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.

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 develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Objective To explore the feasibility of a deep learning model based on early fundus lesions without manual segmentation in pan-retinal images for predicting diabetic kidney disease(DKD)and evaluating the enhancing effects of different binocular fusion strategies.Methods A retrospective cohort of 353 patients with type 2 diabetes mellitus(T2DM)admitted to the Endocrinology Department of Jiangdu People's Hospital Affiliated to Yangzhou University between December 2022 and March 2024 was analyzed.Patients were divided into DKD(n=114)and non-diabetic kidney disease(NDKD)(n=239)group based on the presence of DKD.First,a U-Net-based pre-trained automatic segmentation model was developed to process panoramic fundus retinal images.Subsequently,left and right eye deep learning models were constructed using ResNet152 under a five-fold cross-validation framework(70%training,30%validation).Three binocular fusion strategies were implemented:result fusion,feature fusion,and image fusion models.Model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC).DeLong test was used to compare AUC differences among models,while net reclassification index(NRI)and decision curve analysis(DCA)were used to assess clinical utility.Results Six prediction models were developed:clinical parameter model,left fundus model,right fundus model,binocular image fusion model,binocular result fusion model,and binocular feature fusion model.The Transformer-based binocular feature fusion model achieved the highest AUC in both training and validation sets(0.864 and 0.658,respectively).DeLong tests revealed significant AUC superiority of the Transformer model over the other five models in the training set(all P<0.001),though no significant differences were observed in the validation set(all P>0.05).NRI analysis showed negative values for all comparisons with the Transformer model(training set:-0.255,-0.244,-0.289,-0.426,-0.163;validation set:-0.060,-0.016,-0.028,-0.105,-0.033,respectively),indicating its optimal predictive performance.DCA further demonstrated greater net benefit for the Transformer-based fusion model.Conclusions The deep learning model constructed using early fundus lesions without manual segmentation in pan-retinal images can predict DKD.The Transformer-based fusion strategy present the best performance,providing a novel approach for further optimization and development of tools to predict DKD in the future.

Objective:To explore the potential profile characteristics of cancer-related worries in patients after surgery for early-stage lung cancer, and the influencing factors of different categories, provide reference for patients to formulate individualized rehabilitation programs and psychological intervention measures.Methods:A cross-sectional survey method was used to conveniently select patients who received outpatient follow-up after lung cancer surgery at Shanghai Pulmonary Hospital Affiliated to Tongji University from October 2022 to October 2023 as the survey subjects. The general information questionnaire, the Chinese version of Brief Cancer-related Worry Inventory, the Chinese version of the MD Anderson Symptom Inventory, Brief Illness Perception Questionnaire, 10-item Connor Davidson Resilience Scale and Medical Coping Modes Questionnaire were examined. Latent profile analysis was performed on the cancer-related worry scores of lung cancer surgery patients, and its influencing factors were explored by binary Logistic regression analysis.Results:A total of 302 patients after lung cancer surgery were included, including 111 males and 191 females, aged 18-83(52.73 ± 13.07) years, and the Chinese version of the Brief Cancer-related Worry Inventory scored 380.00 (130.00, 720.00) points. The cancer-related worry of patients after lung cancer surgery could be divided into two potential profile categories: "high worry type" (138 patients accounted for 45.70%) and "low worry type" (164 patients accounted for 54.30%). Symptom burden ( OR=1.055, 95% CI 1.039-1.072), illness perception ( OR=1.190, 95% CI 1.127-1.256), resilience ( OR=0.933, 95% CI 0.886-0.983), and coping modes of confrontation ( OR=0.857, 95% CI 0.757-0.971) and acceptance-resignation ( OR=1.247, 95% CI 1.050-1.481) were influencing factors for grouping cancer related worry profiles (all P<0.05). Conclusions:There was significant heterogeneity in the level of cancer-related worries among patients after surgery for early-stage lung cancer. It is recommended that medical staff provide targeted continuity care measures based on the characteristics of worries of different categories of patients to improve patients' postoperative mental health and quality of life.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Objective To explore the feasibility of a deep learning model based on early fundus lesions without manual segmentation in pan-retinal images for predicting diabetic kidney disease(DKD)and evaluating the enhancing effects of different binocular fusion strategies.Methods A retrospective cohort of 353 patients with type 2 diabetes mellitus(T2DM)admitted to the Endocrinology Department of Jiangdu People's Hospital Affiliated to Yangzhou University between December 2022 and March 2024 was analyzed.Patients were divided into DKD(n=114)and non-diabetic kidney disease(NDKD)(n=239)group based on the presence of DKD.First,a U-Net-based pre-trained automatic segmentation model was developed to process panoramic fundus retinal images.Subsequently,left and right eye deep learning models were constructed using ResNet152 under a five-fold cross-validation framework(70%training,30%validation).Three binocular fusion strategies were implemented:result fusion,feature fusion,and image fusion models.Model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC).DeLong test was used to compare AUC differences among models,while net reclassification index(NRI)and decision curve analysis(DCA)were used to assess clinical utility.Results Six prediction models were developed:clinical parameter model,left fundus model,right fundus model,binocular image fusion model,binocular result fusion model,and binocular feature fusion model.The Transformer-based binocular feature fusion model achieved the highest AUC in both training and validation sets(0.864 and 0.658,respectively).DeLong tests revealed significant AUC superiority of the Transformer model over the other five models in the training set(all P<0.001),though no significant differences were observed in the validation set(all P>0.05).NRI analysis showed negative values for all comparisons with the Transformer model(training set:-0.255,-0.244,-0.289,-0.426,-0.163;validation set:-0.060,-0.016,-0.028,-0.105,-0.033,respectively),indicating its optimal predictive performance.DCA further demonstrated greater net benefit for the Transformer-based fusion model.Conclusions The deep learning model constructed using early fundus lesions without manual segmentation in pan-retinal images can predict DKD.The Transformer-based fusion strategy present the best performance,providing a novel approach for further optimization and development of tools to predict DKD in the future.

Objective:To explore the potential profile characteristics of cancer-related worries in patients after surgery for early-stage lung cancer, and the influencing factors of different categories, provide reference for patients to formulate individualized rehabilitation programs and psychological intervention measures.Methods:A cross-sectional survey method was used to conveniently select patients who received outpatient follow-up after lung cancer surgery at Shanghai Pulmonary Hospital Affiliated to Tongji University from October 2022 to October 2023 as the survey subjects. The general information questionnaire, the Chinese version of Brief Cancer-related Worry Inventory, the Chinese version of the MD Anderson Symptom Inventory, Brief Illness Perception Questionnaire, 10-item Connor Davidson Resilience Scale and Medical Coping Modes Questionnaire were examined. Latent profile analysis was performed on the cancer-related worry scores of lung cancer surgery patients, and its influencing factors were explored by binary Logistic regression analysis.Results:A total of 302 patients after lung cancer surgery were included, including 111 males and 191 females, aged 18-83(52.73 ± 13.07) years, and the Chinese version of the Brief Cancer-related Worry Inventory scored 380.00 (130.00, 720.00) points. The cancer-related worry of patients after lung cancer surgery could be divided into two potential profile categories: "high worry type" (138 patients accounted for 45.70%) and "low worry type" (164 patients accounted for 54.30%). Symptom burden ( OR=1.055, 95% CI 1.039-1.072), illness perception ( OR=1.190, 95% CI 1.127-1.256), resilience ( OR=0.933, 95% CI 0.886-0.983), and coping modes of confrontation ( OR=0.857, 95% CI 0.757-0.971) and acceptance-resignation ( OR=1.247, 95% CI 1.050-1.481) were influencing factors for grouping cancer related worry profiles (all P<0.05). Conclusions:There was significant heterogeneity in the level of cancer-related worries among patients after surgery for early-stage lung cancer. It is recommended that medical staff provide targeted continuity care measures based on the characteristics of worries of different categories of patients to improve patients' postoperative mental health and quality of life.

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.

In order to understand nurses’ willingness to participate in humanistic nursing training and its influencing factors, and provide reference for managers to understand the current situation and improve nurses’ enthusiasm for humanistic nursing training. The convenience sampling method was used to investigate 23 707 nurses in 28 provinces (autonomous regions and municipalities directly under the central government) through a self-designed questionnaire distributed on the Internet. The results showed that 98.1% of nurses thought that participating in humanistic nursing related training was helpful to clinical work, but only 88.6% of the respondents were willing to participate in humanistic nursing training. Thirty factors were analyzed from four aspects of basic characteristics of individuals, cognitive relevant experience and organizational atmosphere. Fifteen factors had significant significance in binary Logistic regression analysis (P<0.05). Among them, the factors that had a positive impact on training willingness were: marriage, education, professional title, post establishment, agree with humanistic care is the basic duty of a nurse praised, experience of being praised at work, family support, rapport with patients, passion of colleagues to participate in training, sustained high-quality care demonstration activities, join the humanistic care related organization, hospital reimbursement of training expenses (OR value of 6.559~1.113, P<0.001). The OR value of humanistic nursing as a nurse’s responsibility was 6.559 and the 95%CI was 5.585~7.702. The factors that hindered nurses from participating in training were: work occupied most of time and energy, think humanistic nursing is abstract and difficult to understand, think the mastered humanistic knowledge and skills meet the needs of work (OR value of 0.657~0.722, P<0.001). Through the analysis, it is considered that nurses have a extremely consistent high recognition of the significance of humanistic nursing training, but their willingness to receive training is affected by many factors such as individual experience, cognitive attitude and organizational atmosphere. In order to realize nurses’ high recognition of humanistic nursing training to high enthusiasm of behavior, the aspects of individual cognition and organizational atmosphere must be discussed.