OBJECTIVE: To assess the impact of albumin (Alb) administration on the prognosis of patients with acute kidney injury (AKI). METHODS: Clinical data of AKI patients in the intensive care unit (ICU) were retrospectively analyzed from the American Medical Information Mart of Intensive Care-IV (MIMIC-IV), including demographic data, acute physiology score (APS), comorbidities, vital signs, laboratory indicators, treatment status, ICU length of stay, and outcome indicators. The main outcome measure is ICU mortality. AKI patients were divided into Alb infusion group and Alb non infusion group based on whether they received Alb treatment. Multiple imputation was used to process missing data and eliminate variables that missing more than 30%. To ensure the stability of the results, propensity score matching (PSM) and inverse probability weighting (IPW) were used to correct the results. Using Kaplan-Meier survival curve and Cox proportional hazards regression model to evaluate the effect of Alb infusion on ICU survival rate in AKI patients. Perform subgroup analysis based on patient age, gender, and comorbidities to evaluate the prognostic effects of Alb on different patient subgroups. RESULTS: A total of 6 390 AKI patients were included, including 1 721 in the Alb infusion group and 4 669 in the Alb non infusion group. After adjusting for key covariates in the Cox regression model, compared with the Alb non infusion group, patients in the Alb infusion group were significantly younger in age, with APS III score, proportion of vasoactive drugs and continuous renal replacement therapy (CRRT) use, sepsis proportion, heart rate, respiratory frequency, aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine (Cr), lactic acid (Lac), and arterial partial pressure of carbon dioxide (PaCO₂) levels significantly higher. The proportion of hypertension, myocardial infarction, and congestive heart failure, as well as blood pressure, urine output, platelet count (PLT), and Alb levels were significantly lower. The results of univariate and multivariate Cox regression analysis on the raw data showed that the risk of death in the Alb infusion group was significantly lower than that in the Alb non infusion group [hazard ratio (HR) = 0.69, 95% confidence interval (95%CI) was 0.60-0.80, all P < 0.05]. The results after propensity score matching (PSM) and inverse probability weighting (IPW) processing are consistent with the original data trend (both P < 0.05). The Kaplan-Meier survival curve showed that the cumulative survival rate during ICU stay in the Alb infusion group was significantly higher than that in the Alb non infusion group (24.48% vs. 12.17%, Log-Rank test: χ² = 74.26, P < 0.05). Subgroup analysis shows that Alb infusion has a more significant survival benefit for AKI patients who use vasoactive drugs, have concurrent sepsis, and do not have liver disease. CONCLUSION Albumin infusion can decrease the ICU mortality of AKI patients.
Humans ; Retrospective Studies ; Acute Kidney Injury/mortality* ; Prognosis ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Albumins/therapeutic use* ; Proportional Hazards Models ; Adult ; Databases, Factual

Objective:To construct a sensitive quality index system for ophthalmic day surgery nursing.Methods:Based on the "structure-process-outcome" three-dimensional quality model, a preliminary screening of sensitive quality indicators for ophthalmic day surgery nursing was conducted through the literature review, survey of current situation, and group meeting. Purposive sampling was used to select 29 members of the expert pool of the Ophthalmology Nursing Committee of Chinese Nursing Association for two rounds of Delphi expert consultation from August to September 2023.Results:In the two rounds of expert consultation, 29 and 23 questionnaires were distributed respectively, and 23 and 21 valid questionnaires were recovered respectively, with effective recovery rates of 79.31% and 91.30% respectively. The expert authority coefficients were both 0.88. The Kendall's coefficient of concordance for the importance of indicators was 0.111 and 0.127, respectively (both P<0.01). The final formed sensitive quality index system for ophthalmic day surgery nursing included three primary indicators, nine secondary indicators, and 40 tertiary indicators. Conclusions:The sensitive quality index system for ophthalmic day surgery nursing constructed in this study is scientific, reliable, and practical, which can provide a reference for evaluating the quality of nursing in ophthalmic day surgery.

Objective:To construct a sensitive quality index system for ophthalmic day surgery nursing.Methods:Based on the "structure-process-outcome" three-dimensional quality model, a preliminary screening of sensitive quality indicators for ophthalmic day surgery nursing was conducted through the literature review, survey of current situation, and group meeting. Purposive sampling was used to select 29 members of the expert pool of the Ophthalmology Nursing Committee of Chinese Nursing Association for two rounds of Delphi expert consultation from August to September 2023.Results:In the two rounds of expert consultation, 29 and 23 questionnaires were distributed respectively, and 23 and 21 valid questionnaires were recovered respectively, with effective recovery rates of 79.31% and 91.30% respectively. The expert authority coefficients were both 0.88. The Kendall's coefficient of concordance for the importance of indicators was 0.111 and 0.127, respectively (both P<0.01). The final formed sensitive quality index system for ophthalmic day surgery nursing included three primary indicators, nine secondary indicators, and 40 tertiary indicators. Conclusions:The sensitive quality index system for ophthalmic day surgery nursing constructed in this study is scientific, reliable, and practical, which can provide a reference for evaluating the quality of nursing in ophthalmic day surgery.

Outer membrane vesicles (OMVs) are nanoscale vesicles secreted by Gram-negative bacteria. As a unique bacterial secretion, OMV secretion can help bacteria maintain the outer membrane stability or remove harmful substances. Studies have shown that local separation of outer membrane and peptidoglycan layers led by abnormalities in outer membrane protein function, abnormal structure or excessive accumulation of LPS, and erroneous accumulation of phospholipids in the outer leaflet, which can all lead to bacterial outer membrane protrusion and eventually bud formation of OMVs. Since OMVs are mainly composed of bacterial outer membrane and periplasmic components, the pathogen associated molecular patterns (PAMPs) on their surface can trigger strong immune responses. For example, OMVs can recruit and activate neutrophils, polarize macrophages to secrete large amounts of inflammatory factors. More importantly, OMVs can act as adjuvants to induce dendritic cell (DC) maturation to enhance adaptive immune response in the body. At the same time, OMVs are derived from bacteria, which make it easy to modify. The methods by genetic engineering and others can improve their tumor targeting, give them new functions, or reduce their immunotoxicity, which is conducive to their application in tumor therapy. OMVs not only induce apoptosis or pyroptosis of tumor cells, but also regulate the host immune system, which makes OMVs themselves have a certain killing effect on tumors. In addition, the tendency of neutrophils to inflammatory tumor sites and the formation of neutrophil extracellular traps enable OMVs to target tumor sites, and the suitable size and the characteristic that they are easily taken up by DCs give OMVs a certain lymphatic targeting ability. Therefore, OMVs are often employed as excellent drug or vaccine carriers in tumor therapy. This review mainly discusses the biological mechanism of OMVs, the regulatory effects of OMVs on immune cells, the functional modification strategies of OMVs, and their research progress in tumor therapy.

Objective To investigate the clinical characteristics and influencing factors of moderate-to-severe pulmonary ventilation dysfunction in occupational pneumoconiosis (hereinafter referred to as "pneumoconiosis") patients complicated with pulmonary tuberculosis. Methods A total of 136 male pneumoconiosis patients complicated with pulmonary tuberculosis suffering different degrees of pulmonary ventilation dysfunction were selected as the study subjects using the judgmental sampling method. Patients were divided into mild dysfunction and moderate-to-severe dysfunction groups based on the degrees of pulmonary ventilation dysfunction. Clinical data from patients of these two groups were collected, and influencing factors of pulmonary ventilation dysfunction were analyzed. Results The prevalence of mild dysfunction and moderate-to-severe dysfunction among the study subjects was 39.0% and 61.0%, respectively. The proportion of patients with moderate-to-severe pulmonary ventilation dysfunction increased with the progression of pneumoconiosis (P<0.05). Patients in moderate-to-severe dysfunction group had higher rates of dyspnea, elevated C-reactive protein, coexisting chronic obstructive pulmonary disease (COPD), and a history of lung infections within the past two years compared with those in the mild dysfunction group (all P<0.05). The result of multivariate logistic regression analysis showed that the degree of pneumoconiosis, complicated with COPD, and a history of lung infections within the past two years were risk factors for moderate-to-severe pulmonary ventilation dysfunction (all P<0.05). Specifically, higher degree of pneumoconiosis was associated with a greater proportion of moderate-to-severe dysfunction, and patients complicated with COPD or had a history of lung infections within the past two years were more likely to experience severe pulmonary ventilation dysfunction. Conclusion The degree of pneumoconiosis, complicated with COPD, and a history of lung infections within the past two years are influencing factors of moderate-to-severe pulmonary ventilation dysfunction in patients with pneumoconiosis combined with pulmonary tuberculosis. Early detection of pneumoconiosis progression, timely diagnosis of COPD and lung infections, and appropriate treatment such as antifibrotic agents, inhaled bronchodilators, and anti-infective therapies are recommended.

Objective To investigate the risk factors of 28-day mortality in septic patients and develop optimizing clinical prediction model based on machine learning algorithms.Methods Data from patients admitted to the department of intensive care unit(ICU)of the Affiliated Hospital of Nanjing University of Chinese Medicine from January 2019 to December 2023 were retrospectively analyzed.The data extracted included①gender,age,history of hypertension,diabetes,coronary heart disease,chronic obstructive pulmonary disease(COPD)and chronic kidney disease(CKD);②Vital signs and results of laboratory examination at admission were also collected,then acute physiology and chronic health evaluationⅡ(APACHEⅡ)score and sequential organ failure assessment(SOFA)score were calculated;③The other laboratory test results not included in APACHEⅡscore and SOFA score,such as blood lactate acid(Lac),alanine aminotransferase(AST),hemoglobin(Hb),procalcitonin(PCT),brian natriuretic peptide(BNP),C-reactive protein(CRP),activated partial thromboplastin time(APTT),D-dimer and troponin I(TNI)were also gathered.According to the 28-day survival,the patients were divided into a survival group and a death group.The difference of the clinical data and related loboratory indicators between the two groups of sepsis patients were compared.LASSO regression and Boruta algorithm were used to screen predictive variables.Models of Logistic regression(LG),neural network(NN)and light gradient boosting machine(LightGBM)were constructed.The data was divided into training set and verification set under a ratio of 7:3,and fivefold cross-validation was used to evaluate the stability of the models.Confusion matrix,receiver operator characteristic curve(ROC curve)and calibration curve were also used to assess the recognition ability and accuracy of three models.Decision curve analysis(DCA)was conducted to evaluate the models'utility in decision-making.Shapley additive explanations(SHAP)analysis was used to explain the best-performing model.Results A total of 426 patients were included in the study,of which 256 survived and 170 died.Compared with death group,the age(72.09±14.08 vs.76.88±11.32,P<0.05),COPD[11.33%(29/256)vs.20.00%(34/170)],CKD[20.31%(52/256)vs.31.77%(54/170)],Lac on admission[mmol/L:1.72(1.20,2.66)vs.2.25(1.60,3.50)],AST[U/L:32.00(18.00,59.75)vs.37.00(24.00,76.50)],CRP[mg/L:71.23(22.51,151.79)vs.87.00(37.00,173.36)],APACHEⅡscore(19.96±6.55 vs.22.83±6.92)and SOFA score[7(5,10)vs.9(5,12)]in surrial group were significantly decreased,the difference were statistically significant(all P<0.05).Age,APACHEⅡscore,Lac,PCT and CRP were revealed as independent predictors of 28-day mortality in sepsis by LASSO regression and Boruta algorithm,the above 5 variables were incorporated into the LG,NN and LightGBM models,and the five-fold cross-validation showed that the LightGBM model had the best stability.The confusion matrix,ROC curve and calibration curves of the 3 models were plotted,and the results showed that the F1 score of the 3 models were 0.61,0.63 and 0.74,respectively;area under the curve(AUC)was 0.68,0.74 and 0.87,respectively;the Log Loss was 0.62,0.41 and 0.34,respectively;and the Brier scores were 0.22,0.13 and 0.09,respectively,indicating that LightGBM model was optimal.DCA showed that LightGBM model had the greatest clinical net benefit.SHAP showed that the predicted results were in good agreement with the actual results.Conclusion The LightGBM model exhibited the best performance in predicting 28-day mortality in septic patients and has the potential to help clinicians identify high-risk patients and guide clinical decision-making.

Objective To investigate the risk factors of 28-day mortality in septic patients and develop optimizing clinical prediction model based on machine learning algorithms.Methods Data from patients admitted to the department of intensive care unit(ICU)of the Affiliated Hospital of Nanjing University of Chinese Medicine from January 2019 to December 2023 were retrospectively analyzed.The data extracted included①gender,age,history of hypertension,diabetes,coronary heart disease,chronic obstructive pulmonary disease(COPD)and chronic kidney disease(CKD);②Vital signs and results of laboratory examination at admission were also collected,then acute physiology and chronic health evaluationⅡ(APACHEⅡ)score and sequential organ failure assessment(SOFA)score were calculated;③The other laboratory test results not included in APACHEⅡscore and SOFA score,such as blood lactate acid(Lac),alanine aminotransferase(AST),hemoglobin(Hb),procalcitonin(PCT),brian natriuretic peptide(BNP),C-reactive protein(CRP),activated partial thromboplastin time(APTT),D-dimer and troponin I(TNI)were also gathered.According to the 28-day survival,the patients were divided into a survival group and a death group.The difference of the clinical data and related loboratory indicators between the two groups of sepsis patients were compared.LASSO regression and Boruta algorithm were used to screen predictive variables.Models of Logistic regression(LG),neural network(NN)and light gradient boosting machine(LightGBM)were constructed.The data was divided into training set and verification set under a ratio of 7:3,and fivefold cross-validation was used to evaluate the stability of the models.Confusion matrix,receiver operator characteristic curve(ROC curve)and calibration curve were also used to assess the recognition ability and accuracy of three models.Decision curve analysis(DCA)was conducted to evaluate the models'utility in decision-making.Shapley additive explanations(SHAP)analysis was used to explain the best-performing model.Results A total of 426 patients were included in the study,of which 256 survived and 170 died.Compared with death group,the age(72.09±14.08 vs.76.88±11.32,P<0.05),COPD[11.33%(29/256)vs.20.00%(34/170)],CKD[20.31%(52/256)vs.31.77%(54/170)],Lac on admission[mmol/L:1.72(1.20,2.66)vs.2.25(1.60,3.50)],AST[U/L:32.00(18.00,59.75)vs.37.00(24.00,76.50)],CRP[mg/L:71.23(22.51,151.79)vs.87.00(37.00,173.36)],APACHEⅡscore(19.96±6.55 vs.22.83±6.92)and SOFA score[7(5,10)vs.9(5,12)]in surrial group were significantly decreased,the difference were statistically significant(all P<0.05).Age,APACHEⅡscore,Lac,PCT and CRP were revealed as independent predictors of 28-day mortality in sepsis by LASSO regression and Boruta algorithm,the above 5 variables were incorporated into the LG,NN and LightGBM models,and the five-fold cross-validation showed that the LightGBM model had the best stability.The confusion matrix,ROC curve and calibration curves of the 3 models were plotted,and the results showed that the F1 score of the 3 models were 0.61,0.63 and 0.74,respectively;area under the curve(AUC)was 0.68,0.74 and 0.87,respectively;the Log Loss was 0.62,0.41 and 0.34,respectively;and the Brier scores were 0.22,0.13 and 0.09,respectively,indicating that LightGBM model was optimal.DCA showed that LightGBM model had the greatest clinical net benefit.SHAP showed that the predicted results were in good agreement with the actual results.Conclusion The LightGBM model exhibited the best performance in predicting 28-day mortality in septic patients and has the potential to help clinicians identify high-risk patients and guide clinical decision-making.

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

ObjectiveTo determine the value of MRI blooming sign in differentiating benign and malignant small breast masses and investigate its radiologic-pathologic correlation. MethodsThis retrospective study included 554 small breast masses （291 malignant and 263 benign） which were ≤ 2 cm and validated by pathology analysis between June 2016 and September 2020. All 554 patients underwent breast MRI. The clinical characteristics and MR features were analyzed. Univariate and multivariate regression analysis were performed to identify the independent risk factors of breast cancer. Two diagnostic models were constructed based on independent risk factors （model 1 included blooming sign and model 2 didn’t）. ROC curve was used to evaluate the diagnostic performances of the two models. The histological changes of peritumoral tissues in all small masses were analyzed. ResultsThe blooming sign was positive in 199 cases （68.4%） of the malignant masses and 25 cases （9.5%） of the benign ones （P＜0.05）. Univariate and multivariate regression analysis showed that age， lesion diameter， margin， ADC value， time signal intensity curve type and blooming sign were independent risk factors for breast cancer. Odds ratio were 1.065， 4.515， 2.811， 0.013， 3.487 and 13.894， respectively. Their corresponding 95%CI were （1.034， 1.097）， （2.368， 8.608）， （1.954， 4.045）， （0.004， 0.049）， （2.087， 5.826） and （7.026， 27.477）， respectively. The diagnostic performance of model 1 （blooming sign included） was better than that of model 2 （blooming sign not included； AUC： 0.938 vs 0.897， P < 0.05）. Histopathological analysis showed that the blooming sign was related to peritumoral lymphocyte infiltration and vascular proliferation. ConclusionsMRI blooming sign is helpful for distinguishing breast cancer from benign masses. The correlated histopathological basis may be peritumoral lymphocyte infiltration and neovascularization.