OBJECTIVE To construct a closed-loop management model for externally dispensed intravenous prescriptions， and to provide reference for standardized management of externally dispensed intravenous prescriptions. METHODS Based on the Expert Consensus on Closed-loop Management of Externally Dispensed Intravenous Prescriptions in Guangxi Zhuang Autonomous Region previously formulated by our hospital， risk points during the entire process were systematically identified through multidisciplinary team brainstorming and a fishbone diagram. A series of strategies were subsequently formulated and implemented， including qualifying designated external dispensing pharmacies and the drug catalogs， operating and maintaining the hospital information system and the Pharmacy Intravenous Admixture Service （PIVAS） intelligent management platform， and strengthening differentiated training for staff in the whole workflow. A whole-process closed-loop management system was constructed with PIVAS as the co re hub and the daytime chemotherapy center as the safety terminal. RESULTS A total of 3 cooperating pharmacies and an initial drug list comprising 35 product specifications were selected. A closed‑loop management process encompassing hospital outpatient prescribing， patient drug purchase in designated pharmacies， PIVAS drug dispensing， and medication use in daytime chemotherapy center was successfully established. This system enabled the mandatory grouping and association of externally dispensed intravenous prescriptions with in-hospital diluents， full-process verification based on drug traceability codes， intelligent monitoring of infusion parameters， and whole-process data traceability. CONCLUSIONS The constructed model effectively resolves the coordination and safety oversight during the use of externally dispensed intravenous drugs from out-of-hospital circulation to in-hospital use， and has preliminarily enabled procedural standardization， whole-process information traceability， and proactive control of medication risks.

Objective: To investigate the impact of milk and egg supplementation on body composition and bone mineral density of rural primary school students in Yunnan Province, so as to provide a reference for developing targeted nutritional intervention strategies. Methods: In December 2023, a cluster sampling method was adopted to select students from grades one to three in four primary schools each from Jinggu and Shidian countys of Yunnan Province, as the intervention group (662 students). Additionally, two boarding primary schools were selected from each county based on the principle of matching scale and student numbers as the control group (455 students). Starting from April 2023, the intervention group received 200 mL milk and 50 g eggs during the break on school days for 8 months, while the control group maintained their usual diet behavior. Body composition was measured by using bioelectrical impedance analysis, and distal radial bone mineral density was assessed via dual energy X-ray absorptiometry in April and December 2023. The intervention effects were analyzed by using a difference in-differences approach. Results: The final measurements of body fat percentage, skeletal muscle mass and fat free mass of the intervention group and the control group of primary school students were significantly higher than the baseline values, and the net effect of milk and egg intervention on these body composition indicators was not statistically significant ( P >0.05, both before and after adjustment). In contrast, bone mineral density increased significantly by 0.02 g/cm 2 in the intervention group. The net intervention effect on bone mineral density was statistically significant ( β=0.02, 95%CI =0.00-0.04), and remained significant after model adjustment ( β=0.02, 95%CI =0.00-0.04) (both P < 0.05). Subgroup analysis showed statistically significant effects of the intervention among girls ( β=0.02, 95%CI =0.00-0.04), day students ( β=0.04, 95%CI =0.01-0.07), and students with normal nutritional status ( β=0.02, 95%CI =0.00-0.04) (all P <0.05). No significant effect of milk and egg supplementation was observed on body composition indicators (all P <0.05). Conclusions Milk and egg supplementation can improve bone mineral density among rural primary school students in Yunnan Province. It is recommended that rural school aged children should increase intake of milk and eggs to support growth and development.

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.

Objective:To construct an artificial intelligence (AI)-assisted system based on deep learning for corneal in vivo confocal microscopy (IVCM) image recognition and to evaluate its value in clinical applications. Methods:A diagnostic study was conducted.A total of 18 860 corneal images were collected from 331 subjects who underwent IVCM examination at Renmin Hospital of Wuhan University and Zhongnan Hospital of Wuhan University from May 2021 to September 2022.The collected images were used for model training and testing after being reviewed and classified by corneal experts.The model design included a low-quality image filtering model, a corneal image diagnosis model, and a 4-layer identification model for corneal epithelium, Bowman membrane, stroma, and endothelium, to initially determine normal and abnormal corneal images and corresponding corneal layers.A human-machine competition was conducted with another 360 database-independent IVCM images to compare the accuracy and time spent on image recognition by three senior ophthalmologists and the AI system.In addition, 8 trainees without IVCM training and with less than three years of clinical experience were selected to recognize the same 360 images without and with model assistance to analyze the effectiveness of model assistance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY2021-K148).Results:The accuracy of this diagnostic model in screening high-quality images was 0.954.Its overall accuracy in identifying normal/abnormal corneal images was 0.916 and 0.896 in the internal and external test sets, respectively.Its accuracy reached 0.983, 0.925 in the internal test sets and 0.988, 0.929 in the external test sets in identifying corneal layers of normal and abnormal images, respectively.In the human-machine competition, the overall recognition accuracy of the model was 0.878, which was similar to the average accuracy of the three senior physicians and was approximately 300 times faster than the experts in recognition speed.Trainees assisted by the system achieved an accuracy of 0.816±0.043 in identifying corneal layers of normal and abnormal images, which was significantly higher than 0.669±0.061 without model assistance ( t=6.304, P<0.001). Conclusions:A deep learning-based assistant system for corneal IVCM image recognition is successfully constructed.This system can discriminate normal/abnormal corneal images and diagnose the corresponding corneal layer of the images, which can improve the efficiency of clinical diagnosis and assist doctors in training and learning.

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 investigate whether it can reduce the incidence of pressure ulcer caused by ICU equipment, when nurses in ICU use the SKIN model framework to carry out preventive and nursing measures. Methods: To construct the teaching group of nursing workshop based on SKIN mode for preventing pressure ulcer caused by ICU equipment and train 36 ICU nurses. Before and after training course, to collect the behaviors of preventing pressure ulcer caused by ICU equipment during clinical work time and the incidence and severity of pressure ulcer. Results: The nurses′ behaviors of controlling skin status of patients who use Bi-level positive airway pressure non-invasive mask and evaluating nutritional status of patients change a lot (χ²=34.420,10.123, P <0.01). The nurses′ behaviors of checking and evaluating skin status of patients who have stomach tube to reduce skin pressure, shear and friction and assessing nutritional status of patients were improved (χ²=7.547-49.451, P <0.05). The nurses′ behaviors mentioned above of nursing patients who have oral air tube were also changed significantly (χ²=5.225, 23.402, 12.444, P<0.05 or 0.01). The incidence of pressure ulcer caused by ICU equipment dropped from 2.86%(100/3 502) to 1.87%(67/3 580) (χ²=7.445, P<0.05). The decrease in incidence of Ⅱand Ⅲ grade pressure ulcer are from 48%(48/100) to 22.39%(15/67) and 34%(34/100) to 7.46%(5/67) respectively and had significant differences as well (χ²=11.202, 15.784, P<0.01). Conclusions This study explains the training approach based on SKIN mode of prevention of pressure ulcer caused by ICU equipment. The results show that the training approach can decrease the incidence and severity of pressure ulcer caused by ICU equipment, improve the nurses′ nursing behaviors and promote the transform of theory related to pressure ulcer from basement to clinical reality.

A total of 1 685 school-age children selected from Hangzhou received lung function testing to evaluate the short-term effects of air pollution on their lung function. The results showed that in every 10 μg/m³ increase of average concentration of PM_2.5 and PM₁₀ on the day of the test and the day before the test,peak expiratory flow (PEF) decreased 0.039 (95%CI: 0.012-0.067) L/s and 0.031 (95% CI:0.011-0.051) L/s,respectively. When the average concentration of SO₂ increased 10 μg/m³ on the day of test and the day prior to the test, PEF and 75% of the forced vital capacity that has not been exhaled (MEF₇₅) decreased 0.437 (95%CI: 0.217-0.658) L/s and 0.396 (95%CI: 0.180-0.613) L/s. After being adjusted for NO₂,with every 10 μg/m³ increase of average concentration of PM_2.5 and PM₁₀ on the day of the test and the day before the test,PEF and MEF₇₅ decreased 0.056 (95%CI: 0.028-0.085), 0.053(95%CI: 0.027-0.081) and 0.047 (95%CI: 0.026-0.068) L/s,0.044 (95%CI: 0.023-0.065) L/s on the day before the test, respectively. The results indicate that air pollution have short-term and lag effects on lung function of school-age children in Hangzhou.

A total of 1 685 school?age children selected from Hangzhou received lung function testing to evaluate the short?term effects of air pollution on their lung function. The results showed that in every 10 μg/m3 increase of average concentration of PM2.5 and PM10 on the day of the test and the day before the test,peak expiratory flow (PEF) decreased 0.039 (95%CI: 0.012-0.067) L/s and 0.031 (95% CI:0.011-0.051) L/s,respectively. When the average concentration of SO2 increased 10 μg/m3 on the day of test and the day prior to the test, PEF and 75% of the forced vital capacity that has not been exhaled (MEF75) decreased 0.437 (95%CI: 0.217-0.658) L/s and 0.396 (95%CI : 0.180-0.613) L/s. After being adjusted for NO2,with every 10 μg/m3 increase of average concentration of PM2.5 and PM10 on the day of the test and the day before the test,PEF and MEF75 decreased 0.056 (95%CI: 0.028-0.085), 0.053(95%CI: 0.027-0.081) and 0.047 (95%CI: 0.026-0.068) L/s, 0.044 (95%CI: 0.023-0.065) L/s on the day before the test, respectively. The results indicate that air pollution have short?term and lag effects on lung function of school?age children in Hangzhou.