Objective:To develop an admission assessment index system for hospice care tailored to emergency patients.Methods:Using a mixed-methods approach, a preliminary framework was established through literature review and qualitative interviews. From May to September 2024, a two-round Delphi consultation was conducted with 26 experts to screen and refine indicators. The final system was determined using a priority chart method for weight assignment.Results:Twenty-five experts in fields of emergency medicine, emergency nursing, palliative care nursing, palliative care medicine and general practice completed both consultation rounds. The experts ranged in age from 36 to 56 years and had 12 to 36 years of work experience. All held intermediate or higher professional titles. The effective recovery rates for the two rounds of consultation questionnaires were 96.15%(25/26) and 100.00%(25/25), respectively. Expert authority coefficients were 0.891 and 0.911, with Kendall′s coordination coefficients of 0.348 and 0.399 ( P<0.001), and variation coefficients ranging from 0.040 to 0.204 and 0.000 to 0.179. The finalized system comprises 5 primary indicators (weighted coefficients: life-limiting disease status 0.268, symptom control 0.236, patient/familywishes 0.201, social support needs 0.162, medical resource utilization 0.133) and 27 secondary indicators. Conclusion:The index system developed in this study provides a comprehensive and practical tool for identification of emergency patients requiring hospice care, facilitating timely clinical decision-making.

Objective Accurate speed perception is crucial for tasks such as man-controlled rendezvous and docking,and teleoperation of space manipulator.Therefore,it is necessary to conduct in-orbit experiments to explore the influence of long-term spaceflight on human speed perception characteristics.Methods The Time-to-Collision(TTC)paradigm was selected to develop experimental software,using a tablet computer for stimulus presentation.Human speed perception characteristics were evaluated based on the subjects'keystroke response data on the keyboard.Through ground-based experiments,the usability and reliability of the paradigm were explored,and the gravity internal model effect was quantitatively analyzed.Through in-orbit experiments on space station tasks,the influence of long-term spaceflight on human speed perception characteristics was further investigated.Results Under the 1G environment on the ground,the TTC paradigm has a high test-retest reliability(r>0.8),and indicators such as average deviation rate and absolute value of average deviation rate show no practice effect.In addition,ground experiments found that compared to vertical upward movement,vertical downward movement is estimated to be faster(i.e.,keystroke time is advanced),showing the existence of the gravity internal model effect.In the microgravity environment of spaceflight,there are no significant differences in average deviation rate and absolute value of average deviation rate among three stages(pre-flight,in-flight,post-flight)and seven tests,indicating that no obvious changes in astronauts'speed perception ability were found at the existing test time points and paradigms.However,the gravity internal model effect(difference between vertical downward and vertical upward)showed a trend of fading in the early stage of astronauts entering orbit.Conclusion Based on the computer screen TTC estimation paradigm,no significant changes in human speed perception ability were found during long-term spaceflight,but microgravity may weaken the human brain's gravity internal model.

Objective: This study aims to investigate the therapeutic effects and underlying mechanisms of Xuanfei Baidu Decoction (XFBD) in a mouse model of dampness-heat toxin pneumonia. By exploring how XFBD exerts its effects, we seek to deepen our understanding of its role in treating pulmonary diseases and to address the current knowledge gap regarding its mechanisms of action, thereby supporting its clinical application. Methods: Ultra-high-performance liquid chromatography and high-resolution mass spectrometry (HRMS) were employed to analyze the chemical constituents of XFBD. The protective effects of XFBD were evaluated using a dampness-heat toxin-induced mouse model, established through dampness-heat exposure and HCoV-229E infection. XFBD was administered orally, followed by assessments including lung index measurement, micro-CT imaging, viral load quantification, cytokine analysis, and histological evaluation via hematoxylin-eosin staining. Proteomics and single-cell transcriptomic analyses were conducted to explore the potential mechanisms underlying XFBD’s pharmacological effects. A cellular model of HCoV-229E infection was developed to investigate changes in the cAMP/PKA signaling pathway. Molecular docking and surface plasmon resonance (SPR) experiments confirmed the strong binding affinity between key XFBD components and PKA. Finally, PKA activators and inhibitors were applied in vitro to validate these mechanistic findings. Results: In vivo studies demonstrated that XFBD significantly reduced the lung index, improved the structural integrity of lung and tongue tissues, and decreased levels of proinflammatory mediators, including IL-6, IL-8, and TNF-α. Proteomic and single-cell transcriptomic analyses showed that the differentially expressed proteins after XFBD treatment were primarily associated with inflammatory responses and immune regulation. The cAMP/PKA signaling pathway was identified as a key mechanism underlying these therapeutic effects. Notably, Western blot, ELISA, molecular docking, and SPR analyses confirmed that XFBD elevated cAMP levels and p-PKA expression, thereby activating the cAMP/PKA signaling pathway in vitro. Conclusion: This study demonstrated that XFBD significantly alleviates symptoms in mice with dampness-heat toxin pneumonia. Its therapeutic effects are mediated, at least in part, through activation of the cAMP/PKA signaling pathway. These findings provide compelling evidence that XFBD is an effective herbal remedy against HCoV-229E infection.

OBJECTIVE To get the current situation of healthcare associated infection(HAI)and implement targe-ted management through analyzing the trends of HAI incidence rate,the distribution of clinical departments,the sites of HAI and the composition of pathogens in a children's hospital in a five-year period.METHODS Data on HAI cases were collected from all inpatients in Children's Hospital of Soochow University from 2019 to 2023 through the real-time monitoring system of XINGLIN Healthcare acquired Infection Surveillance;Cochran-Armit-age test was used to check for the significant changes of distribution of HAI cases.RESULTS A total of 383,376 hospitalized patients were monitored from 2019 to 2023,of which HAI occurred on 6670 cases and 7209 case-times with an incidence rate of 1.74%and an incidence case rate of 1.88%.The top five departments of HAI inci-dence rates were cardiac,neonatal,surgical and pediatric intensive care unit and department of hematology.HAI mainly occurred in blood and alimentary system,upper and lower respiratory tract.A total of 2668 strains of path-ogenic bacteria were isolated,of which 1346 strains were gram-negative bacteria,1140 strains were gram-positive bacteria and 182 strains were fungi.The top three gram-negative bacteria were Klebsiella pneumoniae,Escherich-ia coli and Pseudomonas aeruginosa;the top three gram-negative pathogens were Streptococcus,Staphylococcus aureus and Staphylococcus epidermidis;and the top three fungi were Candida albicans,Candida parapsilosis and Candida tropicalis.CONCLUSIONS The HAI incidence rate of this hospital steadily declines in the past five years,however the same changes are not observed in the departments with high incidence of HAI.Attention should be paid to the raising bloodstream infections and detection rate of S.epidermidis.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

OBJECTIVE To get the current situation of healthcare associated infection(HAI)and implement targe-ted management through analyzing the trends of HAI incidence rate,the distribution of clinical departments,the sites of HAI and the composition of pathogens in a children's hospital in a five-year period.METHODS Data on HAI cases were collected from all inpatients in Children's Hospital of Soochow University from 2019 to 2023 through the real-time monitoring system of XINGLIN Healthcare acquired Infection Surveillance;Cochran-Armit-age test was used to check for the significant changes of distribution of HAI cases.RESULTS A total of 383,376 hospitalized patients were monitored from 2019 to 2023,of which HAI occurred on 6670 cases and 7209 case-times with an incidence rate of 1.74%and an incidence case rate of 1.88%.The top five departments of HAI inci-dence rates were cardiac,neonatal,surgical and pediatric intensive care unit and department of hematology.HAI mainly occurred in blood and alimentary system,upper and lower respiratory tract.A total of 2668 strains of path-ogenic bacteria were isolated,of which 1346 strains were gram-negative bacteria,1140 strains were gram-positive bacteria and 182 strains were fungi.The top three gram-negative bacteria were Klebsiella pneumoniae,Escherich-ia coli and Pseudomonas aeruginosa;the top three gram-negative pathogens were Streptococcus,Staphylococcus aureus and Staphylococcus epidermidis;and the top three fungi were Candida albicans,Candida parapsilosis and Candida tropicalis.CONCLUSIONS The HAI incidence rate of this hospital steadily declines in the past five years,however the same changes are not observed in the departments with high incidence of HAI.Attention should be paid to the raising bloodstream infections and detection rate of S.epidermidis.

Objective:To develop an admission assessment index system for hospice care tailored to emergency patients.Methods:Using a mixed-methods approach, a preliminary framework was established through literature review and qualitative interviews. From May to September 2024, a two-round Delphi consultation was conducted with 26 experts to screen and refine indicators. The final system was determined using a priority chart method for weight assignment.Results:Twenty-five experts in fields of emergency medicine, emergency nursing, palliative care nursing, palliative care medicine and general practice completed both consultation rounds. The experts ranged in age from 36 to 56 years and had 12 to 36 years of work experience. All held intermediate or higher professional titles. The effective recovery rates for the two rounds of consultation questionnaires were 96.15%(25/26) and 100.00%(25/25), respectively. Expert authority coefficients were 0.891 and 0.911, with Kendall′s coordination coefficients of 0.348 and 0.399 ( P<0.001), and variation coefficients ranging from 0.040 to 0.204 and 0.000 to 0.179. The finalized system comprises 5 primary indicators (weighted coefficients: life-limiting disease status 0.268, symptom control 0.236, patient/familywishes 0.201, social support needs 0.162, medical resource utilization 0.133) and 27 secondary indicators. Conclusion:The index system developed in this study provides a comprehensive and practical tool for identification of emergency patients requiring hospice care, facilitating timely clinical decision-making.

Background: Twenty-four-hour urinary free cortisol (UFC) measurement is the initial diagnostic test for Cushing’s syndrome (CS). We compared UFC determination by both direct and extraction immunoassays using Abbott Architect, Siemens Atellica Solution, and Beckman DxI800 with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, we evaluated the value of 24-hr UFC measured by six methods for diagnosing CS. Methods: Residual 24-hr urine samples of 94 CS and 246 non-CS patients were collected.A laboratory-developed LC-MS/MS method was used as reference. UFC was measured by direct assays (D) using Abbott, Siemens, and Beckman platforms and by extraction assays (E) using Siemens and Beckman platforms. Method was compared using Passing–Bablok regression and Bland–Altman plot analyses. Cut-off values for the six assays and corresponding sensitivities and specificities were calculated by ROC analysis. Results: Abbott-D, Beckman-E, Siemens-E, and Siemens-D showed strong correlations with LC-MS/MS (Spearman coefficient r = 0.965, 0.922, 0.922, and 0.897, respectively), while Beckman-D showed weaker correlation (r = 0.755). All immunoassays showed proportionally positive bias. The areas under the curve were 0.975 for Abbott-D, 0.972 for LCMS/MS, 0.966 for Siemens-E, 0.948 for Siemens-D, 0.955 for Beckman-E, and 0.877 for Beckman-D. The cut-off values varied significantly (154.8–1,321.5 nmol/24 hrs). Assay sensitivity and specificity ranged from 76.1% to 93.2% and from 93.0% to 97.1%, respectively. Conclusions Commercially available immunoassays for measuring UFC show different levels of analytical consistency compared to LC-MS/MS. Abbott-D, Siemens-E, and Beckman-E have high diagnostic accuracy for CS.

Objective To investigate the effect of intraoperative renal artery resistance index(RI)in predicting postoperative acute kidney injury(AKI)in patients undergoing cardiac surgery with cardiopul-monary bypass(CPB).Methods Forty-four patients undergoing elective cardiac surgery with CPB,21 males and 23 females,aged ≥18 years,BMI 18.5-30.0 kg/m2,ASA physical status Ⅰ-Ⅲ,were select-ed.Left renal artery peak systolic velocity(PSV)and end-diastolic velocity(EDV)were measured by transesophageal echocardiography(TEE)20 minutes after general anesthesia induction(T1)and 30 minutes after CPB cessation(T2).RI was calculated as(PSV-EDV)/PSV.Patients were divided into two groups:the AKI group and the non-AKI group,according to whether occurred AKI by the diagnostic criteria of the kidney disease:improving global outcomes organization(KDIGO).Logistic multivariate regression analysis was performed to identify the risk factors for AKI occurrence.Results Sixteen patients(36％)oc-curred AKI.Compared with the non-AKI group,the CPB duration and aortic cross-clamp duration were sig-nificantly prolonged(P＜0.05),the renal artery RI at T,and T2 were significantly increased in the AKI group(P＜0.05).Multivariate logstic analysis showed that RI at T2 was an independent risk factor for AKI occurrence after cardiac surgery,the AUC was 0.893(95％CI 0.794-0.991,P＜0.010),the sensitivity and specificity were 84.5％and 78.6％,respectively,and the cut-off value was 0.720.Conclusion Intra-operative renal artery RI in patients undergoing cardiac surgery is an independent risk factor for AKI after cardiac surgery,and renal artery RI＞0.720 at 30 minutes after CPB cessation can serve as a predictive in-dicator for AKI after cardiac surgery.