Cardiac arrest (CA) is a critical condition in the field of cardiovascular medicine. Despite successful resuscitation, patients continue to have a high mortality rate, largely due to post CA syndrome (PCAS). However, the injury and pathophysiological mechanisms underlying PCAS remain unclear. Experimental animal models are valuable tools for exploring the etiology, pathogenesis, and potential interventions for CA and PCAS. Current CA animal models include electrical induction of ventricular fibrillation (VF), myocardial infarction, high potassium, asphyxia, and hemorrhagic shock. Although these models do not fully replicate the complexity of clinical CA, the mechanistic insights they provide remain highly relevant, including post-CA brain injury (PCABI), post-CA myocardial dysfunction (PAMD), systemic ischaemia/reperfusion injury (IRI), and the persistent precipitating pathology. Summarizing the methods of establishing CA models, the challenges encountered in the modeling process, and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.
Animals ; Disease Models, Animal ; Post-Cardiac Arrest Syndrome/physiopathology* ; Heart Arrest/physiopathology* ; Humans ; Ventricular Fibrillation/complications*

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

Objective:To construct a nomogram model for predicting the occurrence of acute kidney injury (AKI) in patients with cardiac arrest (CA) after cardiopulmonary resuscitation (CPR), and to verify its validity for early prediction.Methods:The study retrospectively included patients aged 18 years and older who received CPR for CA and were admitted to the emergency room of Tianjin Medical University General Hospital from February 2016 to September 2023. The general information, underlying diseases, resuscitation related indicators, and first laboratory test results of patients were collected. The patients were randomly divided into training and validation groups at a ratio of 7:3. AKI diagnosis was based on the diagnostic criteria of the Kidney Disease Improving Global Outcomes. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with cardiac arrest, and a nomogram was constructed on the basis of the independent risk factors. The predictive performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic. The calibration curve, decision curve and clinical impact curve were used to evaluate the model. Bootstrap and cross validation methods were used for internal validation.Results:A total of 527 patients with cardiac arrest were included in the study, 230 patients developed AKI, with an AKI incidence of 43.6%. There was no statistically significant difference in clinical baseline data between the training and validation groups (all P>0.05), indicating comparability between the two groups of data. Multivariate logistic analysis revealed that age ( OR=1.346, 95% CI: 1.197-1.543, P<0.001), CA to CPR time ( OR=2.214, 95% CI: 1.512-3.409, P=0.016), adrenaline dosage ( OR=1.921, 95% CI: 1.383-2.783, P=0.004), APACHE-Ⅱ score ( OR=1.531, 95% CI: 1.316-1.820, P<0.001), baseline creatinine ( OR=1.137, 95% CI: 1.090-1.196, P<0.001), and lactate ( OR=2.558, 95% CI: 1.680-4.167, P<0.001) were the independent risk factors for AKI in patients with cardiac arrest. Initial defibrillable rhythm ( OR=0.214, 95% CI: 0.051-0.759, P=0.023) was a protective factor for AKI in patients with cardiac arrest. A nomogram prediction model was constructed based on the above variables. The AUC of the training group was 0.943 (95% CI: 0.921-0.965) and that of the validation group was 0.917 (95% CI: 0.874-0.960). This prediction model demonstrated good discrimination, calibration and clinical applicability. Conclusions:A nomogram predictive model was constructed on the basis of age, CA to CPR time, initial defibrillable rhythm, adrenaline dosage, the APACHE-Ⅱ score, and baseline creatinine and lactate levels. This nomogram has good predictive value for the early occurrence of AKI in patients with cardiac arrest after cardiopulmonary resuscitation, which can provide new strategies for the early identification of AKI and precise intervention.

Objective:To develop and validate an interactive network dynamic nomogram for early prediction of poor neurological prognosis in patients with post-cardiac arrest brain injury (PCABI).Methods:A retrospective study was conducted on hospitalized patients who achieved return of spontaneous circulation after cardiac arrest at Tianjin Medical University General Hospital between January 2020 and April 2024. Patients were classified into favorable and poor prognosis groups based on the Glasgow-Pittsburgh Cerebral Performance Category at discharge. Eligible patients were randomly assigned to a training cohort and an internal validation cohort in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of poor neurological outcomes in PCABI, which were subsequently used to develop a nomogram prediction model. The predictive performance of the nomogram was evaluated by comparing its area under the curve (AUC) of receiver operating characteristic with those of individual predictors using the DeLong test. Model calibration and clinical utility were assessed using calibration curves and decision curve analysis, respectively. Internal validation was conducted, and an interactive dynamic nomogram was developed using web-based visualization techniques.Results:A total of 276 PCABI patients were enrolled (training set: 196; validation set: 80), with 82 cases (29.7%) classified as poor prognosis. Multivariate logistic regression analysis identified age ( OR=1.071, 95% CI: 1.021-1.124, P=0.005), APACHEⅡ score ( OR=1.746, 95% CI: 1.393-2.190, P<0.001), initial shockable rhythm ( OR=0.142, 95% CI: 0.025-0.819, P=0.029), defibrillation ( OR=0.228, 95% CI: 0.060-0.869, P=0.030), cardiopulmonary resuscitation duration ( OR=2.116, 95% CI: 1.487-3.010, P<0.001), and lactate level ( OR=1.392, 95% CI: 1.005-1.927, P=0.047) as independent predictors of poor neurological outcomes in PCABI. A nomogram prediction model was developed based on these factors, achieving an AUC of 0.965 (95% CI: 0.939-0.989) in the training cohort and 0.987 (95% CI: 0.967-1.000) in the internal validation cohort. The nomogram demonstrated significantly superior predictive performance compared to individual predictors ( P<0.001) and exhibited excellent discrimination, calibration, and clinical net benefit. The interactive dynamic nomogram, developed through web-based visualization, further enhanced its applicability in clinical practice. Conclusions:The interactive network dynamic nomogram, developed based on age, APACHEⅡ score, initial shockable rhythm, defibrillation, cardiopulmonary resuscitation duration, and lactate level, demonstrated favorable predictive value for poor neurological outcomes in PCABI. This tool facilitates clinical application and offers a novel strategy for early identification and targeted interventions in high-risk patients.

BACKGROUND:Steroid-induced osteonecrosis of the femoral head is a refractory disease in the field of orthopedics.There is no definitive idea to fully explain its pathogenesis.With the increased research on the active ingredients of Panax notoginseng interfering with the signaling pathways related to various diseases,the active ingredients of Panax notoginseng that treat steroid-induced necrosis of the femoral head via the regulation of relevant signaling pathways have gradually become a hot research topic. OBJECTIVE:To systematically summarize the literature on the pathological mechanism of steroid-induced osteonecrosis of the femoral head and the regulation of signaling pathways by the active ingredients of Panax notoginseng in recent years,thereby providing a reference for the follow-up study on the active ingredients of Panax notoginseng in the treatment of this disease. METHODS:CNKI,WanFang,and PubMed were searched for relevant literature with the key words of"glucocorticoid,steroid-induced osteonecrosis of the femoral head,pathological mechanism,signaling pathway,Panax notoginseng,active ingredient"in Chinese and English.Documents related to the pathological mechanism of steroid-induced osteonecrosis of the femoral head as well as related to the intervention of active ingredients of Panax notoginseng on the signaling pathway of steroid-induced osteonecrosis of the femoral head were retrieved.A total of 63 documents were finally included according to the inclusion and exclusion criteria. RESULTS AND CONCLUSION:The main ingredients of Panax notoginseng include Panax notoginseng saponins,ginsenoside,Panax notoginseng saponins,quercetin,kaempferol,etc.Panax notoginseng saponins,ginsenoside Rb1 and quercetin can promote bone repair and angiogenesis by acting on the transforming growth factor-β/bone morphogenetic protein pathway.Panax notoginseng saponins,ginsenoside CK and kaempferol can promote osteogenic differentiation and lipid metabolism by acting on the Wnt/β-catenin pathway.Panax notoginseng saponins and Panax notoginseng saponins R1/R2 act on the MAPK pathway to inhibit osteoclastogenesis and promote bone repair.Panax notoginseng saponins,ginsenoside Rb2 and quercetin can inhibit osteoclast proliferation and promote osteoblastic differentiation by acting on the RANKL/RANK/OPG pathway.Panax notoginseng saponins,quercetin and kaempferol can repair vascular injury and promote osteogenesis by acting on the hypoxia-inducible factor-1α pathway.Panax notoginseng saponins R1,quercetin combined with hydroxyapatite nanoparticles,Panax notoginseng saponins combined with polyethylene-L-lactic acid and other biomaterials have good research prospects in the treatment of steroid-induced osteonecrosis of the femoral head.The active ingredients of Panax notoginseng can regulate the signaling pathways related to steroid-induced osteonecrosis of the femoral head through various mechanisms,and play an active intervention role in the disease.However,the depth and breadth of relevant research are insufficient at present,and the future research should be based on the existing mechanism to explore the specific mechanism of Panax notoginseng regulating different pathways and the interaction between pathways,which will be beneficial to the multi-development of the active ingredients of Panax notoginseng in the treatment of steroid-induced osteonecrosis of the femoral head.

Objective:To construct an early prediction model for post cardiopulmonary resuscitation-acute kidney injury (PCPR-AKI) by machine learning and provide a basis for early identification of acute kidney injury (AKI) high-risk patients and accurate treatment.Methods:It was a single-center retrospective study. The clinical data of patients admitted to Tianjin Medical University General Hospital after cardiopulmonary resuscitation following cardiac arrest from January 1, 2016 to October 31, 2023 were collected. The end-point event of the study was defined as AKI occurring within 48 hours after cardiopulmonary resuscitation. The patients were divided into AKI group and non-AKI group according to the AKI diagnostic criteria, and the differences of baseline clinical data between the two groups were compared. The patients who met the inclusion criteria were randomly (using the train_test_split function, set the random seeds to 1, 2, and 3) divided into training and validation sets at a ratio of 7∶3. Random forest (RF), support vector machine, decision tree, extreme gradient boosting and light gradient boosting machine algorithm were used to develop the early prediction model of PCPR-AKI. The receiver-operating characteristic curve and decision curve analysis were used to evaluate the performance and clinical practicality of the predictive models, and the importance of variables in the optimal model was screened and ranked.Results:A total of 547 patients were enrolled, with age of 66 (59, 70) years old and 282 males (51.6%). There were 238 patients (43.5%) having incidence of AKI within 48 hours after cardiopulmonary resuscitation. In the AKI group, 182 patients (76.5%) were in stage 1, 47 patients (19.7%) were in stage 2, and 9 patients (3.8%) were in stage 3. There were statistically significant differences in the age, time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, proportion of initial defibrillation rhythm, proportion of electric defibrillation, proportion of mechanical ventilation, adrenaline dosage, sodium bicarbonate dosage, proportion of coronary heart disease, proportion of hypertension, proportion of diabetes, serum creatinine, blood urea nitrogen, blood lactic acid, blood potassium, brain natriuretic peptide, troponin, D-dimer, neuron specific enolase, and 24 hours urine volume after cardiopulmonary resuscitation between AKI group and non-AKI group (all P<0.05). Among the five machine learning algorithms, RF model achieved the best performance and clinical practicality, with area under the curve of 0.875, sensitivity of 0.863, specificity of 0.956, and accuracy rate of 90.7%. In the variable importance ranking of RF model, the top 10 variables were as follows: time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, initial defibrillable rhythm, serum creatinine, mechanical ventilation, blood lactate acid, adrenaline dosage, brain natriuretic peptide, D-dimer and age. Conclusions:An early predictive model for PCPR-AKI is successfully constructed based on machine learning. RF model has the best predictive performance. According to the importance of the variables, it can provide clinical strategies for early identification and precise intervention for PCPR-AKI.

Adult ; Humans ; Heart Arrest/therapy* ; Cardiopulmonary Resuscitation ; Emergency Service, Hospital ; Out-of-Hospital Cardiac Arrest/therapy* ; Treatment Outcome

Objective:To understand the incidence of HIV infection, high risk behaviors and pre-exposure prophylaxis/post-exposure prophylaxis (PrEP/PEP) utilization in men who have sex with men (MSM) in Beijing.Methods:Sample size was estimated to be 1 227 persons and 207 person year respectively in the survey and the cohort by using Epi Info 7.0 software. Using convenient sampling method, MSM were recruited by using Wechat app. Questionnaire was completed online to collect the information about demographic characteristics, high risk behavior, and utilization of PrEP/PEP of the MSM. MSM collected dry blood spot (DBS) samples by themselves, and mailed the DBS samples to laboratory for HIV nucleic acid testing. Open cohort was established and those with HIV negative nucleic acid testing results were followed up. Non-conditional binary logistic regression method was used to identify the associated factors for high risk anal sex in the last month and having multiple homosexual partners in the last month.Results:A total of 1 147 MSM were recruited, and follow up for 236 person years was conducted in 956 MSM with negative HIV nucleic acid testing results. The detection rate of new HIV infection was 1.3 per 100 person-years (3/236). During the last month, the proportions of consistent condom use in anal sex and oral sex were 50.7% (238/469) and 4.9% (23/469). In the MSM, 5.9% (43/723) had sex with HIV positive partners in the last month. 9.8% (103/1 049) used PrEP, and 8.7% (91/1 049) used PEP. The proportion of consistent condom use in PrEP and PEP were 34.3% (24/70) and 72.2% (39/54) respectively. Logistic regression analysis revealed that compared with those who used no PrEP/PEP, those who used PrEP/PEP were more likely to have unprotected anal sex in the last month (a OR=3.16, 95% CI:1.45-7.18), and more likely to have multiple homosexual partners in the last month (a OR=2.64, 95% CI:1.19-6.30), and compared with those who used no Rush Popper or drugs in the last month, those who used Rush Popper or drugs in the last month were more likely to have unprotected anal sex in the last month (a OR=2.34, 95% CI:1.67-3.30), and more likely to have multiple homosexual partners (a OR=2.42,95% CI:1.76-3.33). Conclusions:It is necessary to strengthen the health education to promote condom use and introduce the harm of drug use in MSM. In PrEP and PEP services, it is still necessary to suggest consistent condom use for MSM.

Objective:To investigate the predictive value of neuron-specific enolase (NSE), serum 100 calcium-binding protein β (S100β), gray-white-matter-ratio on head CT (GWR) and the combination of the three on the prognosis of neurological function in patients with post-cardiac arrest brain injury (PCABI).Methods:A total of 136 patients admitted to Tianjin Medical University General Hospital after resuscitation from cardiac arrest from September 2021 to May 2023 were selected and included in the good prognosis group (96 patients) and the poor prognosis group (40 patients) based on the Glasgow-Pittsburgh Cerebral Performance (CPC) classification at discharge, respectively, to compare the demographic data, resuscitation data and NSE, S100β and GWR levels within 24 h of admission between the 2 groups, and modified Poisson regression was applied to investigate the factors affecting the neuroprognosis of PCABI patients. The effectiveness of NSE, S100β, GWR and the combination of the three in predicting neurological prognosis was evaluated using receiver operating characteristic (ROC) curve and the area under the curve (AUC), and the statistical differences in AUC were compared by Delong's test.Results:NSE, S100β, GWR, history of coronary artery disease, APACHEⅡ score, time from CA to CPR, duration of resuscitation, and dose of epinephrine use were independent factors influencing the neurological prognosis of PCABI patients ( P<0.05). Compared with the good prognosis group, NSE and S100β levels were significantly higher and GWR levels were significantly lower in the poor prognosis group, with statistically significant differences ( P<0.01). The AUCs for NSE, S100β and GWR to predict poor neurological prognosis were 0.905(0.851, 0.959), 0.876 (0.797, 0.956), 0.842(0.754, 0.930), with cut-off values of 26.75 ng/mL, 1.35 ng/mL and 1.195, respectively, and an AUC of 0.982 (0.961, 1.000) for the combination of the three predicting poor neurological prognosis, significantly higher than any single indicator ( P=0.001 4, 0.001 6, 0.002 8). Conclusions:Early monitoring of NSE, S100β and GWR is effective in predicting the neurological prognosis of PCABI patients at discharge, and the combination of all three significantly improves the predictive power.

Objective To understand the primary structure and potential antigenic epitopes of antigen Pf332(Ag332) of P.falciparum iso late FCC1/HN.Methods　Based on the published Pf332 gene sequence , nine pairs of primers were designed for the PCR amplification of the Pf332 gen e fragments from genomic DNA of P.falciparum isolate FCC1/HN. The amplified gene fragments were subcloned into pMD-18T vectors and sequenced. The sequences were aligned using DNAstar software to obtain the full-length sequence of the gene Pf332. The primary structure and sequence homology of Ag332 were analyzed by SAPS, Tmpred, SingalP and Blastn programs. Three fragments, R0, R1 and R2, cor responding to nt#9595-10083, nt#10339-10767 and nt#10855-11247 of Pf332 gene were subcloned into the eukaryotic expression vector pcDNA3-S separately. The Balb/c mice were immunized with pcDNA3-S-R0, pcDNA3-S-R1 and pcDNA3- S-R2 separately, and the expressions of the recombinant proteins were detected by immunohistochemistry assay. The protective immune responses elicited by DNA I mmunization were analyzed by ELISA and parasite growth inhibition tests in vitro .Results　Nine Pf332 gene fragments were specifically amplif ied, subcloned into pMD-18T vectors and sequenced. Pf332 gene of the P.falci parum isolate FCC1/HN was 16,377 bp in length, encoding a protein of 5,458 ami no acids, about 615.28kDa. The Ag332 contains 17 regions of highly degenerated Glu-rich repeats, with 30.18% Glu in total amino acids of Ag332. Ag332 of P.falciparum isolate FCC1/HN and 3D7 exhibited 94.55 % homology in amino acid residues. The results of immunohischemistry assay showed that R0, R1 and R2 were expressed in mice muscle tissue. The amount of IgG antibody of the groups immu nized with pcDNA3-S-R0, pcDNA3-S-R1 and pcDNA3-S-R2 were higher than those of blank and pcDNA3 groups (P＜0.05). The result of parasite growth inhibition test showed that the immunized sera at 1∶5 dilution of groups of pcDNA3-S-R0, pcDNA3-S-R1 and pcDNA3-S-R2 had an incomplete inhibitor y effect on P.falciparum growth. Conclusion　The antigen Pf332 is an large protein containing highly degenerated Glu-rich repeats. Pf332 gene fragments, R1 and R2 encoding potent antigenic epitope repeats.