primary breast diseases were further selected for their notable clinical teaching significance,and significant multi-systemic diseases affecting the breast,whether related to gene mutations or not.Teaching plans were draf-ted using a diversified parallel teaching approaches,taking into account the characteristics of different diseases and the focus of different teaching methods.Conclusions This study initiated the development of the teaching content for breast rare diseases and developed the teaching syllabus using the CBL+PBL+RBL integrated multi teaching model and targeting each rare breast disease for the critical point for teaching.

Purpose To explore the feasibility of targeted diagnosis and localization of prostate cancer via background free differential ultrasound molecular imaging based on prostate-specific membrane antigen (PSMA) targeted ultrasound nanobubbles (NB). Materials and Methods Targeted PSMA-NB and non-targeted NB were constructed. The targeting ability of PSMA-NB on human prostate tumor 22RV1 cells (PSMA positive expression) and PC-3 cells (PSMA negative expression) was determined in vitro. Ten nude mouse models of human prostate tumor 22RV1 cells (n=5) and PC-3 cells (n=5) were constructed. PSMA-NB was injected into the rat tail vein,and in-situ blasting was performed. Ultrasound molecular images before and after blasting were collected,using destruction supplement post-processing technology to obtain and compare the differential ultrasound molecular imaging effects between the two groups. Results The particle size of PSMA-NB and NB were (363.7±24.4) nm and (236.0±55.2) nm,with statistical difference (t=3.19,P=0.007),respectively. Cell targeting results showed that PSMA-NB only adhered to the nucleus with positive PSMA-expression. Animal experiments indicated that the differential ultrasonic molecular images of PSMA positive expression group only showed the highly enhanced area of contrast agent at the tumor site,with no background noise. Conclusion Background free differential ultrasound molecular images can be used for precise targeted diagnosis and localization of PSMA positive prostate cancer,which is constructed based on PSMA targeted ultrasound nanobubbles.

Purpose To explore the feasibility of targeted diagnosis and localization of prostate cancer via background free differential ultrasound molecular imaging based on prostate-specific membrane antigen (PSMA) targeted ultrasound nanobubbles (NB). Materials and Methods Targeted PSMA-NB and non-targeted NB were constructed. The targeting ability of PSMA-NB on human prostate tumor 22RV1 cells (PSMA positive expression) and PC-3 cells (PSMA negative expression) was determined in vitro. Ten nude mouse models of human prostate tumor 22RV1 cells (n=5) and PC-3 cells (n=5) were constructed. PSMA-NB was injected into the rat tail vein,and in-situ blasting was performed. Ultrasound molecular images before and after blasting were collected,using destruction supplement post-processing technology to obtain and compare the differential ultrasound molecular imaging effects between the two groups. Results The particle size of PSMA-NB and NB were (363.7±24.4) nm and (236.0±55.2) nm,with statistical difference (t=3.19,P=0.007),respectively. Cell targeting results showed that PSMA-NB only adhered to the nucleus with positive PSMA-expression. Animal experiments indicated that the differential ultrasonic molecular images of PSMA positive expression group only showed the highly enhanced area of contrast agent at the tumor site,with no background noise. Conclusion Background free differential ultrasound molecular images can be used for precise targeted diagnosis and localization of PSMA positive prostate cancer,which is constructed based on PSMA targeted ultrasound nanobubbles.

ObjectiveTo analyze the microbial diversity in the rhizosphere soil of Gastrodia elata with different yields and explore the influence of soil microorganisms on the yield of G. elata. MethodThe experiment adopted the 16S DNA and ITS high-throughput sequencing technologies to study the diversity of the bacterial and fungal community in the rhizosphere soil of G. elata with high yield （GC） and low yield （DC）. ResultProteobacteria， Firmicutes， and other unidentified Bacteria were dominant in the rhizosphere soil of G. elata. The dominant rhizosphere fungi were Ascomycota， Basidiomycota， and Mortierellomycota. There was no significant difference in microbial community abundance in the high-yield and low-yield rhizosphere soil of G. elata， but there was a significant difference in species composition. Thirty-eight microbes such as Bradyrhizobium， Schleiferilactobacillus， and Archaeorhizomyces were gathered in large numbers in the high-yield rhizosphere soil， and thirty microbes such as Fusarium， Coprinellus， and Nitrosotalea were gathered in large numbers in the low-yield rhizosphere soil. At the level of genus and species， there were six different species in the high-yield and low-yield rhizosphere soil of G. elata， among which Russula mariae， Archeaeorhizomyces， and Ilyonectria were gathered in the high-yield rhizosphere soil of G. elata， while Nitrosotalea， Coprinellus disserminatus， and Fusarium were gathered in the low-yield rhizosphere soil of G. elata. ConclusionThere are different microorganisms in the rhizosphere soil of G. elata with different yields， and it is speculated that these microorganisms are related to the yields of G. elata. The research results are expected to provide a vital theoretical basis for the follow-up study of the high yield of G. elata.

Objective: Depression was common during coronavirus disease-2019 (COVID-19) pandemic, while the association of perceived stress with depression among vaccinated healthcare workers has not been investigated. This study aimed to address this issue. Methods: We included a total of 898 fully vaccinated healthcare workers during the outbreak of severe acute respiratory syndrome coronavirus 2 Delta variant in Nanjing, 2021. Depression was ascertained by Patient Health Questionnaire-9, with a cut-off score of ≥5 indicative of mild-to-severe depression. Perceived stress, resilience, and compassion fatigue were assessed by Perceived Stress Scale-10, Resilience Scale-25, and Professional Quality of Life Scale version-5, respectively. Logistic regression analyses were used to estimate the odds ratio (OR) and 95% confidence interval (CI), along with subgroup and mediation analyses. Results: The prevalence of mild-to-severe depression was 41.1% in vaccinated healthcare workers. The odd of mild-to-severe depression was increased with higher perceived stress. Compared with vaccinated healthcare workers with the lowest tertile of perceived stress, those with the highest tertile had increased odds of mild-to-severe depression by 120% (OR 2.20, 95% CI 1.46 to 3.31) after multivariable-adjustment. However, perceived stress was not associated with mild-to-severe depression in vaccinated healthcare workers with strong resilience, but was in those with weak resilience (pinteraction=0.004). Further analysis showed that compassion fatigue mediated the relationship between perceived stress and mild-to-severe depression, with a mediating effect of 49.7%. Conclusion Perceived stress was related to an increased odd of mild-to-severe depression in vaccinated healthcare workers during COVID-19 pandemic, and this relationship might be explained by compassion fatigue.