Fermented traditional Chinese medicine(TCM) has a long history of medicinal use, such as Sojae Semen Praeparatum, Arisaema Cum Bile, Pinelliae Rhizoma Fermentata, red yeast rice, and Jianqu. Fermentation technology was recorded in the earliest TCM work, Shen Nong's Classic of the Materia Medica. Microorganisms are essential components of the fermentation process. However, the contamination of fermented TCM by toxigenic fungi and mycotoxins due to unstandardized fermentation processes seriously affects the quality of TCM and poses a threat to the life and health of consumers. In this paper, the characteristics, microbial composition, and mycotoxin profile of fermented TCM are systematically summarized to provide a theoretical basis for its quality and safety control.
Fermentation ; Mycotoxins/analysis* ; Drugs, Chinese Herbal/analysis* ; Fungi/classification* ; Bacteria/genetics* ; Drug Contamination ; Medicine, Chinese Traditional

Objective:To establish a predictive risk model for acute kidney injury (AKI) in acute myocardial infarction (AMI) patients based on machine learning algorithm and compare with a traditional logistic regression model.Methods:It was a retrospective study. The demographic data, laboratory examination, treatment regimen and medication of AMI patients from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University were collected. The diagnostic criteria of AKI were based on the AKI diagnosis and treatment guidelines published by Kidney Diseases: Improving Global Outcomes in 2012. The selected AMI patients were randomly divided into training set (70%) and internal test set (30%) by simple random sampling. SelectFromModel and Lasso regression models were used to extract clinical parameters as predictors of AKI in AMI patients. Logistic regression model (model A) and machine learning algorithm (model B) were used to establish the risk prediction model of AKI in AMI patients. DeLong method was used to compare the area under the receiver-operating characteristic (ROC) curve ( AUC) between model A and model B for selecting the best model. Results:A total of 6 014 AMI patients were included in the study, with age of (58.4±11.7) years old and 3 414 males (80.5%). There were 674 patients (11.2%) with AKI. There were 4 252 patients (70.7%) in the training set and 1 762 patients (29.3%) in the test set. The selected twelve clinical parameters by the SelectFromModel and Lasso regression models included the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine, admission blood urea nitrogen, admission peak creatine kinase isoenzyme, diuretics, maximum daily dose of diuretics, days of diuretic use and statins. Logistic regression prediction model showed that AUC for the test set was 0.80 (95% CI 0.76-0.84). The machine learning algorithm model obtained AUC in the test set with 0.82 (95% CI 0.78-0.85).There was no significant difference in AUC between the two models ( Z=0.858, P=0.363), and AUC of the machine learning algorithm predictive model was slightly higher than that of the traditional logistic regression model. Conclusions:The prediction effect of AKI risk in AMI patients based on machine learning algorithm is similar to that of traditional logistic regression model, and the prediction accuracy of machine learning algorithm is better. The introduction of machine learning algorithm model may improve the ability to predict AKI risk.

Aim To investigate the effects of Cartham-us tinctorius L.extract(CTLE)on oxidative stress,lipid metabolism,and apoptosis levels of mice with al-cohol-induced liver injury and its mechanism of action.Methods The mouse model of alcohol-associated liver disease was established by chronic alcohol feeding and acute alcohol gavage.Mice were randomly divided into four groups.During the modeling period,the state changes of mice were observed every day,and their weight was recorded.At the end of modeling,blood and liver tissues were collected from each group of mice.The blood of mice was analyzed biochemically,and HE staining and Oil Red O staining were used to evaluate further the degree of pathological damage in the liver of mice.Quantitative real-time PCR(qPCR)and Western blot were applied to detect the mRNA and protein expression levels of p-PI3K,PI3K,p-Akt,Akt,p-mTOR,mTOR,p-FoxO1,FoxO1,p-FoxO3a,FoxO3a,p-FoxO4,FoxO4,BCL and BAX factors.Results Compared to the model group,the CTLE administration group showed improved hepatic patho-logical injury and reduced lipid deposition.The bio-chemical indexes in serum and liver,such as ALT,AST,TG,TC,and MDA levels were reduced,while GSH and SOD levels increased.Regulating the PI3K/Akt/FoxO pathway resulted in increased production of SOD,which reduced damage and apoptosis caused by reactive oxygen species(ROS).Conclusions CTLE can exert anti-oxidative stress and anti-apoptotic effects through the PI3K/Akt/FoxO pathway and attenuates alcoholic liver injury in mice,providing new ideas for the treatment of alcoholic liver disease and the develop-ment of related drugs.

Objective To establish a method for HPLC fingerprint analysis and determine three main components of Modified Zengye Decoction.Methods The chromatographic column was Shimadzu WondaSil C18 column(250 mm×4.6 mm,5 μm),the mobile phase was acetonitrile-0.3％aqueous phosphoric acid with a gradient elution procedure,the volume flow rate was 1.0 mL/min,the detection wavelengths were 265,203,310 and 290 nm,the column temperature was 25 ℃,and the injection volume was 20 μL.The HPLC fingerprints of the 10 batches of Modified Zengye Decoction were established,and the similarity analysis was performed by using the similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine(version 2012A).The common peaks were identified and assigned,and the contents of the three main components were quantitatively determined.Results There were 17 common peaks in the fingerprints of 10 batches of Modified Zengye Decoction with similarities ranging from 0.872-0.989.The fingerprints recognized peak 9,14 and 17 as ferulic acid,aurantiamarin and harpagoside,respectively.The contents of ferulic acid,aurantiamarin and harpagoside were 0.067 3-0.174 8,0.498 8-1.522 7,0.270 9-0.802 4 mg/g,and the transfer rate were 30.74％-55.63％,11.77％-35.94％,23.15％-68.56％,respectively.Conclusion The established HPLC fingerprint analysis method combined with main components quantitative analysis method can be used for the quality analysis and control of Modified Zengye Decoction with simple analysis method and reliable results.

Objective:To evaluate the detection performance of left ventricular ejection fraction (LVEF) in patients with chronic renal failure (CRF) by an artificial intelligence (AI) model based on yolox framework integrating left ventricular segmentation and critical point detection.Methods:From January 2019 to June 2023, a total of 4 284 echocardiographic images of 2 000 adults aged 18-80 years without segmental wall motion abnormalities, structural heart disease, cardiac surgery or cardiomyopathy were collected in Henan Provincial People′s Hospital to delineate the endocardial membrane, as a training set, an AI model based on yolox framework integrating left ventricular segmentation and critical point detection was established. The images were divided into the training set( n=1 675) and the test set( n=325) in a ratio of about 5∶1. All 228 echocardiographic images of 100 normal adult volunteers who were treated in Henan Provincial Chest Hospital from May 2020 to May 2021 were collected as external test set validation. All 792 echocardiographic images of 204 patients treated in Henan Provincial People′s Hospital from April 2019 to June 2023 were continuously enrolled to evaluate the measurement efficiency of AI model. Spearman correlation statistical method was used to analyze the consistency of AI model measurement with manual measurement and TomTec software measurement methods of 3 senior echocardiographic professionals. Subjects were divided into clear image group, unclear image group, normal LVEF group and reduced LVEF group, the differences of general data between the two groups were compared. The correlation coefficient(ICC) within the group was calculated to analyze the consistency, so as to evaluate the model performance. Results:LVEF measured by AI model was significantly correlated with both manual measurement and TomTec model measurement ( rs=0.834, 0.826; all P<0.01). ICC values of the clear image group and the unclear image group were 0.96 and 0.97, respectively. ICC values for all subjects, normal LVEF group and reduced LVEF group were 0.96, 0.90 and 0.96, respectively. Conclusions:The AI model based on yolox framework integrating left ventricular segmentation and critical point detection has good diagnostic performance in the automatic measurement of LVEF in patients with CRF.

In the face of the severe challenges posed by chronic illnesses, patients not only experience negative emotions due to their condition but also undergo positive transformations, such as a sense of benefit finding. This article summarizes the theoretical foundations, assessment tools, influencing factors, and intervention measures related to benefit finding among chronic disease patients. The aim is to provide references for healthcare professionals to develop and implement personalized psychological nursing care for chronic disease patients.

ObjectiveTo understand the situation of nosocomial infection in cancer hospitals and its changing trend， so as to provide a basis for adjusting the focus of nosocomial infection prevention and control in cancer hospitals. MethodsData of nosocomial infection quality control indices of Sun Yat-sen University Cancer Center from 2019 to 2021 were obtained through the nosocomial infection monitoring system， and the changes of these indices across the three years were analyzed by Chi-square test and Cochran-Armitage trend test. ResultsFrom 2019 to 2021， the incidence rates of nosocomial infection in this hospital were 0.80%， 0.78% and 0.57%， which decreased significantly year by year （P<0.001）. Among them， surgical site and respiratory system infection were more common， accounting for 35.75% and 31.08%， respectively. Gram-negative bacteria and fungi were the main pathogens. The incidence rate of multidrug-resistant bacteria in hospital increased year by year， from 0.08‰ to 0.14‰ （P<0.001）， among which methicillin-resistant staphylococcus aureus， carbapenem-resistant Enterobacter and bacteria producing ultra-broad spectrum β-lactamase （ESBLs） bacteria increased significantly. The incidence rates of three-tube associated infections were no different across 3 years （P>0.05）， which were still at high levels. ConclusionFrom 2019 to 2021， the prevention and control of nonsocomial infection in the cancer hospital has been improved overall. Meanwhile， the infections of respiratory system and surgical sites， ESBLs related multidrug-resistant bacteria and three-tube are weak links in cancer specialized hospitals， which need to be emphasized and improved.

Child ; Humans ; Malacoplakia/pathology* ; Intestines/pathology*

This paper reported 3 cases of poisoning caused by chlorfenagyr. Chlorfenapyr poisoning has gradually increased in clinical practice. The early stage after poisoning is digestive tract symptoms, followed by sweating, high fever, changes in consciousness, changes in myocardial enzymology, etc. Its main mechanism of intoxication is uncoupling oxidative phosphorylation. Since there is no specific antidote after poisoning, the fatality rate of chlorfenapyr poisoning remains high. The therapeutic measures are early gastrointestinal decontamination, symptomatic and supportive treatments, and early blood purification may be an effective treatment.
Humans ; Pyrethrins ; Gastrointestinal Tract ; Insecticides ; Poisoning/diagnosis*

Digital therapeutics(DTs)refers to a non-drug intervention method that uses electronic devices such as computers,smartphones,and wearable devices to evaluate and intervene through software programs and Internet technologies.It has been confirmed that there is a good therapeutic effect on a variety of mental disorders.Digital therapeutics can improve the insomnia problems of insomniacs,enhance the attention and work memory ability of patients with attention deficit hyperactivity disorder,and can also alleviate symptoms such as depression and anxiety disorder.Digital therapy will develop towards personalized treatment,popular treatment,fragmented treatment,and entertainment treatment in the future and have broad development prospects.