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

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

Background::Findings on the association of genetic factors and colorectal cancer (CRC) survival are limited and inconsistent, and revealing the mechanism underlying their prognostic roles is of great importance. This study aimed to explore the relationship between functional genetic variations and the prognosis of CRC and further reveal the possible mechanism.Methods::We first systematically performed expression quantitative trait locus (eQTL) analysis using The Cancer Genome Atlas (TCGA) dataset. Then, the Kaplan-Meier analysis was used to filter out the survival-related eQTL target genes of CRC patients in two public datasets (TCGA and GSE39582 dataset from the Gene Expression Omnibus database). The seven most potentially functional eQTL single nucleotide polymorphisms (SNPs) associated with six survival-related eQTL target genes were genotyped in 907 Chinese CRC patients with clinical prognosis data. The regulatory mechanism of the survival-related SNP was further confirmed by functional experiments.Results::The rs71630754 regulating the expression of endoplasmic reticulum aminopeptidase 1 ( ERAP1) was significantly associated with the prognosis of CRC (additive model, hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.08-1.88, P = 0.012). The results of dual-luciferase reporter assay and electrophoretic mobility shift assay showed that the A allele of the rs71630754 could increase the binding of transcription factor 3 (TCF3) and subsequently reduce the expression of ERAP1. The results of bioinformatic analysis showed that lower expression of ERAP1 could affect the tumor immune microenvironment and was significantly associated with severe survival outcomes. Conclusion::The rs71630754 could influence the prognosis of CRC patients by regulating the expression of the immune-related gene ERAP1. Trial Registration::No. NCT00454519 (https://clinicaltrials.gov/)

Objective:To study the serological characteristics of ABO*A2.08 subtype and explore its genetic molecular mechanism.Methods:ABO blood group identification was performed on proband and her family members by routine serological methods.ABO genotyping and sequence analysis were performed by polymerase chain reaction-sequence specific primer(PCR-SSP),and direct sequencing of PCR products from exons 6 and 7 of ABO gene were directly sequenced and analyzed.The effect of gene mutation in A2.08 subtype on structural stability of GTA protein was investigated by homologous protein conserved analysis,3D molecular modeling and protein stability prediction.Results:The proband's serological test results showed subtype Ax,and ABO genotyping confirmed that the proband's genotype was ABO*A207/08.Gene sequencing of the proband's father confirmed the characteristic variation of c.539G＞C in the 7th exon of ABO gene,leading to the replacement of polypeptide chain p.Arg180Pro(R180P).3D protein molecular modeling and analysis suggested that the number of hydrogen bonds of local amino acids in the protein structure was changed after the mutation,and protein stability prediction showed that the mutation had a great influence on the protein structure stability.Conclusion:The mutation of the 7th exon c.539G＞C of ABO gene leads to the substitution of polypeptide chain amino acid,which affects the structural stability of GTA protein and leads to the change of enzyme activity,resulting in the A2.08 phenotype.The mutated gene can be stably inherited.

Traumatic supraorbital fissure syndrome (TSOFS) is a symptom complex caused by nerve entrapment in the supraorbital fissure after skull base trauma. If the compressed cranial nerve in the supraorbital fissure is not decompressed surgically, ptosis, diplopia and eye movement disorder may exist for a long time and seriously affect the patients′ quality of life. Since its overall incidence is not high, it is not familiarized with the majority of neurosurgeons and some TSOFS may be complicated with skull base vascular injury. If the supraorbital fissure surgery is performed without treatment of vascular injury, it may cause massive hemorrhage, and disability and even life-threatening in severe cases. At present, there is no consensus or guideline on the diagnosis and treatment of TSOFS that can be referred to both domestically and internationally. To improve the understanding of TSOFS among clinical physicians and establish standardized diagnosis and treatment plans, the Skull Base Trauma Group of the Neurorepair Professional Committee of the Chinese Medical Doctor Association, Neurotrauma Group of the Neurosurgery Branch of the Chinese Medical Association, Neurotrauma Group of the Traumatology Branch of the Chinese Medical Association, and Editorial Committee of Chinese Journal of Trauma organized relevant experts to formulate Chinese expert consensus on the diagnosis and treatment of traumatic supraorbital fissure syndrome ( version 2024) based on evidence of evidence-based medicine and clinical experience of diagnosis and treatment. This consensus puts forward 12 recommendations on the diagnosis, classification, treatment, efficacy evaluation and follow-up of TSOFS, aiming to provide references for neurosurgeons from hospitals of all levels to standardize the diagnosis and treatment of TSOFS.

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.

Objective This study aims to assess the current status of pharmaceutical management in county medical communities in Hubei province,and provide recommendations for the homogenization,standardization,and regulation of pharmaceutical management in these communities.It also intends to offer decision-making support for health administrative departments,and provide reference experiences for management in other regions.Methods The current status of pharmaceutical management in county medical communities in Hubei province was conducted through a questionnaire survey and field research.Existing problems were analyzed,key management areas were identified,and reasonable recommendations were proposed.Results Pharmaceutical management in county medical communities has significant shortcomings in organizational structure,system construction,personnel allocation,key link control,and the leading unit's outreach capabilities.These deficiencies are not aligned with the high-quality development of pharmacy in the new era.Conclusions It is recommended that county medical communities should establish a comprehensive pharmaceutical management quality control system.This can be achieved by improving organizational management,strengthening talent development,enhancing core systems,setting monitoring indicators,and increasing outreach capabilities.Additionally,evaluation standards for the quality control system of pharmaceutical management should be established to enhance management capabilities through scientific assessment and positive feedback.

Purpose: This study aims to investigate the current status of affiliate stigma among parents of autistic children, analyze the influencing factors, explore the relationship among mindfulness, coping styles, and affiliate stigma, and verify the mediating role of coping styles between mindfulness and affiliate stigma in parents of children with autism in China.MethodBetween February and April 2023, the Child Development Behaviour Centre of a public hospital in China recruited 345 parents of children with autism. These parents completed the general information questionnaire, the Mindful Attention Awareness Scale, the Affiliate Stigma Scale, and the Simple Coping Style Questionnaire. We then adapted the Hayes Process Macro and Bootstrap methods to examine the mediating effects of coping styles between mindfulness and affiliate stigma. Results: (1) The total affiliate stigma score of parents of children with autism was 48.53 (standard deviation:: 10.74). Parents' age, monthly family income, duration of care, mindfulness, and coping styles were the influencing factors of parental affiliate stigma. (2) Mindfulness was positively correlated with positive coping style (r = 0.33, p < .01) and negatively correlated with negative coping style, affiliate stigma (r = −0.38, −0.39, p < .01), whereas affiliate stigma was negatively correlated with positive coping style (r = −0.34, p < .01) and positively correlated with negative coping style (r = 0.41, p < .01). (3) Positive coping style and negative coping style play a parallel mediating role between mindfulness and affiliate stigma of parents of autistic children. Conclusions Parents of children with autism experience significant levels of affiliate stigma. Mindfulness has a direct impact on associated stigma in parents of children with autism and also indirectly predicts associated stigma through the intermediary influence of positive and negative coping styles. Healthcare professionals could perform mindfulness interventions from an optimistic psychology viewpoint to boost parents' mindfulness and coping abilities, thereby accomplishing the objective of mitigating affiliate stigma.

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.