AIM: To investigate the potential inhibitory effect of pterostilbene on the endothelial-to-mesenchymal transition(EndMT)induced by high glucose conditions in human retinal microvascular endothelial cells(HRMECs).METHODS: The optimal concentration of pterostilbene for treating HRMECs was determined using the CCK-8 assay, with 12.5 and 25 μmol/L concentrations selected for subsequent experiments. Four experimental groups were established: control group, high glucose group, high glucose combined with 12.5 μmol/L pterostilbene treatment group, and high glucose combined with 25 μmol/L pterostilbene treatment group. The expression levels of HDAC7 and EndMT-associated markers were detected via Western blot analysis. Cell migration ability was assessed using Transwell migration assays and scratch wound healing tests, while vasculogenic capability was evaluated through tube formation assays.RESULTS: The CCK-8 assay revealed that pterostilbene at a concentration of 22.07 μmol/L inhibited 50% of cell viability in HRMECs. Western blot analysis demonstrated that compared with the control group, the expression levels of HDAC7, ZEB1, Vimentin, and Snail were significantly upregulated in HRMECs cultured in high glucose(all P<0.01), while the expressions of VE-cadherin and CD31 were significantly reduced(all P<0.01). Compared to the high glucose group, the treatment with 12.5 and 25 μmol/L pterostilbene significantly reduced the expression of HDAC7, ZEB1, Vimentin, and Snail under high glucose conditions(all P<0.01). Notably, 25 μmol/L pterostilbene enhanced the expression of VE-cadherin and CD31(all P<0.01). Scratch wound healing tests revealed that HRMECs treated with high glucose exhibited a significantly increased cell migration rate compared to the control group(P<0.05), while the application of 25 μmol/L pterostilbene significantly suppressed HRMECs migration under high glucose conditions(P<0.01). Transwell migration assays demonstrated that the cell migration rate in the high glucose group was significantly higher than that in the control group(P<0.01), with cell migration rate markedly reduced following treatment with both of 12.5 and 25 μmol/L pterostilbene(all P<0.01). The tube formation assay revealed that the ability of HRMECs to form tubular structures was significantly enhanced under high glucose conditions(P<0.01), and both 12.5 and 25 μmol/L of pterostilbene effectively inhibited this effect(all P<0.01).CONCLUSION: Pterostilbene can inhibit HDAC7 expression, suppress EndMT-mediated migration of HRMECs, and impair tube formation under high-glucose conditions.

AIM: To investigate the potential inhibitory effect of pterostilbene on the endothelial-to-mesenchymal transition(EndMT)induced by high glucose conditions in human retinal microvascular endothelial cells(HRMECs).METHODS: The optimal concentration of pterostilbene for treating HRMECs was determined using the CCK-8 assay, with 12.5 and 25 μmol/L concentrations selected for subsequent experiments. Four experimental groups were established: control group, high glucose group, high glucose combined with 12.5 μmol/L pterostilbene treatment group, and high glucose combined with 25 μmol/L pterostilbene treatment group. The expression levels of HDAC7 and EndMT-associated markers were detected via Western blot analysis. Cell migration ability was assessed using Transwell migration assays and scratch wound healing tests, while vasculogenic capability was evaluated through tube formation assays.RESULTS: The CCK-8 assay revealed that pterostilbene at a concentration of 22.07 μmol/L inhibited 50% of cell viability in HRMECs. Western blot analysis demonstrated that compared with the control group, the expression levels of HDAC7, ZEB1, Vimentin, and Snail were significantly upregulated in HRMECs cultured in high glucose(all P<0.01), while the expressions of VE-cadherin and CD31 were significantly reduced(all P<0.01). Compared to the high glucose group, the treatment with 12.5 and 25 μmol/L pterostilbene significantly reduced the expression of HDAC7, ZEB1, Vimentin, and Snail under high glucose conditions(all P<0.01). Notably, 25 μmol/L pterostilbene enhanced the expression of VE-cadherin and CD31(all P<0.01). Scratch wound healing tests revealed that HRMECs treated with high glucose exhibited a significantly increased cell migration rate compared to the control group(P<0.05), while the application of 25 μmol/L pterostilbene significantly suppressed HRMECs migration under high glucose conditions(P<0.01). Transwell migration assays demonstrated that the cell migration rate in the high glucose group was significantly higher than that in the control group(P<0.01), with cell migration rate markedly reduced following treatment with both of 12.5 and 25 μmol/L pterostilbene(all P<0.01). The tube formation assay revealed that the ability of HRMECs to form tubular structures was significantly enhanced under high glucose conditions(P<0.01), and both 12.5 and 25 μmol/L of pterostilbene effectively inhibited this effect(all P<0.01).CONCLUSION: Pterostilbene can inhibit HDAC7 expression, suppress EndMT-mediated migration of HRMECs, and impair tube formation under high-glucose conditions.

Primary pulmonary nuclear protein of the testis (NUT) midline carcinoma (NMC) is a rare and highly aggressive thoracic malignancy that poses significant diagnostic and therapeutic challenges in clinical practice. This tumor is characterized by its heterogeneous clinical presentations and poor prognosis, often evading accurate initial diagnosis. In this study, we present two cases of primary pulmonary NMC treated with an integrated therapeutic approach combining anti-angiogenic agents, platinum-based chemotherapy, and radiotherapy. This multimodal strategy achieved survival durations of 32 and 13 months, respectively, surpassing the currently reported median survival of advanced NMC. Through a systematic literature review of reported cases, we have summarized the currently used diagnostic methods and treatment modalities for NMC. Our findings suggest that multimodal therapy incorporating anti-angiogenic treatment may offer superior clinical outcomes compared to conventional monotherapy regimens, particularly for patients who are not eligible for surgery. This comprehensive investigation enhances our understanding of NMC management by elucidating diagnostic pitfalls through histopathological correlation and proposing an effective therapeutic combination that demonstrates improved survival outcomes. By providing valuable insights into the diagnosis and treatment of primary pulmonary NMC, we hope to contribute to the development of more effective strategies for managing this rare and aggressive malignancy.
Humans ; Angiogenesis Inhibitors/therapeutic use* ; Carcinoma/therapy* ; Combined Modality Therapy ; Lung Neoplasms/diagnosis* ; Nuclear Proteins ; Oncogene Proteins ; Neoplasm Proteins

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

In the process of conducting implementation research on health service issues, stakeholders' preference for contents related to evidence-based practice (EBP) and implementation strategies is closely related to whether EBP can be effectively implemented.However, multiple preference assessment methods exist, each with their own strengths, weaknesses, and application scenarios, which makes it challenging for researchers to select appropriate and effective preference assessment methods. This paper aims to review the origins, characteristics, and application scenarios of commonly used preference assessment methods, with the hope of providing valuable reference and lessons for domestic scholars to select and apply appropriate preference assessment methods in implementation research.

In the process of conducting implementation research on health service issues, stakeholders' preference for contents related to evidence-based practice (EBP) and implementation strategies is closely related to whether EBP can be effectively implemented.However, multiple preference assessment methods exist, each with their own strengths, weaknesses, and application scenarios, which makes it challenging for researchers to select appropriate and effective preference assessment methods. This paper aims to review the origins, characteristics, and application scenarios of commonly used preference assessment methods, with the hope of providing valuable reference and lessons for domestic scholars to select and apply appropriate preference assessment methods in implementation research.

Objective:To investigate the differences and applicability of free silica detection methods of different crystal forms in dust, and to provide a basis for the selection of various methods.Methods:From December 2021 to June 2022, dust samples from 20 enterprises in different industries in 18 cities in Henan Province were randomly selected as the investigation objects. X-ray diffraction (XRD) method was used to analyze the samples and classify the samples. Based on GBZ/T 192.4-2007 "Determination of Dust in the Air of Workplace-Part 4: Content of Free Silica in Dust", pyrophosphate method and infrared spectrophotometry were used for quantitative determination. The measured results were analyzed by paired sample t test to evaluate the advantages and disadvantages of the two methods and their applicable scope. Results:The XRD results of 20 dust samples could be divided into α, β, γ crystal types and the mixed type of α and γ. There was no significant difference between pyrophosphate method and infrared spectrophotometry ( P=0.180). The pyrophosphate method results of β, γ and α, γ mixed crystalline free silica were significantly higher than those of infrared spectrophotometry, and the difference was statistically significant ( P<0.001) . Conclusion:Pyrophosphate method and infrared spectrophotometry are suitable for α-type free silica, while pyrophosphate method is suitable for β, γ and α, γ mixed crystalline free silica.

The development and change patterns as well as the disease course management of multiple ground-glass nodules(GGNs)in the lungs are currently hotspots and difficulties in clinical lung cancer research.Understanding the latest advancements in the natural history of multiple GGNs is crucial for grasping the disease variation patterns and formulat-ing management strategies.Meanwhile,utilizing advanced methods such as intelligent follow-up management platforms makes the long-term standardized management of GGNs possible.Therefore,this article provides an overview of the latest research advancements on the natural history of multiple GGNs and new experience in GGNs management.

Objective:To investigate the differences and applicability of free silica detection methods of different crystal forms in dust, and to provide a basis for the selection of various methods.Methods:From December 2021 to June 2022, dust samples from 20 enterprises in different industries in 18 cities in Henan Province were randomly selected as the investigation objects. X-ray diffraction (XRD) method was used to analyze the samples and classify the samples. Based on GBZ/T 192.4-2007 "Determination of Dust in the Air of Workplace-Part 4: Content of Free Silica in Dust", pyrophosphate method and infrared spectrophotometry were used for quantitative determination. The measured results were analyzed by paired sample t test to evaluate the advantages and disadvantages of the two methods and their applicable scope. Results:The XRD results of 20 dust samples could be divided into α, β, γ crystal types and the mixed type of α and γ. There was no significant difference between pyrophosphate method and infrared spectrophotometry ( P=0.180). The pyrophosphate method results of β, γ and α, γ mixed crystalline free silica were significantly higher than those of infrared spectrophotometry, and the difference was statistically significant ( P<0.001) . Conclusion:Pyrophosphate method and infrared spectrophotometry are suitable for α-type free silica, while pyrophosphate method is suitable for β, γ and α, γ mixed crystalline free silica.