In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

The gut microbiota regulates intestinal nutrient absorption, participates in modulating host glucolipid metabolism, and contributes to ameliorating glucolipid metabolism disorder. Dysbiosis of the gut microbiota can compromise the integrity of the intestinal mucosal barrier, induce inflammatory responses, and exacerbate insulin resistance and abnormal lipid metabolism in the host. Dangua Humai Oral Liquid, a hospital-developed formulation for regulating glucolipid metabolism, has been granted a national invention patent and demonstrates significant clinical efficacy. This study aimed to investigate the effects of Dangua Humai Oral Liquid on gut microbiota and the intestinal mucosal barrier in a mouse model with glucolipid metabolism disorder. A glucolipid metabolism disorder model was established by feeding mice a high-glucose and high-fat diet. The mice were divided into a normal group, a model group, and a treatment group, with eight mice in each group. The treatment group received a daily gavage of Dangua Humai Oral Liquid(20 g·kg~(-1)), while the normal group and model group were given an equivalent volume of sterile water. After 15 weeks of intervention, glucolipid metabolism, intestinal mucosal barrier function, and inflammatory responses were evaluated. Metagenomics and untargeted metabolomics were employed to analyze changes in gut microbiota and associated metabolic pathways. Significant differences were observed between the indicators of the normal group and the model group. Compared with the model group, the treatment group exhibited marked improvements in glucolipid metabolism disorder, alleviated pathological damage in the liver and small intestine tissue, elevated expression of recombinant claudin 1(CLDN1), occluding(OCLN), and zonula occludens 1(ZO-1) in the small intestine tissue, and reduced serum levels of inflammatory factors lipopolysaccharides(LPS), lipopolysaccharide-binding protein(LBP), interleukin-6(IL-6), and tumor necrosis factor-α(TNF-α). At the phylum level, the relative abundance of Bacteroidota decreased, while that of Firmicutes increased. Lipid-related metabolic pathways were significantly altered. In conclusion, based on the successful establishment of the mouse model of glucolipid metabolism disorder, this study confirmed that Dangua Humai Oral Liquid effectively modulates gut microbiota and mucosal barrier function, reduces serum inflammatory factor levels, and regulates lipid-related metabolic pathways, thereby ameliorating glucolipid metabolism disorder.
Animals ; Gastrointestinal Microbiome/drug effects* ; Mice ; Intestinal Mucosa/microbiology* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice, Inbred C57BL ; Humans ; Glycolipids/metabolism* ; Lipid Metabolism/drug effects* ; Administration, Oral ; Disease Models, Animal

OBJECTIVE: To explore the therapeutic effects and underlying mechanisms of Dendrobium officinale (Tiepi Shihu) extract (DOE) on insomnia. METHODS: Forty-two male Sprague-Dawley rats were randomly divided into 6 groups (n=7 per group): normal control, model control, melatonin (MT, 40 mg/kg), and 3-dose DOE (0.25, 0.50, and 1.00 g/kg) groups. Rats were raised in a strong-light (10,000 LUX) and -noise (>80 db) environment (12 h/d) for 16 weeks to induce insomnia, and from week 10 to week 16, MT and DOE were correspondingly administered to rats. The behavior tests including sodium pentobarbital-induced sleep experiment, sucrose preference test, and autonomous activity test were used to evaluate changes in sleep and emotions of rats. The metabolic-related indicators such as blood pressure, blood viscosity, blood glucose, and uric acid in rats were measured. The pathological changes in the cornu ammonis 1 (CA1) region of rat brain were evaluated using hematoxylin and eosin staining and Nissl staining. Additionally, the sleep-related factors gamma-aminobutyric acid (GABA), glutamate (GA), 5-hydroxytryptamine (5-HT), and interleukin-6 (IL-6) were measured using enzyme linked immunosorbent assay. Finally, we screened potential sleep-improving receptors of DOE using polymerase chain reaction (PCR) array and validated the results with quantitative PCR and immunohistochemistry. RESULTS: DOE significantly improved rats' sleep and mood, increased the sodium pentobarbital-induced sleep time and sucrose preference index, and reduced autonomic activity times (P<0.05 or P<0.01). DOE also had a good effect on metabolic abnormalities, significantly reducing triglyceride, blood glucose, blood pressure, and blood viscosity indicators (P<0.05 or P<0.01). DOE significantly increased the GABA content in hippocampus and reduced the GA/GABA ratio and IL-6 level (P<0.05 or P<0.01). In addition, DOE improved the pathological changes such as the disorder of cell arrangement in the hippocampus and the decrease of Nissel bodies. Seven differential genes were screened by PCR array, and the GABA_A receptors (Gabra5, Gabra6, Gabrq) were selected for verification. The results showed that DOE could up-regulate their expressions (P<0.05 or P<0.01). CONCLUSION DOE demonstrated remarkable potential for improving insomnia, which may be through regulating GABA_A receptors expressions and GA/GABA ratio.
Animals ; Dendrobium/chemistry* ; Rats, Sprague-Dawley ; Male ; Sleep Initiation and Maintenance Disorders/blood* ; Plant Extracts/therapeutic use* ; Receptors, GABA-A/metabolism* ; Noise/adverse effects* ; Light/adverse effects* ; gamma-Aminobutyric Acid/metabolism* ; Sleep/drug effects* ; Rats ; Receptors, GABA/metabolism*

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

In China, the crude oil supply is highly dependent on overseas countries, and thus strengthening crude oil self-sufficiency has become an important issue of the national energy security. Tertiary oil recovery, especially polymer flooding, has been widely applied in large oil fields in China, which can increase the recovery rate by 15%-20% compared with water flooding. However, the widely used oil flooding polymers show poor thermal stability and salinity tolerance, complicated synthesis ways of monomers, and environmental unfriendliness. Moreover, the polymer flooding induces problems including pore plugging, heterogeneity intensification, high dispersion of remaining oil resources, pressure rise in injection wells, and low efficiency circulation of injection medium, which restrict the subsequent recovery of old oil fields. Here, we systematically review the developing and current situations of polymer flooding, introduce the innovative biomanufacturing of oil flooding polymers and their monomers or precursors as well as low-cost bio-based chemical raw materials for multiple compound flooding. The comprehensive study of the relationships between microbial fermentation metabolites and polymer flooding will reveal the green and low-carbon paths for polymer flooding. Such study will enable the application of enzymes produced by microorganisms in polymer production and polymer plugging removal after polymer flooding as well as the application of microbial metabolites such as biosurfactants, organic acids, alcohols, biogas, and amino acids in enhancing oil recovery. This review suggests that incorporating biomanufacturing into polymer flooding will ensure the high productivity and stability for crude oil production in China.
Polymers/metabolism* ; China ; Petroleum ; Oil and Gas Fields

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

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.

Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.