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 A joint Poisson-logistic model in a Bayesian framework is proposed to constructed using tuberculosis(TB)reporting data from 14 prefectures in Xinjiang from 2014 to 2020 in combination with relevant social,economic,and environmental factors affecting the reported incidence rate of TB to explore potential underreporting areas of the TB reporting data,and to provide a strong evidence－based support for the subsequent decision－making on the precision prevention and control of TB.Methods Relevant factors affecting the reporting process and disease process of TB were collected,and important covariates were screened for inclusion in the model using the factor detector in the Geo-detector method,and the reported incidence model of TB and the expected incidence model of TB in Xinjiang were constructed separately,which together constituted a hybrid model of underreporting of TB(Poisson-logistic joint model).The mixed model was used to estimate the risk of TB underreporting in each prefecture of Xinjiang,and to explore the regional distribution of the potential risk of TB underreporting.Results Factor detector result pairs showed that GDP per capita was associated with the largest contribution to the risk of TB underreporting(0.5481);goodness－of－fit test showed that the data were well fitted(Bayesian P－value<0.001),and the Bayesian Poisson-logistic joint model could be applied to the study of the risk of underreporting of TB reporting data in Xinjiang from 2014 to 2020.The results showed that the risk of underreporting of TB The risk of underreporting of reported data was concentrated in the four southern Xinjiang prefectures,with the greatest risk of underreporting of TB reported data in Kashgar 0.1426(0.1403,0.1445).The lower risk of underreporting was concentrated in the eastern and central parts of Xinjiang,with the lowest risk of underreporting in the city of Karamay[0.1017(0.9983,0.1034)].In a joint Bayesian Poisson-logistic model,it was found that population density(IRR＝1.0060,95%CI:1.0059~1.0061)and average annual temperature(IRR＝1.0087,95%CI:1.0086~1.0088)were risk factors for underreporting of TB,and GDP per capita(IRR＝0.9385,95%CI:0.9365~0.9394)and an increase in the number of registered nurses(IRR＝0.9916,95%CI:0.9913 to 0.9920)reduced the risk of TB underreporting.Conclusion The Bayesian Poisson-logistic joint model estimated the potential incidence of TB in Xinjiang Uygur Autonomous Region and revealed significant discrepancies between reported and true TB incidence rates.It identified underreporting trends and localized potential underreporting risk areas,providing a theoretical basis for tailored and precise TB prevention and control strategies in Xinjiang.

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 To explore the factors influencing early recovery quality after radical sur-gery for colorectal cancer in elderly patients and establish a prediction model.Methods A total of 182 elderly patients who underwent elective radical surgery for colorectal cancer at the Affiliated Hospi-tal of Yangzhou University between May 2023 and May 2024 were enrolled as study objects.Data such as gender,age,body mass index(BMI),American Society of Anesthesiologists(ASA)classification,albumin,serum creatinine,hemoglobin,and D-dimer levels at admission were collected.Surgical ap-proach,operative time,anesthesia time,length of hospital stay,and whether the patient was transferred to the intensive care unit(ICU)postoperatively were also recorded.Relevant patient information was compiled through the electronic medical record system to calculate the modified frailty index(mFI).The 15-item Quality of Recovery Scale(QoR-15)was used to assess patients'recovery quality three days postoperatively.Results A total of 163 patients had good recovery(QoR-15 score ≥120)and 19 had poor recovery(QoR-15 score＜120).Preoperative mFI(≥0.27)and BMI(≥21.05 kg/m2)were identified as factors influencing early recovery quality after radical surgery for colorectal cancer in elderly patients.The area under the receiver operating characteristic(ROC)curve(AUC)for the prediction model of recovery quality after radical surgery for colorectal cancer in elderly patients was 0.816(95％CI:0.710～0.921),indicating good agreement between the models predicted recovery quality and ac-tual recovery quality,indicating high discrimination and accuracy.Conclusion Preoperative mFI(≥0.27)and BMI(≥21.05 kg/m2)are factors influencing recovery quality after radical surgery for colorectal cancer in elderly patients.Improving perioperative frailty status and appropriately regu-lating BMI levels can help reduce the risk of postoperative complications.

Objective A joint Poisson-logistic model in a Bayesian framework is proposed to constructed using tuberculosis(TB)reporting data from 14 prefectures in Xinjiang from 2014 to 2020 in combination with relevant social,economic,and environmental factors affecting the reported incidence rate of TB to explore potential underreporting areas of the TB reporting data,and to provide a strong evidence－based support for the subsequent decision－making on the precision prevention and control of TB.Methods Relevant factors affecting the reporting process and disease process of TB were collected,and important covariates were screened for inclusion in the model using the factor detector in the Geo-detector method,and the reported incidence model of TB and the expected incidence model of TB in Xinjiang were constructed separately,which together constituted a hybrid model of underreporting of TB(Poisson-logistic joint model).The mixed model was used to estimate the risk of TB underreporting in each prefecture of Xinjiang,and to explore the regional distribution of the potential risk of TB underreporting.Results Factor detector result pairs showed that GDP per capita was associated with the largest contribution to the risk of TB underreporting(0.5481);goodness－of－fit test showed that the data were well fitted(Bayesian P－value<0.001),and the Bayesian Poisson-logistic joint model could be applied to the study of the risk of underreporting of TB reporting data in Xinjiang from 2014 to 2020.The results showed that the risk of underreporting of TB The risk of underreporting of reported data was concentrated in the four southern Xinjiang prefectures,with the greatest risk of underreporting of TB reported data in Kashgar 0.1426(0.1403,0.1445).The lower risk of underreporting was concentrated in the eastern and central parts of Xinjiang,with the lowest risk of underreporting in the city of Karamay[0.1017(0.9983,0.1034)].In a joint Bayesian Poisson-logistic model,it was found that population density(IRR＝1.0060,95%CI:1.0059~1.0061)and average annual temperature(IRR＝1.0087,95%CI:1.0086~1.0088)were risk factors for underreporting of TB,and GDP per capita(IRR＝0.9385,95%CI:0.9365~0.9394)and an increase in the number of registered nurses(IRR＝0.9916,95%CI:0.9913 to 0.9920)reduced the risk of TB underreporting.Conclusion The Bayesian Poisson-logistic joint model estimated the potential incidence of TB in Xinjiang Uygur Autonomous Region and revealed significant discrepancies between reported and true TB incidence rates.It identified underreporting trends and localized potential underreporting risk areas,providing a theoretical basis for tailored and precise TB prevention and control strategies in Xinjiang.

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 explore the specialty work engagement dilemma among wound therapists in non-specialist clinics, so as to provide a reference basis for improving wound therapists' work engagement and reducing job burnout.Methods:This study was a phenomenological study. Semi-structured interviews with 12 wound therapists in non-specialist clinics from 12 hospitals were conducted between May and July 2024 using purposive sampling. Colaizzi seven-step method was used for data analysis and topic refinement.Results:A total of five themes and 13 sub-themes were extracted, including the role change dilemma (focus dispersion due to transfer of positions in clinical departments, and identity challenges due to adjustment of the field of job posting) , career development dilemma (reduced motivation due to obstacles to in-depth development in specialized fields, and limited conditions for scientific research training and implementation impeding the recognition and return of work) , support environment dilemma (insufficient medical insurance support increased job burden, lack of hospital support reduced work efficiency, lack of team support weakened work focus, poor self-support limited the depth of work engagement, lack of support from patients and their families increased the difficulty of maintaining work motivation) , pros and cons weighing dilemma (unclear delineation of risk and responsibility, difficult to weigh risk and benefit) , dilemma of whole-course management of disease (lack of communication between hospitals weakened the continuity of work, lack of home follow-up increased the uncertainty of the work) .Conclusions:Specialty work engagement dilemmas among wound therapists in non-specialist clinics are influenced by a variety of factors. It is necessary for medical institutions, healthcare workers and patients' families to work together to create a favorable working environment for wound therapists in order to promote the high-quality development of wound therapy nursing services in China.

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.

Abastract:Objective To validate the performance of six different lipoprotein(a)[Lp(a)]immunoassay detection systems,compare the correlation and consistency of the measurement results of different detection systems,and explore their clinical application in the e-valuation of atherosclerotic cardiovascular disease(AsCVD).Methods A total of 150 AsCVD patients attending Peking Union Medi-cal College Hospital were retrospectively selected as the subjects of study group,and 50 individuals of physical examination during the same period were selected as healthy control group.Lp(a)levels were measured in 200 serum samples using six immunoassay detection systems,including two Lp(a)particle concentration assays in nmol/L(Roche and Mindray Ⅱ),and four Lp(a)mass concentration assays in mg/L(Mindray,MedicalSystem,BSBE,and Sekisui).All assays'precisions were evaluated.The results of each assay sys-tem were compared with the mean value of all the Lp(a)assays.Passing-Bablok regression analysis and Bland-Altman bias plots were used to assess the accuracy of assays,and the consistency between different systems was analyzed using the concordance correlation co-efficient(CCC).In addition,the consistencies of different assays in assessing AsCVD in clinical setting were compared using weighted Kappa statistical method,and the positive rates of Lp(a)particle concentration and mass concentration,as well as the overestimation and underestimation of mass concentration were also assessed for both the study group and the healthy control group.Results The pre-cision of the six Lp(a)assays ranged from 0.6％to 2.1％.Passing-Bablok regression analysis showed that the Spearman correlation co-efficients of the regression equations were all greater than 0.970,and the intercepts and slopes of the regression lines were-43.311 to 39.456 and 0.547 to 5.500,respectively.The Bland-Altman bias plots showed that the percent bias of the six assays compared to the mean value of Lp(a)determination was in the range of-25.939％to 40.205％.The results of the Lp(a)mass concentration detection system showed a positive deviation in Mindray and MedicalSystem,and a negative deviation in BSBE and Sekisui.Compared with the mean value of Lp(a),the results of consistency analysis showed that Roche and Mindray Ⅱ had excellent consistency(CCC:0.992 to 0.993).Mindray,MedicalSystem and BSBE had good consistency(CCC:0.950 to 0.986),and Sekisui showed moderate consistency(CCC:0.935).In descending order,the positivity rates of Lp(a)in the study groups were:MedicalSystem＞BSBE＞Mindray＞Roche=Mindray Ⅱ＞Sekisui.The overall concordances of Mindray,MedicalSystem,BSBE and Sekisui compared to Lp(a)particle concentration assay in different groups were 97.33％,93.33％,97.33％and 98.00％with Kappa values of 0.910,0.798,0.912 and 0.927,respec-tively.Conclusion The two assays for Lp(a)particle concentration have fine correlation and consistency,but there were significant differences between the four assays for Lp(a)mass concentration.Compared to the Lp(a)particle concentration assays,the four assay for Lp(a)mass concentration resulted in overestimation or underestimation of Lp(a)levels in the assessment of AsCVD.Accurate de-termination of Lp(a)concentration should be of great importance in accurately assessing the overall risk of AsCVD in patients.

Objectives: Hypoparathyroidism (HP) is a rare endocrine disorder caused by parathyroid hormone (PTH) defi ciency. The PTH is a candidate gene for familial isolated hypoparathyroidism (FIH). This study aimed to investigate the pathogenicity of two novel rare variants (RVs) ofPTH through in vitro functional study. Methods: Targeted next-generation sequencing was used to identify candidate gene mutations. Clinical data were retrospectively collected. Wild-type (WT) PTH was used as a template for site-directed mutagenesis to create mutant eukaryotic expression plasmids, which were transfected into cells. Treated with or without 4-phenylbu tyric acid (4-PBA), the levels of intact PTH (iPTH) and PTH (1-84) were measured by chemiluminescence, and protein expression was assessed using Western blotting. Results: Two patients carrying PTH mutations (c.154G > A: p.Val52Ile, c.270G > T: p.Leu90Phe) were identified.Patient 1, a 45-year-old male, presented with carpal and pedal numbness, muscle cramps, and low serum calcium (1.29 mmol/L). Patient 2, a 12-year-old female, had muscle twitches, convulsions, low calcium (1.50 mmol/L), and iPTH of 4 pg/mL. The iPTH or PTH (1-84) levels in the medium transfected with mutant Val52Ile and Leu90Phe PTH decreased by 31%–38%, and 51%–96% compared to WT (allP < 0.05), which were not rescued by 4-PBA. No significant changes in intracellular PTH expression were observed. Conclusions In this study, two novel RVs of PTH(Val52Ile and Leu90Phe) were identified that may impair hormone synthesis and secretion. Our study has broadened the mutation spectrum of the PTH and shed light on potential mechanisms underlying FIH.