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 formulate the Expert Consensus on the Implementation and Management of Drug Selection for Centralized Volume-Based Procurement in Medical Institutions of Guangxi （hereinafter referred to as the “ Consensus ”）， and to provide decision-making support and practical guidance for the drug selection and management of centralized volume-based procurement （hereinafter referred to as “centralized procurement”） drugs in medical institutions at all levels in Guangxi. METHODS A systematic review was conducted on the materials from previous batches of centralized procurement implemented in Guangxi. A comprehensive search was carried out for drug-related works and books， along with a systematic collation of guidelines on drug selection， expert consensus on centralized procurement， and policy documents. Through three rounds of specialized seminars， combined with existing evidence-based data and the practical drug selection experiences of medical institutions at various levels， this Consensus was formulated after thorough discussion and successive rounds of revision. RESULTS & CONCLUSIONS The Consensus systematically outlines the three key stages in the implementation of centralized procurement in medical institutions： procurement volume reporting， confirmation of agreed procurement volume， and procurement and usage implementation. It proposes drug selection strategies for centralized procurement bas ed on multiple dimensions， including specifications， dosage forms， packaging materials， fill volume， and manufacturing enterprises. In response to practical challenges encountered in the selection process， corresponding countermeasures are proposed， such as establishing a regularized information reserve mechanism， strengthening information technology support， and implementing categorized selection approaches. The Consensus advocates for medical institutions to construct an integrated “policy， data， and quality” decision-making system to promote full-cycle management of centralized procurement. This Consensus will provide scientific and practical guidance for medical institutions at all levels in Guangxi in the drug selection of centralized procurement， facilitating the smooth implementation and sustainable development of centralized procurement policies at the institutional level.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network between participants with subjective cognitive decline (SCD) and healthy individuals, and to analyze the correlations of the volumes of the different subfields and altered covariance brain regions with memory function.Methods:A total of 57 SCD individuals(SCD group) and 44 normal controls(NC group) participants were assessed for memory function using composite scores from the auditory verbal learning test (AVLT) and the Wechsler memory scale visual reproduction (VR) test from June 2022 to October 2023.T1-weighted structural magnetic resonance imaging (MRI) data were collected from all participants, and hippocampal subfields, cortical regions, and subcortical nuclei were segmented using FreeSurfer to measure the gray matter volume of each structure. A structural covariance network was constructed based on the correlation of gray matter volumes across regions. Statistical analysis was performed using R 4.3.1 software. Inter-group differences in hippocampal subfield volumes were compared using multivariate analysis of covariance. Differences in structural covariance connectivity between groups were assessed using Z-test, while network topology differences were compared through permutation testing. Finally, partial correlation analysis was used to examine correlation of the volumes of the differential hippocampal subfields and covariance brain regions with memory function. Results:The SCD group exhibited significantly lower years of education, AVLT-immediate score, AVLT-delayed score, VR-immediate score, VR-delayed score, and memory function Z-score compared to the NC group ( t=2.064, 3.888, 2.622, 3.222, 4.761, 5.184, all P<0.05). The volumes of the right subiculum((387.75±55.20)mm 3, (352.70±70.25)mm 3), left presubiculum((263.12±38.52)mm 3, (239.79±46.02)mm 3), left subiculum((388.12±49.34)mm 3, (351.74±67.30)mm 3) and left CA1((571.01±80.01)mm 3, (526.51±98.80)mm 3) in the SCD group were smaller than the corresponding volumes in NC group ( F=9.139, 8.039, 11.207, 7.266, all P<0.05, FDR correction). Differences in structural covariance connectivity were found between the SCD and NC groups in the following pairs: right CA1-right subiculum, right CA1-left subiculum, right CA3-left parasubiculum and right hippocampus-amygdala transition area-left subiculum ( Z=-3.848, -3.896, -3.597, -3.895, all P<0.05, FDR correction).Partial correlation analysis revealed that in the SCD group, the volume of the left subiculum ( r=0.359, P=0.007), left CA1 ( r=0.430, P=0.001), right entorhinal cortex ( r=0.296, P=0.029), right middle temporal gyrus ( r=0.361, P=0.007), right parahippocampal gyrus ( r=0.313, P=0.021)were positively correlated with the total memory function score. Conclusion:Hippocampal subfields atrophy, as well as alterations in structural covariance network, have been found in SCD individuals. Furthermore, the decline in memory function may be closely associated with atrophy in hippocampal subfields and structurally covariant regions.

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 evaluate the practical application of the Media Fill Test(MFT)in assessing aseptic compounding competency of personnel in Pharmacy Intravenous Admixture Services(PIVAS).Methods A multicenter controlled study was conducted across six tertiary hospitals(center ①-⑥)in China.Participants were divided into an inexperienced group(Group A,n=118)and an experienced group(Group B,n=118),each performing five MFT operations.Positive controls validated medium efficacy.Contamination rates and pass rates were analyzed using chi-square and Fisher's exact tests.Results Valid samples included 584 for Group A and 588 for Group B.The sample pass rate was 66.78%(390/584)in Group A and 91.67%(539/588)in Group B,while personnel pass rates were 46.15%(54/117)and 80.51%(95/118),respectively,with significant intergroup differences(both P<0.01).All centers except Center ⑥ showed significantly higher pass rates in Group B(all P<0.05).Conclusion MFT effectively differentiates technical proficiency levels and is suitable for training evaluation of novice PIVAS staff.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network between participants with subjective cognitive decline (SCD) and healthy individuals, and to analyze the correlations of the volumes of the different subfields and altered covariance brain regions with memory function.Methods:A total of 57 SCD individuals(SCD group) and 44 normal controls(NC group) participants were assessed for memory function using composite scores from the auditory verbal learning test (AVLT) and the Wechsler memory scale visual reproduction (VR) test from June 2022 to October 2023.T1-weighted structural magnetic resonance imaging (MRI) data were collected from all participants, and hippocampal subfields, cortical regions, and subcortical nuclei were segmented using FreeSurfer to measure the gray matter volume of each structure. A structural covariance network was constructed based on the correlation of gray matter volumes across regions. Statistical analysis was performed using R 4.3.1 software. Inter-group differences in hippocampal subfield volumes were compared using multivariate analysis of covariance. Differences in structural covariance connectivity between groups were assessed using Z-test, while network topology differences were compared through permutation testing. Finally, partial correlation analysis was used to examine correlation of the volumes of the differential hippocampal subfields and covariance brain regions with memory function. Results:The SCD group exhibited significantly lower years of education, AVLT-immediate score, AVLT-delayed score, VR-immediate score, VR-delayed score, and memory function Z-score compared to the NC group ( t=2.064, 3.888, 2.622, 3.222, 4.761, 5.184, all P<0.05). The volumes of the right subiculum((387.75±55.20)mm 3, (352.70±70.25)mm 3), left presubiculum((263.12±38.52)mm 3, (239.79±46.02)mm 3), left subiculum((388.12±49.34)mm 3, (351.74±67.30)mm 3) and left CA1((571.01±80.01)mm 3, (526.51±98.80)mm 3) in the SCD group were smaller than the corresponding volumes in NC group ( F=9.139, 8.039, 11.207, 7.266, all P<0.05, FDR correction). Differences in structural covariance connectivity were found between the SCD and NC groups in the following pairs: right CA1-right subiculum, right CA1-left subiculum, right CA3-left parasubiculum and right hippocampus-amygdala transition area-left subiculum ( Z=-3.848, -3.896, -3.597, -3.895, all P<0.05, FDR correction).Partial correlation analysis revealed that in the SCD group, the volume of the left subiculum ( r=0.359, P=0.007), left CA1 ( r=0.430, P=0.001), right entorhinal cortex ( r=0.296, P=0.029), right middle temporal gyrus ( r=0.361, P=0.007), right parahippocampal gyrus ( r=0.313, P=0.021)were positively correlated with the total memory function score. Conclusion:Hippocampal subfields atrophy, as well as alterations in structural covariance network, have been found in SCD individuals. Furthermore, the decline in memory function may be closely associated with atrophy in hippocampal subfields and structurally covariant regions.

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 evaluate the practical application of the Media Fill Test(MFT)in assessing aseptic compounding competency of personnel in Pharmacy Intravenous Admixture Services(PIVAS).Methods A multicenter controlled study was conducted across six tertiary hospitals(center ①-⑥)in China.Participants were divided into an inexperienced group(Group A,n=118)and an experienced group(Group B,n=118),each performing five MFT operations.Positive controls validated medium efficacy.Contamination rates and pass rates were analyzed using chi-square and Fisher's exact tests.Results Valid samples included 584 for Group A and 588 for Group B.The sample pass rate was 66.78%(390/584)in Group A and 91.67%(539/588)in Group B,while personnel pass rates were 46.15%(54/117)and 80.51%(95/118),respectively,with significant intergroup differences(both P<0.01).All centers except Center ⑥ showed significantly higher pass rates in Group B(all P<0.05).Conclusion MFT effectively differentiates technical proficiency levels and is suitable for training evaluation of novice PIVAS staff.

From July 23, 2018, to December 31, 2019, a total of 299 219 children in Shandong Province received the fifth dose of the diphtheria, tetanus, and acellular pertussis combined vaccine (DTaP). Among these recipients, the distribution by age was as follows: 20 children under 2 years old (0.01%), 273 996 children aged 2 years (91.57%), 20 242 children aged 3 years (6.76%), 3 932 children aged 4 years (1.31%), 963 children aged 5 years (0.32%), and 66 children aged 6 years and above (0.02%). In total, 1 972 cases of adverse events following immunization (AEFI) were reported after the administration of the fifth dose of DTaP, resulting in an incidence rate of 659.05 per 100 000 doses. Among these, 1 718 cases were classified as common vaccine reactions, with an incidence rate of 574.16 per 100 000 doses, while 247 cases were identified as rare reactions, yielding an incidence rate of 82.55 per 100 000 doses. The incidence of AEFIs, as well as the rates of common and rare reactions, exhibited a significant increasing trend with the number of doses administered (all P<0.001). Among the rare reactions, there were 10 cases classified as severe, resulting in a reported incidence of 3.34 per 100 000 doses.

Objective:To analyze the changing trends and area specific differences in influenza vaccination coverage in key populations in Shandong Province and provide evidence for influenza prevention and control.Methods:The information about influenza vaccination in Shandong during 2015 to 2024 were collected from the Immunization Information System of Shandong Province, and the annual percentage change and average annual percentage change (AAPC) were calculated by using software Joinpoint 5.3.0. The trend of changes in vaccination coverage was analyzed and area specific differences were compared.Results:From 2015 to 2024, the influenza vaccination coverage in medical personnel ranged from 0.02% to 20.54% in Shandong (AAPC=63.24%, 95% CI: 51.37%-100.65%), showing a rapid increase from 2015 to 2020 ( P<0.001) and a stead decrease from 2020 to 2024 ( P=0.008). The vaccination coverage in teachers and students ranged from 1.51% to 6.88% (AAPC=12.33%, 95% CI: -0.97%-36.27%), with a monotonic increase from 2015 to 2024 ( P=0.070). The vaccination coverage in elderly people aged ≥60 years ranged from 0.03% to 11.68% (AAPC=102.65%, 95% CI: 64.16%-208.69%), with a rapid increase from 2015 to 2017 ( P<0.001), a steady increase from 2017 to 2020 ( P<0.001), and a slower increasee from 2020 to 2024 ( P=0.046). There were area speciific differences in the temporal trend of influenza vaccination coverage. Compared with areas with low gross domestic product (GDP), the areas with high and medium GDP had significantly higher vaccination coverage and increase rates. Conclusions:The influenza vaccination coverage in key populations showed an upward trend in Shandong from 2015 to 2024, and the vaccination coverage in elderly people aged ≥60 years increased significantly. However, it is important to increase vaccination coverage in areas with low GDP and in teachers and students.