Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

This study reports on three patients with Shwachman-Diamond syndrome (SDS) who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) at the First Affiliated Hospital of Zhejiang University School of Medicine. Based on relevant literature, the clinical manifestations and genetic mutation characteristics of SDS were summarized, and the efficacy and timing of allo HSCT for such patients were explored. Three SDS patients were all male, with transplant ages of 32, 33, and 32 years old, respectively. All three patients were diagnosed in childhood. Case 1 presented with anemia as the initial clinical manifestation, which gradually progressed to a decrease in whole blood cells; Case 2 and 3 both present with a decrease in whole blood cells as the initial clinical manifestation. Case 1 and 3 have intellectual disabilities, while case 3 presents with pancreatic steatosis and chronic pancreatitis. All three patients have short stature. Three patients all detected heterozygous mutations in the SBDS: c.258+2T>C splice site. The family members of the three patients have no clinical manifestations of SDS. All three patients were treated with a reduced dose pre-treatment regimen (Fludarabine+Busulfan+Me-CCNU+Rabbit Anti-human Thymocyte Globulin). Case 1 and case 2 underwent haploid hematopoietic stem cell transplantation, while case 3 underwent unrelated donor hematopoietic stem cell transplantation. Case 1 was diagnosed with myelodysplastic syndrome transforming into acute myeloid leukemia before transplantation, but experienced early recurrence and death after transplantation; Case 2 is secondary implantation failure, dependent on platelet transfusion; Case 3 was removed from medication maintenance treatment after transplantation, and blood routine monitoring was normal.

Objective:To explore the clinical application value of a deep learning algorithm-based ultrasound biomicroscopy (UBM) image analysis system for primary angles-closure glaucoma (PACG) anterior located ciliary body.Methods:A diagnostic test study was conducted.A total of 2 132 UBM images from 726 eyes of 378 PACG patients who underwent UBM examination were collected at Renmin Hospital of Wuhan University from August 2022 to December 2023.The dataset was divided into a training set of 1 599 images and a test set of 533 images, and a deep learning algorithm was employed to construct a model.An additional 334 UBM images from 101 eyes of 69 PACG patients treated at Huangshi Aier Eye Hospital were selected to conduct external testing.A separate set of another 110 UBM images were selected for a human-machine competition to compare the accuracy and speed between anterior located ciliary body evaluation system and three senior ophthalmologists.Furthermore, eight junior ophthalmologists assessed the 110 UBM images independently without and with the assistance of the model, and the differences between the two evaluations were analyzed to assess the assisstance effect of the model.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109).Results:The model achieved an accuracy of 93.43% for anterior located ciliary body identification in the internal test set, with a sensitivity of 84.30% and a specificity of 97.78%.The model also performed well on the external test set with an accuracy of 92.81%.In the human-machine competition, the model's accuracy was comparable to that of the senior ophthalmologists and outperformed two of the three senior ophthalmologists.The average total time of the three senior ophthalmologists was 726.73 seconds, approximately 12.47 times longer than the model's 58.30 seconds.With model assistance, the diagnostic accuracy of the eight junior ophthalmologists was 86.71%, which was significantly higher than 76.25% without model assistance ( χ2=-7.550, P<0.001).And the image evaluation time was (714.91±213.82)seconds, which was significantly lower than (987.90±238.56)seconds without model assistance ( t=2.774, P<0.05). Conclusions:The UBM image analysis system based on a deep learning algorithm demonstrates high accuracy in diagnosing anterior located ciliary body in PACG and provides a strong support for the UBM image recognition training of junior ophthalmologists.

Objective: To construct a core competency training indicator system for high-level public health talents using the Delphi method, so as to provide insights into optimization of high-level public health talents training. Methods: Based on review of publications pertaining to core competency training for high-level public health talents, in combination with public health priorities and development needs in China, a preliminary indicator system was constructed. Twenty public health experts were invited for two rounds of Delphi expert consultation. Indicators were screened based on the frequency of full marks, mean score and coefficient of variation (CV), and the weight of indicators was determined using the Entropy weight method. The effectiveness of the Delphi expert consultation was evaluated using the active degree, authority coefficient and the consistency degree. Results: Twenty experts participated in the consultation, including 12 men, and there were 6 experts at ages of 30 to 39 years, 8 at ages of 40 to 49 years, and 6 at ages of 50 years and older. There were 17 experts with an educational level of master and above, 19 with senior professional titles and all experts had working experiences for 10 years and longer. The active degrees were 95.24% and 100.00% for two rounds of consultations, and the overall authority coefficient was 0.87. Following two rounds of consultations, all CVs were reduced to below 0.25, and the coordination coefficients were all statistically significant (P<0.05), with a higher coordination coefficient in the second round of consultation than in the first round. The constructed core competency training indicator system for high-level public health talents included four primary indicators, including health protection, health service improvement, health promotion, and essential quality, with weights of 0.388, 0.310, 0.122 and 0.180, 11 secondary indicators, with high weights seen for protection of human health from threats (0.178), monitoring and assessment of health service utilization (0.157) and promotion of health service quality and access (0.112), and 70 tertiary indicators, with high weights seen for Chinese writing (0.038), capability for health policy suggestions (0.034) and global perspective (0.030). Conclusion The core competency training indicator system for high-level public health talents constructed in this study may provide insights into training of high-level public health talents.

Objective:To clarify the long-term trend of nursing human resource allocation in China, and provide basis for relevant departments to optimize nursing human resource allocation.Methods:Data were extracted from China′s health statistical yearbook from 1990 to 2019. Six indicators of the number of registered nurses, the number of registered nurses per 1 000 population, the ratio of registered nurses to health technicians, doctor-nurse ratio, hospital bed-nurse ratio and nurse-patient ratio were used to make a descriptive statistical analysis on the changing trend of nursing human resources in China.Results:From 1990 to 2019, the number of registered nurses increased from 0.970 million to 4.445 million in China, with an average annual growth rate of 5.37%, the average annual growth rate was 2.23% from 1990 to 2005, 9.96% from 2006 to 2013, and 8.21% from 2014 to 2019. The number of registered nurses per 1 000 population increased from 0.85 to 3.18, with a 4.65% annual growth rate, the doctor-nurse ratio increased from 1∶0.55 to 1∶1.15, with a 2.58% annual growth rate, the hospital bed-nurse ratio increased from 1∶0.33 to 1∶0.50, and the average annual growth rate was 1.44%. The proportion of registered nurses to health technicians increased from 24.87% in 1990 to 43.84% in 2019, however, the number of registered nurses in hospitals accounted for about 73.00% of registered nurses in health institutions.Conclusions:From 1990 to 2019, the overall allocation efficiency of nursing human resources in China has been significantly optimized, and indicators such as doctor-nurse ratio and hospital bed-nurse ratio have improved, but the growth rate is slowing down. The relevant departments need to take necessary measures to further increase the absolute and relative number of nurses to cope with the increasing service demand.

Objective: To test the applicability of the relative value used to measure Chinese nurses′ workload, and measure part of single workload of nursing procedures. Methods: Time, physical effort, mental effort and medical risk were used as the four dimensions to evaluate nurses′ workload. Based on these dimensions, a set of questionnaires covering 117 common nursing procedures were designed and 58 nurses form 58 hospitals were surveyed. Establishing three different regulation modals, of which the applicability and stability were evaluated through the good of fit. Results: Median of the relative value of different dimensions was used to indicate the average level of the various nursing procedures′ workload. The results showed that the median of relative value between 100 to 199 accounted for the largest share, up to 88.89% of 104 items. The median of relative value between 200 to 299 accounted for 7.69%(9 items). The median of relative value between 0 to 99(3 items)and more than 300(1 item)accounted for a small proportion. The lowest point of workload was blood pressure measuring, having a relative value of 90(70, 100) while the PICC catheterization accounted for the highest point of workload, being 370(200, 500) Three kinds of modals were valid(Model Ⅰ, F=10 626.16, P<0.001; Model Ⅱ, F=17 108.22, P<0.001; Model Ⅲ: F=6 694.16, P<0.001), while the good of fit of these regulation modals was between 0.8 to 0.9. Time, physical effort, mental effort and iatrogenic risk were the key variables of nurses′ workload. Conclusions The applicability of the relative value to measure nurses′ workload is fairly satisfactory, and the relative value points based on multi-dimension will enjoy a promising future.

Objective? To investigate the current development of hospital nursing information systems from the perspective of nurses. Methods? According to the characteristics of nursing information system, self-made questionnaires were published online from 3rd to 10th May, 2018 in Weixin Subscription Number of Zhejiang Zhongwei Nursing Information Management Research Institute and all kinds of Weixin groups related to nursing management/information. A total of 1 966 nurses volunteered to participate in the survey and 1 364 valid questionnaires were collected. Results? A total of 417 hospitals were covered in the survey, including 281 tertiary hospitals and 130 secondary hospitals. 82.75% of the tertiary hospitals and 65.65% of the secondary hospitals had nursing information system. The nursing information systems were mainly based on the clinical nursing information system and the nursing management information system. The proportion of clinical nursing information system "meeting clinical nursing needs very well" was 5.21%. The proportion of "satisfying nursing management needs very well" in nursing management information system was 1.96%. There are some problems in nursing information system, such as "each subsystem can not be docked" "data can not be extracted","data need to be re-entered" and so on. Conclusions? The construction of hospital nursing information system in China is relatively backward, and nursing information system can not meet the needs of clinical and management. In order to build intelligent nursing information system, it is urgent to carry out research on nursing standard terminology.

Clinical training base is playing an important role in the quality guarantee of postgradu-ates in clinical medicine. The clinical allocation, teaching resources and teaching staff are important factors affecting the quality of teaching. Clinical training bases should define the teaching objectives and guarantee the teaching orientation, organization and incentive system. Meanwhile, the clinical training bases should do well in teaching evaluation and continuous improvement, so that to promote the implementation of teaching objectives and ensure the quality of teaching. Finally, a training mechanism centering on the growth and development of postgraduates majoring in clinical medicine will be constructed, improving the training effect and cultivating excellent clinicians.

Objective: To study the effect of WT1 expression on the prognosis of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in acute leukemia (AL) and its significance as molecular marker to dynamically monitor minimal residual disease (MRD) . Methods: Retrospectively analyzed those AL patients who underwent allo-HSCT in the First Hospital Affiliated to Zhejiang University School of Medicine during Jan 2016 to Dec 2017, a total number of 314 cases, 163 males and 151 females, median age was 30 (9-64) years old. Comparing the difference of WT1 expression at diagnosed, pre-HSCT and after HSCT. Using the receiver operating characteristic (ROC) curve to determine the WT1 threshold at different time so as to predict relapse. The threshold of WT1 expression before transplantation was 1.010%, within 3 months after HSCT was 0.079% and 6 months after HSCT was 0.375%. According to these thresholds, WT1 positive patients were divided into low expression groups and high expression groups. Analyzed the relationship between overall survival (OS) , disease-free survival (DFS) , cumulative incidence of relapse (CIR) and WT1 expression. Results: The OS and DFS of high expression group pre-HSCT were lower than low expression group [69.2% (9/13) vs 89.1% (57/64) , χ²=4.086, P=0.043; 53.8% (7/13) vs 87.5% (56/64) , χ²=9.766, P=0.002], CIR was higher than low expression group [30.8% (4/13) vs 7.8% (5/64) , P=0.017]. There was no significant difference of OS and DFS between high expression and low expression group of 3 months after HSCT (P=0.558, P=0.269) . The OS and DFS of high expression group of 6 months after transplantation were both lower than low expression group (P=0.049, P=0.035) . Multivariate analysis showed that WT1>0.375% when 6 months after transplantation was the only independent prognostic factor for shorter DFS (P=0.022) . There was no statistically significant difference in CIR between the high-expression group and the low-expression group 3 months after transplantation and 6 months after transplantation (P=0.114, P=0.306) . Conclusion High expression of WT1 before and after HSCT was an adverse prognosis factor. It is of clinical practical value to use WT1 as a transplant recommendation index for patients with acute leukemia and as a marker to monitor MRD dynamically.