Objective : To observe the brain tissue injury during hypoxia-ischemia, as well as the pathological changes and the expression of ferroptosis-related factors after the use of Shenfu injection(SFI), and to explore the protective effect of SFI on hypoxic-ischemic brain injury(HIBD) by inhibiting ferroptosis. Methods : An animal model of HIBD in SD rats was constructed and intervened with SFI. Pathologic changes in brain tissue were observed by HE staining methods. Nissen staining was used to observe neuron survival. Glutathione Peroxidase 4(GPX4) and Divalent Metal Transporter 1(DMT1) expression were detected in brain tissue by Western blot, immunohistochemistry and immunofluorescence. Reduced Glutathione(GSH), Lactate Dehydrogenase(LDH), Malondialdehyde(MDA), Superoxide Dismutase(SOD) and tissue iron content were determined with the kits. BV-2 microglial cell line(BV2) cells were culturedin vitroand divided into control group(Ctrl group), oxygen-glucose deprivation group(OGD group), iron ferroptosis-inducing group(Erastin group), iron ferroptosis-inhibiting group(Fer-1 group), Shenfu injection group(SFI group), and Erastin+Shenfu injection group(Erastin+SFI group). 2′,7′-Dichlorodihydrofluorescein diacetate(DCFH-DA) reactive oxygen species(ROS) fluorescent probe was used to detect the ROS release level; Immunofluorescence was used to observe intracellular GPX4, DMT1 expression. Results : Compared with the Sham group, rats in the HIBD group showed significant neuronal cell damage in brain tissue, decreased GPX4 expression(P<0.01), increased DMT1 expression(P<0.01), decreased GSH and SOD levels(P<0.01), and increased LDH, MDA and tissue iron levels(P<0.05,P<0.05,P<0.01). In contrast, after the intervention of SFI, GPX4 expression was elevated(P<0.01), DMT1 expression decreased(P<0.01), GSH and SOD levels were elevated(P<0.01), and LDH, MDA, and tissue iron levels decreased(P<0.05,P<0.05,P<0.01). The cells experiments showed that compared with the Ctrl group, the OGD group had a significantly higher ROS content and a decrease in the expression of GPX4 fluorescence intensity, and an increase in the fluorescence intensity of DMT1(P<0.01), compared with the OGD group, the ROS content was reduced in the SFI group, while the expression of GPX4 was elevated and the expression of DMT1 was reduced(P<0.01). Conclusion Hippocampal and cortical regions are severely damaged after HIBD in neonatal rats, and their brain tissues show decreased expression of GPX4 and increased expression of DMT1. The above suggests that ferroptosis is involved in HIBD brain injury in neonatal rats. In contrast, Shenfu injection has a protective effect on HIBD experimental animal model and BV2 cell injury model by reducing iron aggregation and ROS production.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.

OBJECTIVE To investigate the quality and management of cleaning and disinfection of soft endoscopes in Wuxi hospitals,to analyze the existing problems and put forward suggestions for improvement,and to provide references for endoscope-related infection in hospitals.METHODS From Sep.to Nov.2024,the cleaning and disin-fection status and managements of soft endoscopes were investigated.Endoscopes were sampling for adenosine triphosphate(ATP)biofluorescence and bacteriological testing;final rinsing water,hands of disinfection person-nels,disinfectants and environmental surfaces were collected for bacteriological testing.RESULTS Totally 68.18％of hospitals performed daily leaks test before cleaning,and 81.82％used o-Phthalaldehyde;81.25％of hospitals used peracetic acid in endoscopy sterilization,with only 1 hospital using glutaraldehyde.Totally 86.36％of hospi-tals monitored the concentration of disinfectants before daily works.There were statistically significant differ-ences in full-time,education levels,age and working experience of personnels among different levels of hospitals(P＜0.05).The pass rate of the 60 endoscope ATP specimens was 88.33％,and there were statistically signifi-cant differences in the pass rate of gastroscopic ATP testing among different levels of hospitals(P=0.032).The qualification status differed significantly in the types of endoscopes and the time length of enzyme washing(P＜0.05).All 60 endoscopic bacteriological samples were qualified,while 141 environmental bacteriological samples were with a pass rate of 94.33％.Unqualified samples were mainly for disinfection personnel's hands,rinse tank faucets,final rinse water.Rolestonella piercei and Staphylococcus wolffii were detected in one failed final rinse wa-ter by mass spectrometry.CONCLUSIONS The overall equipment and staffing of the surveyed endoscope centers(rooms)basically meet operational needs and generally follow endoscope cleaning and disinfection guidelines.However,there are still deficiencies in cleaning and disinfection management,equipment maintenance,disinfect-ant concentration monitoring and personnel training.Further efforts are needed to strengthen the disinfection quali-ty monitoring of endoscope and related factors.

[Objective] To establish a method for detecting the potency of recombinant human coagulation factor Ⅶa for injection. [Methods] By adding the sample and factor Ⅶ deficient plasma to the sample cup and activating the reaction with prothrombin time assay reagent (PT reagent), the coagulation time of the sample was determined by the change in magnetic bead swing amplitude in the sample cup. The logarithm of coagulation time was inversely proportional to the logarithm of human factor Ⅶa potency. [Results] Under the experimental conditions, the specificity of the methodology was evaluated through spiked recovery, and the recovery rates ranged from 90.0% to 110.0%. Within the range from 0.125 to 1.000 IU/mL, there was a good linear response between the potency and coagulation time of the standard and sample, with correlation coefficients r>0.99. As for the accuracy and repeatability, the recovery rates of various concentrations detected in the stock solution were 101.0%, 100.0% and 112.0%, respectively, with RSD values of 2.6%, 4.0% and 0.0%, respectively. The recovery rates of various concentrations in finished product testing were 104.0%, 94.7% and 112.0%, respectively, with RSD values of 1.9%, 2.4% and 0.0%, respectively. As for the intermediate precision, the RSD were 4.5% and 3.7%, respectively. After treated with sample diluent, the sample was tested at room temperature for 6 hours and still exhibited relatively stable biological activity. [Conclusion] This detection method is accurate, stable, easy to operate and highly automated, and is suitable for detecting the potency of recombinant human coagulation factor Ⅶa for Injection.

To analyze the disease burden of hypertension in the elderly population from 1990 to 2021 and to predict future trends in China and globally, thereby providing insights for public health decision-making regarding older adults with hypertension in China.

Objective To explore the application effect of the"ASK-T"aspiration prevention management model in reducing the risk of aspiration in elderly patients with chronic obstructive pulmonary disease(COPD)complicated by dysphagia.Methods 196 of 409 COPD patients with aspiration risk admitted to the Respiratory Disease Department of Guangdong Provincial Hospital of Chinese Medicine,Higher Education Mega Center Hospital from July 2021 to June 2024 were selected as the study object,and 102 COPD patients with aspiration risk admitted from July 2021 to December 2022 were set as the pre-improvement group.The patients in this group received the original nursing process and management mode of the department during hospitalization.94 COPD patients with aspiration risk admitted from January 2023 to June 2024 were set as the improved group,and the patients in this group were introduced into the department and implemented an innovative improvement project:"ASK-T"anti-aspiration management mode and then received nursing management.Patients in both groups were observed and followed up for 4 weeks.The risk grade of aspiration,SSA score,DHI score,aspiration pneumonia and aspiration pneumonia,nutritional status and satisfaction after intervention were compared between the two groups.Result in the improved group was significantly lower than that in the pre-improvement group(P<0.05),and the SSA score,DHI score and total DHI score of patients in the 2 groups were significantly lower than that before intervention(P<0.05).The SSA score,DHI score and DHI total score in the improved group were lower than those in the pre-improved group(P<0.05).The results of 4-week follow-up showed that the scores of NRS-2002 for nutritional risk screening in 2 groups were lower than before intervention(P<0.05),and the scores of NRS-2002 for nutritional risk screening in the improved group were significantly lower than those in the pre-improvement group(P<0.05).The comparison showed that the incidence of aspiration and aspiration pneumonia in the improved group was lower than that in the pre-improved group(P<0.05),and the satisfaction score of patients with the relevant preventive measures was higher than that in the pre-improved group(P<0.05).Conclusion The"ASK-T"aspiration preven-tion management model,when applied in the nursing of elderly patients with COPD complicated by dysphagia,can significantly reduce the risk of aspiration and the incidence of aspiration pneumonia,effectively improve patients' swallowing function and quality of life,and enhance their nutritional status.Furthermore,this model significantly improves patient satisfaction with intervention measures,providing a scientific and effective management strategy for clinical nursing of elderly patients with COPD complicated by dysphagia.It is worthy of promotion and application in clinical practice.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Aim To evaluate the ameliorative effects of different ratios of Renshen-Huangjing(RH) on SPS-induced PTSD-like behaviors in mice,and to investi-gate the action mechanism using bioinformatics analysis and experimental studies.Methods The aqueous ex-tract was extracted in four ratios of RH in a total weight of 60 g,i.e.1∶0(RH1),2∶1(RH2),1∶2(RH3),and 0∶1(RH4).The extraction rates of Rg1,Rb1,and polysaccharides from different ratios of RH were then detected using UPLC-UV method.The SPS model was established,and RH1,RH2,RH3 and RH4(400 mg·kg-1)were administered by intragas-tric gavage for 14 day,followed by behavioral tests to e-valuate the PTSD-like behaviors.The serum CORT,IL-1β and IL-10 were determined by ELISA.The possible targets of action were analyzed using bioinformatics.The expression levels of Calpain-1,PSD95,BDNF and GluN2B in the hippocampus were detected by Western blot.Results The SPS model induced PTSD-like be-haviors in mice.Serum levels of CORT and IL-1β in-creased and level of IL-10 decreased in SPS model.After treatment with different ratios of RHs,RH2 showed the best therapeutic effect,which was manifes-ted in the suppression of PTSD-like behaviors,the re-duction of CORT and IL-1β levels,and the promotion of IL-10 levels;160 overlapping targets might explain the therapeutic effects of RH on PTSD,and these tar-gets were enriched in inhibiting synaptic damage,exer-ting antioxidant properties and suppressing neuroin-flammation,respectively.RH2 prevented the SPS-in-duced decrease in the expression of Calpain-1,PSD95,BDNF and the elevation of GluN2B.Molecular docking showed strong binding of Rg1 and Rb1 to Calpain-1,PSD95,and BDNF,respectively.Conclusions The a-queous extract of RH in a 2∶1 ratio can more effec-tively prevent SPS-induced PTSD-like behaviors,and its effect may be related to targets such as Calpain-1,PSD95,BDNF and GluN2B.