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 compare quality control (relative purity and specific activity) and process control [plasminogen (Pg) antigen recovery and potency recovery] indexes of samples before and after adding the Q chromatography step to the full chromatography process of human Pg, thereby determining whether the addition of this step could improve Pg recovery by affinity chromatography. Methods: A Q chromatography step was added before the Pg affinity chromatography in the original Pg chromatography process. The loading solution, flow through solution and eluate of Q chromatography and Pg affinity chromatography were collected. The potency of coagulation factor Ⅱ (FⅡ), Ⅶ (FⅦ), Ⅷ (FⅧ), Ⅸ (FⅨ), and Ⅹ(FⅩ) were detected by the coagulation method, the total protein content was detected by the BCA method, and the Pg potency was detected by the chromogenic substrate method. The content of specific plasma proteins was detected by immunoturbidimetry, the potency recovery of coagulation factors was calculated, and the flow direction of coagulation factors was analyzed. The recovery of different plasma protein antigens were calculated, and the distribution of impurity proteins was analyzed. The relative purity and specific activity of Pg, antigen content, and potency recovery in the target fractions were calculated and compared with the original process indicators, so as to determine the effect of adding Q chromatography on the original process. Furthermore, the reproducibility after process modification was assessed. Results: 100% of FⅡ, FⅩ, and FⅨ, 87.81% of FⅧ, and 40.44% of FⅦ in filtered plasma were removed by Q chromatography. The residual FⅦ (53.26%) and FⅧ (13.30%) in Q flow-through fraction were completely removed by Pg affinity chromatography. In both the original process (without Q-chromatography) and the modified process (with Q-chromatography), non-target plasma proteins mainly existed in the flow-through fraction of Pg affinity chromatography. The antigen recovery of IgM, ceruloplasmin (CER), and fibronectin (FNC) in Q-chromatography flow-through fraction were reduced. In contrast, antigen recovery of other plasma proteins [IgG, IgA, Pg, albumin (AlB), alpha-1-antitrypsin (AAT), and fibrinogen (Fg)] were all >90%, which were consistent with the protein composition and proportion in the original affinity chromatography loading solution. Compared with the recovery rate of Pg antigen in the original process (74.4%), the total recovery of Pg antigen in the modified process was significantly increased (89.97%). Compared with the recovery of IgG (97.48%) and Fg (95.32%) in the Pg affinity flows-through fraction of the original process, the modified process resulted in a slight reduction in the recovery of IgG (94.60%), while the recovery of Fg was not affected (95.05%). The potency recovery rate, specific activity, and relative purity of Pg after Q chromatography were 99.3%, 0.016 U/mg, and 0.15%. These values were the same as those of Pg affinity chromatography loading solution by the original process, indicating that introduction of Q chromatography did not affect subsequent Pg affinity chromatography. Compared with the recovery of Pg antigen in three batches of the original process (66.49±1.02)%, the recovery of Pg antigen in the affinity chromatography eluent of the modified process [five batches; (77.43±4.43)%] was significantly improved. Furthermore, the potency recovery was (86.80±4.28)%, the relative purity was (81.99±1.25)%, the specific activity was (8.679±1.073)U/mg, and the process was reproducible. Conclusion: The addition of Q chromatography could improve the recovery of Pg affinity chromatography in the full chromatography process.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Objectives:Coronary slow flow(CSF)has long been regarded as a marker of coronary microvascular dysfunction(CMD).This study aims to evaluate the diagnostic value of CSF for CMD in patients with angina and nonobstructive coronary arteries(ANOCA).Methods:The study data were derived from the ANOCA-CMD prospective cohort study.All enrolled patients underwent coronary angiography and concurrent coronary physiological assessments in the left anterior descending artery using pressure-wire and thermodilution techniques to obtain coronary flow reserve(CFR)and the index of microcirculatory resistance(IMR).Based on the results,CMD was classified into four subtypes:CMD with elevated IMR(IMR≥25),CMD with reduced CFR(CFR<2.5),CMD with either reduced CFR or elevated IMR(CFR<2.5 or IMR≥25),and CMD with both reduced CFR and elevated IMR(CFR<2.5 and IMR≥25).The corrected thrombolysis in myocardial infarction(TIMI)frame count(CTFC)in the left anterior descending artery was calculated from coronary angiography images,with CSF defined as CTFC>27.This study evaluated the correlation between CTFC,CFR,and IMR,and investigated the diagnostic value of CSF for CMD in ANOCA patients.Results:A total of 103 ANOCA patients were enrolled in this study,with a mean age of(64.2±10.6)years,and 53.4％were female.Among them,57 patients(55.3％)were diagnosed with coronary slow flow.Patients with slow flow had higher IMR(P<0.001)and CFR(P=0.041).Similarly,the proportion of CMD with elevated IMR was higher in the slow flow group(P<0.001),while the proportion of CMD with reduced CFR was lower(P=0.044).There was no significant difference between the groups in the proportions of CMD with either reduced CFR or elevated IMR or CMD with both reduced CFR and elevated IMR(all P>0.05).CTFC was positively correlated with hyperemic mean transit time(r=0.424,P<0.001),IMR(r=0.430,P<0.001),and CFR(r=0.211,P=0.032).The area under the curve(AUC)of CTFC for diagnosing CMD with elevated IMR was 0.721(95％CI:0.623-0.819)with an accuracy of 67％(57％,76％),for diagnosing CMD with reduced CFR was 0.610(95％CI:0.499-0.720)with an accuracy of 60％(50％,70％),for diagnosing CMD with either reduced CFR or elevated IMR was 0.549(95％CI:0.425-0.673)with an accuracy of 47％(37％,57％),and for diagnosing CMD with both reduced CFR and elevated IMR was 0.582(95％CI:0.471-0.693)with an accuracy of 47％(37％,57％).Thus,CSF demonstrated limited diagnostic values across all subtypes of CMD.Conclusions:In ANOCA patients,CSF cannot serve as an effective diagnostic marker for CMD.Therefore,in clinical practice,the slow flow phenomenon should not be directly equated with the presence of coronary microvascular dysfunction in ANOCA patients.

Objectives:Coronary slow flow(CSF)has long been regarded as a marker of coronary microvascular dysfunction(CMD).This study aims to evaluate the diagnostic value of CSF for CMD in patients with angina and nonobstructive coronary arteries(ANOCA).Methods:The study data were derived from the ANOCA-CMD prospective cohort study.All enrolled patients underwent coronary angiography and concurrent coronary physiological assessments in the left anterior descending artery using pressure-wire and thermodilution techniques to obtain coronary flow reserve(CFR)and the index of microcirculatory resistance(IMR).Based on the results,CMD was classified into four subtypes:CMD with elevated IMR(IMR≥25),CMD with reduced CFR(CFR<2.5),CMD with either reduced CFR or elevated IMR(CFR<2.5 or IMR≥25),and CMD with both reduced CFR and elevated IMR(CFR<2.5 and IMR≥25).The corrected thrombolysis in myocardial infarction(TIMI)frame count(CTFC)in the left anterior descending artery was calculated from coronary angiography images,with CSF defined as CTFC>27.This study evaluated the correlation between CTFC,CFR,and IMR,and investigated the diagnostic value of CSF for CMD in ANOCA patients.Results:A total of 103 ANOCA patients were enrolled in this study,with a mean age of(64.2±10.6)years,and 53.4％were female.Among them,57 patients(55.3％)were diagnosed with coronary slow flow.Patients with slow flow had higher IMR(P<0.001)and CFR(P=0.041).Similarly,the proportion of CMD with elevated IMR was higher in the slow flow group(P<0.001),while the proportion of CMD with reduced CFR was lower(P=0.044).There was no significant difference between the groups in the proportions of CMD with either reduced CFR or elevated IMR or CMD with both reduced CFR and elevated IMR(all P>0.05).CTFC was positively correlated with hyperemic mean transit time(r=0.424,P<0.001),IMR(r=0.430,P<0.001),and CFR(r=0.211,P=0.032).The area under the curve(AUC)of CTFC for diagnosing CMD with elevated IMR was 0.721(95％CI:0.623-0.819)with an accuracy of 67％(57％,76％),for diagnosing CMD with reduced CFR was 0.610(95％CI:0.499-0.720)with an accuracy of 60％(50％,70％),for diagnosing CMD with either reduced CFR or elevated IMR was 0.549(95％CI:0.425-0.673)with an accuracy of 47％(37％,57％),and for diagnosing CMD with both reduced CFR and elevated IMR was 0.582(95％CI:0.471-0.693)with an accuracy of 47％(37％,57％).Thus,CSF demonstrated limited diagnostic values across all subtypes of CMD.Conclusions:In ANOCA patients,CSF cannot serve as an effective diagnostic marker for CMD.Therefore,in clinical practice,the slow flow phenomenon should not be directly equated with the presence of coronary microvascular dysfunction in ANOCA patients.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

[Objective] To determine the feasibility of preparing plasminogen (Pg) with Fraction Ⅲ precipitation (hereinafter referred to as FⅢ-P) from low-temperature ethanol process by full chromatography (hereinafter referred to as FⅢ-P process). [Methods] The FⅢ-P was diluted with dissolution buffer at different dilution times and stirring time. The potency and antigen concentration of Pg in dissolution sample were detected and the dissolution and clarification conditions were determined. Pre-treatment of loading sample and pre-experiment of affinity chromatography were carried out on the FⅢ-P dissolution sample to judge whether the loading sample had an impact on the chromatography by observing the performance of the affinity chromatography column and to evaluate whether the affinity chromatography could achieve the purpose of purifying Pg by detecting the plasma protein antigen concentration and Pg potency of the samples in the process. Two batches of FⅢ-P process were studied step by step, and the specific activity, steps and total recovery, and the output of Pg per ton of plasma were calculated. The feasibility of preparing Pg by FⅢ-P process was evaluated by comparing with the data of full chromatography process using plasma as raw material (hereinafter referred to as plasma process). [Results] The FⅢ-P was dissolved with 10 times of dissolution buffer, stirred for 1 hour, centrifuged at room temperature of 10 000×g for 15 minutes. The supernatant was first filtered with a screen, then clarified with an 8/0.8 μm filter, and finally filtered with a 0.45/0.2 μm filter and loaded. Pre-test showed that from clarification and filtration to Pg affinity chromatography, the step recovery of activity and antigen was 39.51% and 108.64%, respectively, the antigen concentration of Pg increased by 31.16 times and the activity increased by 11.39 times after affinity chromatography, which reaching the effect of affinity chromatography purification of Pg. The results of 2 batches of step-by-step scale-up FⅢ-P process showed that the total recoveries of antigen and activity from plasma to SP chromatography of FⅢ-P process were (45.76±1.10)% and (24.15±0.59)%, respectively, which had a total loss of about 1/3 of antigen and about 2/3 of activity compared to the plasma process. The Pg specific activity of SP chromatography eluent was (4.68±0.25) U/mg, which was about half of that of plasma process, but meeted the internal standard of > 4 U/mg. The output of Pg antigen per ton of plasma in the FⅢ-P process was 68.73% of that in the plasma process, and the output of Pg activity per ton of plasma in the plasma process was 29.82% of that in the plasma process, which basically achieved the purpose of waste utilization of FⅢ-P. [Conclusion] The technical route of preparing Pg from FⅢ-P by full chromatography is feasible.

Objective:To explore the application value of breast ultrasound structured report in the clinical teaching of standardized residency training of ultrasonography.Methods:Forty-eight residents from the Department of Ultrasonography in Renmin Hospital of Wuhan University were selected as the research objects, and were randomly divided into experimental group and control group in average. The experimental group used structured report template to write the breast ultrasound report, while the control group used free text. After 1 month of clinical teaching, the teachers who were not clear about the grouping assessed and analyzed the differences between the two groups of residents in grasping the characteristics of lesions and reporting content. At the same time, the students were asked to fill in a questionnaire for self-evaluation to analyze the learning situation of the two groups of students. SPSS 21.0 statistical analysis software was used for t-test and Chi-square test. Results:In terms of teacher evaluation, the scores of residents in the experimental group were higher than those in the control group in terms of completeness of report content, accuracy of description of peripheral conditions of lesions, standardization of professional terms and practicality of differential diagnosis (all P<0.001). There was no significant difference in the accuracy of lesion description between the two groups ( P=0.342). In terms of self-evaluation of residents in the standardized residency training, the evaluations of residents in the experimental group on the degree of mastery of knowledge points, clinical thinking cultivation and satisfaction of teaching mode were significantly higher than those of residents in the control group ( P=0.006, 0.012, <0.001, respectively). There were no significant differences between the two groups in learning interest and operation convenience ( P=0.186, 0.065, respectively). Conclusions:The structured report of breast ultrasound is helpful for the residents to master the characteristics of lesions and write a complete and professional report. It is also helpful for them to improve the standardization of operation and clinical thinking ability.

Objective To propose a deep learning neural network approach for transforming megavoltage computed tomography(MVCT)images of cervical cancer into pseudo kilovoltage computed tomography(kVCT)images with high signal-to-noise ratio and contrast-to-noise ratio,thus providing three-dimensional anatomical images and localization information required for adaptive radiotherapy of cervical cancer,and guiding the accelerator to achieve precise treatment.Methods The MVCT and kVCT images of 54 patients treated with cervical cancer radiotherapy were collected,with 44 cases randomly selected as the training set,and the remaining 10 cases as the test set.A cyclic generative adversarial network with gating mechanism and multi-channel data input was used to synthesize pseudo-kVCT images from MVCT images.The network training results were evaluated with imaging quality evaluation parameters,such as mean absolute error(MAE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM).Results The MAE,PSNR,and SSIM of MVCT imagesvspseudo-kVCT(5:5)images were(24.9±0.7)HUvs(17.8±0.3)HU,(29.8±0.2)dBvs(30.7±0.2)dB,and 0.841±0.007 vs 0.898±0.003,respectively.Conclusion The generated pseudo-kVCT images have advantages in noise reduction and contrast enhancement,and can reduce the need for additional MV-kVCT electron density calibration in dose calculations.The dose calculation ability of pseudo-kVCT is comparable to that of MVCT,providing a possibility for the application of pseudo-kVCT images in image-guided adaptive radiotherapy.